From d4ffc362e140efce0b04ddb68e95778178d78e2e Mon Sep 17 00:00:00 2001 From: Hendrik Wouters Date: Wed, 8 Nov 2017 16:57:31 +0100 Subject: [PATCH 001/129] adding routines for reading ground and air profile data --- data_air.py | 393 +++++++++++++++++++++++++++++++++++++++++++++++ data_ground.py | 393 +++++++++++++++++++++++++++++++++++++++++++++++ data_sounding.py | 393 +++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 1179 insertions(+) create mode 100644 data_air.py create mode 100644 data_ground.py create mode 100644 data_sounding.py diff --git a/data_air.py b/data_air.py new file mode 100644 index 0000000..011afe6 --- /dev/null +++ b/data_air.py @@ -0,0 +1,393 @@ +import numpy as np + +from bs4 import BeautifulSoup +import pandas as pd +import datetime as dt +import pylab as pl +import io +import os +import calendar +import Pysolar +import Pysolar.util +#from urllib import request +def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25): + + #initialize error BLH + BLHe = 0. + eps = 2.#security limit + iTHTV_0 = np.where(~np.isnan(THTV))[0] + if len(iTHTV_0) > 0: + iTHTV_0 = iTHTV_0[0] + THTV_0 = THTV[iTHTV_0] + else: + THTV_0 = np.nan + RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2 + + + #RiB = 9.81/THTV_0 * ( THTV[i-1] + (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2 + #RiB - RiBc = 0 + + #best guess of BLH + + #print("RiB: ",RiB) + #print("RiBc: ",RiBc) + + + + BLHi = np.where(RiB > RiBc)[0] + if len(BLHi ) > 0: + BLHi = BLHi[0] + #print("BLHi: ",BLHi) + BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1] + + # possible error is calculated as the difference height levels used for the interpolation + BLHu = np.max([BLH,HAGL[BLHi]-eps]) + BLHd = np.min([BLH,HAGL[BLHi-1]+eps]) + # calculate an alternative BLH based on another critical Richardson number (RiBce): + BLHi =np.where(RiB > RiBce)[0] + if len(BLHi ) > 0: + BLHi = BLHi[0] + + BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1] + BLHu = np.max([BLHu,HAGL[BLHi]-eps]) + BLHd = np.min([BLHd,HAGL[BLHi-1]+eps]) + + BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)]) + BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)]) + + else: + BLH,BLHu,BLHd = np.nan, np.nan,np.nan + + else: + BLH,BLHu,BLHd = np.nan, np.nan,np.nan + + return BLH,BLHu,BLHd + +def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)): + STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds()) + return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)] + + +#from os import listdir +#from os.path import isfile #,join +import glob + + +class wyoming(object): + def __init__(self): + self.status = 'init' + self.found = False + self.DT = None + self.current = None + self.mode = 'b' + self.profile_type = 'wyoming' + self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'] + self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/" + + def set_STNM(self,STNM): + self.__init__() + self.STNM = STNM + self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html") + self.FILES = [os.path.realpath(FILE) for FILE in self.FILES] + self.current = None + self.found = False + self.FILES.sort() + + def find_first(self,year=None,get_atm=False): + self.found = False + + # check first file/year or specified year + if year == None: + self.iFN = 0 + self.FN = self.FILES[self.iFN] + else: + self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html") + self.iFN = self.FILES.index(self.FN) + self.sounding_series = BeautifulSoup(open(self.FN), "html.parser") + self.current = self.sounding_series.find('h2') + keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)" + + # go through other files and find first sounding when year is not specified + self.iFN=self.iFN+1 + while keepsearching: + self.FN = self.FILES[self.iFN] + self.sounding_series = BeautifulSoup(open(self.FN), "html.parser") + self.current = self.sounding_series.find('h2') + self.iFN=self.iFN+1 + keepsearching = (self.current is None) and (self.iFN < len(self.FILES)) + self.found = (self.current is not None) + + self.status = 'fetch' + if self.found: + self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13])) + + if self.found and get_atm: + self.get_values_one_column_atm() + + + def find(self,DT,get_atm=False): + + self.found = False + keepsearching = True + print(DT) + # we open a new file only when it's needed. Otherwise we just scroll to the right sounding. + if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)): + self.DT = DT + self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html") + self.iFN = self.FILES.index(self.FN) + self.sounding_series = BeautifulSoup(open(self.FN), "html.parser") + self.current = self.sounding_series.find('h2') + + keepsearching = (self.current is not None) + while keepsearching: + DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13])) + if DTcurrent == DT: + self.found = True + keepsearching = False + if get_atm: + self.get_values_one_column_atm() + self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13])) + elif DTcurrent > DT: + keepsearching = False + self.current = None + else: + self.current = self.current.find_next('h2') + if self.current is None: + keepsearching = False + self.found = (self.current is not None) + self.status = 'fetch' + + def find_next(self,get_atm=False): + self.found = False + self.DT = None + if self.current is None: + self.find_first() + else: + self.current = self.current.find_next('h2') + self.found = (self.current is not None) + keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES))) + while keepsearching: + self.iFN=self.iFN+1 + self.FN = self.FILES[self.iFN] + self.sounding_series = BeautifulSoup(open(self.FN), "html.parser") + self.current = self.sounding_series.find('h2') + + self.found = (self.current is not None) + keepsearching = ((self.current is None) and (self.iFN < len(self.FILES))) + if self.found: + self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13])) + if self.found and get_atm: + self.get_values_one_column_atm() + + + + def get_values_one_column_atm(self): + + # for iDT,DT in enumerate(DTS): + + #websource = urllib.request.urlopen(webpage) + #soup = BeautifulSoup(open(webpage), "html.parser") + + + #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTV','THTA','QABS']):
+                if len(np.where(HAGL <= BLH)[0]) >= 4:
+                    meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL <= BLH][2:-1],dtype=np.float))
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][1:2],dtype=np.float)
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV']:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta = np.max((0.1,(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) )
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+        PARAMS.insert(0,'day', PARAMS['datetime'][0].day)
+        PARAMS.insert(0,'tstart', PARAMS['datetime'][0].hour)
+        PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+        PARAMS.insert(0,'lon', np.float(PARAMS['Station longitude'][0]))
+        PARAMS['ldatetime'] = PARAMS.datetime.value - dt.timedelta(hours=PARAMS.lon.value/360.*24.) 
+        PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+        PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+        PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+        PARAMS.insert(0,'OK',((BLHVe < 100.) and (len(np.where(~pd.isnull(ONE_COLUMN['THTV'][ONE_COLUMN['HAGL'] > 5000.]))[0]) >0 )))
+        
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
+            
+        elif self.mode == 'b': # best BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
+            
+        elif self.mode == 'u':# upper BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNu['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNu['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNu['THTA'])[2]-list(ONE_COLUMNu['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNu['QABS'])[2]-list(ONE_COLUMNu['QABS'])[1]))
+            
+        elif self.mode == 'd': # lower BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNd['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNd['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNd['THTA'])[2]-list(ONE_COLUMNd['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNd['QABS'])[2]-list(ONE_COLUMNd['QABS'])[1]))
+            
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        if self.mode == 'o': #original 
+            self.ONE_COLUMN = ONE_COLUMN
+        elif self.mode == 'b': # best BLH
+            self.ONE_COLUMN = ONE_COLUMNb
+        elif self.mode == 'u':# upper BLH
+            self.ONE_COLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # lower BLH
+            self.ONE_COLUMN=ONE_COLUMNd
+        else:
+            self.ONE_COLUMN = ONE_COLUMN
+
+class one_column(object):
+    def __init__(self):
+        self.status = 'init'
+        
+        
+    def set_one_column_atm(self,INPUT):
+        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
+
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = ONE_COLUMN
+        self.__dict__.update(PARAMS.to_dict()['value'])
+        self.__dict__.update(ONE_COLUMN.to_dict('list'))
+        self.status = 'filled'
+        
+        #convert all list to arrays for CLASS
+        for key in self.__dict__.keys():
+            if type(self.__dict__[key]).__name__ == 'list':
+                self.__dict__[key] = np.array(self.__dict__[key])
+
+
diff --git a/data_ground.py b/data_ground.py
new file mode 100644
index 0000000..d4e0b5a
--- /dev/null
+++ b/data_ground.py
@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: vsc42247
+
+Purpose: Set surface conditions for the CLASS boundary-layer model
+"""
+
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+import pynacolada as pcd
+import pandas as pd
+
+def get_class4gl_ground(class_settings,**kwargs):   
+    
+    key = "IGBPDIS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+    
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        print('reading soil water saturation from '+input_fn)
+
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wsat'] = input_nc.variables['wsat'][ilon,ilat]
+        input_nc.close()
+
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc"
+        print('reading soil water field capacity from '+input_fn)
+    
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wfc'] = input_nc.variables['wfc'][ilon,ilat]
+        input_nc.close()
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc"
+        print('reading soil wilting point from '+input_fn)
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wwilt'] = input_nc.variables['wwp'][ilon,ilat]
+        input_nc.close()
+        
+    key = "GLEAM"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+        
+        #INPUT_gleam = gleam() 
+        #INPUT_gleam.path = "/kyukon/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/" 
+        
+        gleam_path = "/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/"
+        print('reading soil-water content for "+str(class_settings,datetime.year)+" from '+gleam_path)
+        
+        gleam_files = {}
+        
+        gleam_vars = ['SMroot','SMsurf']
+        
+        for VAR in gleam_vars:
+            gleam_files[VAR] = nc4.Dataset(gleam_path+'/'+str(class_settings.datetime.year)+'/'+VAR+'_'+str(class_settings.datetime.year)+'_GLEAM_v3.1a.nc','r')
+        
+
+        year = class_settings.datetime.year
+        day = class_settings.datetime.day
+        hour = class_settings.datetime.hour
+  
+        ilat = np.where(gleam_files['SMsurf'].variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(gleam_files['SMsurf'].variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        VAR = 'SMsurf'; class_settings.wg = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
+        VAR = 'SMroot'; class_settings.w2 = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
+        
+        for VAR in gleam_vars:
+            gleam_files[VAR].close()
+    
+    key = "MOD44B"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+    
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc"
+        print('initializing vegetation fraction from '+input_fn)
+        var = 'cveg'
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__[var] = input_nc.variables['fv'][ilon,ilat]
+        input_nc.close()
+        
+    key = "DSMW"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+         # Procedure of the thermal properties:
+         # 1. determine soil texture from DSMW
+         # 2. soil type with look-up table (according to DWD/EXTPAR)
+         # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987) 
+         #    with parameter look-up table from Noilhan and Planton (1989). 
+         #    Note: The look-up table is inspired on DWD/COSMO
+                 
+       
+        #preparing for soil thermal properties
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc"
+        
+        print("deriving soil thermal properties for the force-restore methodes from the soil texture file "+ input_fn)
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        DSMW = input_nc.variables['DSMW'][ilat,ilon]
+        
+        
+        #EXTPAR: zfine   = soil_texslo(soil_unit)%tex_fine
+        SP = {}; SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code']
+        for SPKEY in SPKEYS: 
+            SP[SPKEY] = np.array(input_nc.variables[SPKEY][DSMW])
+        input_nc.close()
+        
+        SP['texture'] = (0.5*SP['tex_medium']+1.0*SP['tex_coarse']) /(SP['tex_coarse']+SP['tex_medium']+SP['tex_fine'])
+        
+        if pd.isnull(SP['texture']):
+            print('Warning, texture is invalid> Setting to Ocean')
+            SP['itex'] = 9
+        
+        else:
+            SP['itex'] = int(SP['texture']*100)
+        
+        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+        SP['isoil'] = np.zeros_like(SP['itex'],dtype=np.int)
+        LOOKUP = [
+                  [0 ,7],# fine textured, clay (soil type 7)
+                  [20,6],# medium to fine textured, loamy clay (soil type 6)
+                  [40,5],# medium textured, loam (soil type 5)
+                  [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                  [80,3],# coarse textured, sand (soil type 3)
+                ]
+        for iitex,iisoil in LOOKUP: 
+            SP['isoil'][SP['itex'] >= iitex ] = iisoil 
+        
+        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+        LOOKUP = [
+                  [9001, 1 ], # ice, glacier (soil type 1) 
+                  [9002, 2 ], # rock, lithosols (soil type 2)
+                  [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                  [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                  [9,    9 ], # undefined (ocean)
+                  [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                  [9000, 9 ], # undefined (inland lake)
+                  [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                  [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                ]
+        # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+        for icode,iisoil in LOOKUP: 
+            SP['isoil'][SP['code'] == icode] = iisoil 
+        
+        #adopted from data_soil.f90 (COSMO5.0)
+        SP_LOOKUP = { 
+          # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea  
+          # (by index)                                           loam                    loam                                water      ice
+          'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+          'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+          'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+          'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+          'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+          'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+          'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+          'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+          'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+          'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+          'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+          'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+          'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+          'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+          'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+          'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+          'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+          'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+          'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+          #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+          'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , np.nan    , np.nan   ,  np.nan  ],
+          #error in table 2 of NP89: values need to be multiplied by e-6
+          'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+          'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , np.nan    , np.nan   ,  np.nan  ],
+          'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , np.nan    , np.nan   ,  np.nan  ],
+          'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , np.nan    , np.nan   ,  np.nan  ],
+          'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , np.nan    , np.nan   ,  np.nan  ],
+        }
+        
+        for SPKEY in SP_LOOKUP.keys(): 
+            SP[SPKEY] = np.zeros_like(SP['isoil'],dtype=np.float)
+        
+        for i in range(11):
+            SELECT = (SP['isoil'] == i)
+            for SPKEY in SP_LOOKUP.keys(): 
+                SP[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+        
+        for SPKEY in list(SP_LOOKUP.keys())[-6:]: 
+            var = SPKEY
+            class_settings.__dict__[var] = np.float(SP[SPKEY])
+            
+        # only print the last parameter value in the plot
+        
+        #inputs.append(cp.deepcopy(class_settings))
+        #var = 'cala'
+        #class_settings.__dict__[var] = np.float(SP['cala0'])
+        #valnew = class_settings.__dict__[var]
+        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+        
+        #inputs.append(cp.deepcopy(class_settings))
+        #var = 'crhoc'
+        #class_settings.__dict__[var] = np.float(SP['crhoc'])
+        #valnew = class_settings.__dict__[var]
+        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+        
+    key = "CERES"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
+        
+        CERES_start_date = dt.datetime(2000,3,1)
+        DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+        DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+        print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+            
+        var = 'cc'
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        print(class_settings.lat,class_settings.lon)
+        
+        class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:(idatetime+class_settings.runtime),ilat,ilon])/100.
+   
+        input_nc.close()
+    
+    key = "GIMMS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
+       
+    
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+        print("Reading Leag Area Index from "+input_fn)
+        var = 'LAI'
+        
+        #plt.plot
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+        
+        print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+        tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+        
+        if np.isnan(tarray[idatetime]):
+            print("interpolating GIMMS cveg nan value")
+            
+            mask = np.isnan(tarray)
+            if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+            else:
+                print("Warning. Could not interpolate GIMMS cveg nan value")
+                
+        class_settings.__dict__[var] = tarray[idatetime]
+        
+        input_nc.close()
+ 
+    key = "IGBPDIS_ALPHA"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        var = 'alpha'
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+        print("Reading albedo from "+input_fn)
+    
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        
+        landfr = {}
+        for ltype in ['W','B','H','TC']:   
+            landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+        
+        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+        
+        alpha=0.
+        for ltype in landfr.keys():
+            alpha += landfr[ltype]*aweights[ltype]
+        
+        
+        class_settings.__dict__[var] = alpha
+        input_nc.close()        
+        
+        
+    key = "ERAINT_ST"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+        print("Reading soil temperature from "+input_fn)
+        
+        var = 'Tsoil'
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+        
+        
+        class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+        
+        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+        var = 'T2'
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+        
+        
+        class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+        
+        
+        input_nc.close()
+        
+        
+    
+    #inputs.append(cp.deepcopy(class_settings))
+    #var = 'T2'
+    #valold = class_settings.__dict__[var]
+    #
+    #class_settings.__dict__[var] = 305.
+    #class_settings.__dict__['Tsoil'] = 302.
+    #valnew = class_settings.__dict__[var]
+    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+    
+    
+    
+    #inputs.append(cp.deepcopy(class_settings))
+    #
+    #var = 'Lambda'
+    #valold = class_settings.__dict__[var]
+    
+    ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book. 
+    ## I need to ask Chiel.
+    ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+    #
+    #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg'] 
+    #class_settings.__dict__[var] = valnew
+    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+    
+    
+    
+    key = "GLAS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+        print("Reading canopy height for determining roughness length from "+input_fn)
+        var = 'z0m'
+    
+        
+        #plt.plot
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+        
+        lowerlimit = 0.01
+        if testval < lowerlimit:
+            print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+            class_settings.__dict__[var] = lowerlimit
+        else:
+            class_settings.__dict__[var] = testval
+        
+        class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+        
+        
+        input_nc.close()
+        
diff --git a/data_sounding.py b/data_sounding.py
new file mode 100644
index 0000000..011afe6
--- /dev/null
+++ b/data_sounding.py
@@ -0,0 +1,393 @@
+import numpy as np
+
+from bs4 import BeautifulSoup
+import pandas as pd
+import datetime as dt
+import pylab as pl
+import io
+import os
+import calendar
+import Pysolar
+import Pysolar.util
+#from urllib import request
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHd
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self):
+       self.status = 'init'
+       self.found = False
+       self.DT = None
+       self.current = None
+       self.mode = 'b'
+       self.profile_type = 'wyoming'  
+       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+         
+    def set_STNM(self,STNM):
+        self.__init__()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+        self.current = self.sounding_series.find('h2')
+        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+        
+        # go through other files and find first sounding when year is not specified
+        self.iFN=self.iFN+1
+        while keepsearching:
+            self.FN = self.FILES[self.iFN]
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            self.iFN=self.iFN+1
+            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+        self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        if self.found and get_atm:
+            self.get_values_one_column_atm()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                if get_atm:
+                    self.get_values_one_column_atm()
+                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        if self.found and get_atm:
+            self.get_values_one_column_atm()
+       
+
+
+    def get_values_one_column_atm(self):
+
+        # for iDT,DT in enumerate(DTS):
+        
+            #websource = urllib.request.urlopen(webpage)
+        #soup = BeautifulSoup(open(webpage), "html.parser")
+        
+       
+        #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTV','THTA','QABS']):
+                if len(np.where(HAGL <= BLH)[0]) >= 4:
+                    meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL <= BLH][2:-1],dtype=np.float))
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][1:2],dtype=np.float)
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV']:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta = np.max((0.1,(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) )
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+        PARAMS.insert(0,'day', PARAMS['datetime'][0].day)
+        PARAMS.insert(0,'tstart', PARAMS['datetime'][0].hour)
+        PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+        PARAMS.insert(0,'lon', np.float(PARAMS['Station longitude'][0]))
+        PARAMS['ldatetime'] = PARAMS.datetime.value - dt.timedelta(hours=PARAMS.lon.value/360.*24.) 
+        PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+        PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+        PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+        PARAMS.insert(0,'OK',((BLHVe < 100.) and (len(np.where(~pd.isnull(ONE_COLUMN['THTV'][ONE_COLUMN['HAGL'] > 5000.]))[0]) >0 )))
+        
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
+            
+        elif self.mode == 'b': # best BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
+            
+        elif self.mode == 'u':# upper BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNu['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNu['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNu['THTA'])[2]-list(ONE_COLUMNu['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNu['QABS'])[2]-list(ONE_COLUMNu['QABS'])[1]))
+            
+        elif self.mode == 'd': # lower BLH
+            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNd['THTA'])[1]))
+            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNd['QABS'])[1]))
+            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNd['THTA'])[2]-list(ONE_COLUMNd['THTA'])[1]))
+            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNd['QABS'])[2]-list(ONE_COLUMNd['QABS'])[1]))
+            
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        if self.mode == 'o': #original 
+            self.ONE_COLUMN = ONE_COLUMN
+        elif self.mode == 'b': # best BLH
+            self.ONE_COLUMN = ONE_COLUMNb
+        elif self.mode == 'u':# upper BLH
+            self.ONE_COLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # lower BLH
+            self.ONE_COLUMN=ONE_COLUMNd
+        else:
+            self.ONE_COLUMN = ONE_COLUMN
+
+class one_column(object):
+    def __init__(self):
+        self.status = 'init'
+        
+        
+    def set_one_column_atm(self,INPUT):
+        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
+
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = ONE_COLUMN
+        self.__dict__.update(PARAMS.to_dict()['value'])
+        self.__dict__.update(ONE_COLUMN.to_dict('list'))
+        self.status = 'filled'
+        
+        #convert all list to arrays for CLASS
+        for key in self.__dict__.keys():
+            if type(self.__dict__[key]).__name__ == 'list':
+                self.__dict__[key] = np.array(self.__dict__[key])
+
+

From 73aa61b24fc21a83dbaeea951b57d0b9751ad714 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 8 Nov 2017 17:09:33 +0100
Subject: [PATCH 002/129] make change to the main code to be able to read air
 profiles

---
 data_sounding.py | 393 -----------------------------------------------
 model.py         | 283 ++++++++++++++++++++++++++++++----
 2 files changed, 256 insertions(+), 420 deletions(-)
 delete mode 100644 data_sounding.py

diff --git a/data_sounding.py b/data_sounding.py
deleted file mode 100644
index 011afe6..0000000
--- a/data_sounding.py
+++ /dev/null
@@ -1,393 +0,0 @@
-import numpy as np
-
-from bs4 import BeautifulSoup
-import pandas as pd
-import datetime as dt
-import pylab as pl
-import io
-import os
-import calendar
-import Pysolar
-import Pysolar.util
-#from urllib import request
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_one_column_atm()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_one_column_atm()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_one_column_atm()
-       
-
-
-    def get_values_one_column_atm(self):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTV','THTA','QABS']):
-                if len(np.where(HAGL <= BLH)[0]) >= 4:
-                    meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL <= BLH][2:-1],dtype=np.float))
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][1:2],dtype=np.float)
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV']:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta = np.max((0.1,(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) )
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-        PARAMS.insert(0,'day', PARAMS['datetime'][0].day)
-        PARAMS.insert(0,'tstart', PARAMS['datetime'][0].hour)
-        PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-        PARAMS.insert(0,'lon', np.float(PARAMS['Station longitude'][0]))
-        PARAMS['ldatetime'] = PARAMS.datetime.value - dt.timedelta(hours=PARAMS.lon.value/360.*24.) 
-        PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-        PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-        PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-        PARAMS.insert(0,'OK',((BLHVe < 100.) and (len(np.where(~pd.isnull(ONE_COLUMN['THTV'][ONE_COLUMN['HAGL'] > 5000.]))[0]) >0 )))
-        
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
-            
-        elif self.mode == 'b': # best BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
-            
-        elif self.mode == 'u':# upper BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNu['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNu['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNu['THTA'])[2]-list(ONE_COLUMNu['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNu['QABS'])[2]-list(ONE_COLUMNu['QABS'])[1]))
-            
-        elif self.mode == 'd': # lower BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNd['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNd['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNd['THTA'])[2]-list(ONE_COLUMNd['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNd['QABS'])[2]-list(ONE_COLUMNd['QABS'])[1]))
-            
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        if self.mode == 'o': #original 
-            self.ONE_COLUMN = ONE_COLUMN
-        elif self.mode == 'b': # best BLH
-            self.ONE_COLUMN = ONE_COLUMNb
-        elif self.mode == 'u':# upper BLH
-            self.ONE_COLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # lower BLH
-            self.ONE_COLUMN=ONE_COLUMNd
-        else:
-            self.ONE_COLUMN = ONE_COLUMN
-
-class one_column(object):
-    def __init__(self):
-        self.status = 'init'
-        
-        
-    def set_one_column_atm(self,INPUT):
-        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
-
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = ONE_COLUMN
-        self.__dict__.update(PARAMS.to_dict()['value'])
-        self.__dict__.update(ONE_COLUMN.to_dict('list'))
-        self.status = 'filled'
-        
-        #convert all list to arrays for CLASS
-        for key in self.__dict__.keys():
-            if type(self.__dict__[key]).__name__ == 'list':
-                self.__dict__[key] = np.array(self.__dict__[key])
-
-
diff --git a/model.py b/model.py
index bb01df7..8e8efdc 100644
--- a/model.py
+++ b/model.py
@@ -1,4 +1,4 @@
-# 
+#_ 
 # CLASS
 # Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
 # Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
@@ -9,7 +9,7 @@
 # This file is part of CLASS
 # 
 # CLASS is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
+# it under the terms of the GNU General Public License as published bygamma
 # the Free Software Foundation, either version 3 of the License, or
 # (at your option) any later version.
 # 
@@ -43,7 +43,8 @@ def run(self):
         self.init()
   
         # time integrate model 
-        for self.t in range(self.tsteps):
+        #for self.t in range(self.tsteps):
+        while self.t < self.tsteps:
           
             # time integrate components
             self.timestep()
@@ -115,6 +116,13 @@ def init(self):
        
          # Temperature 
         self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
+        
+        
+        self.substep    = False
+        self.substeps   = 0
+
+
+
         self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
         self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
         self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
@@ -152,8 +160,14 @@ def init(self):
         self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
         self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
        
+        
+        
+        
+        
+        
         # Moisture 
         self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
+
         self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
         self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
         self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
@@ -167,6 +181,107 @@ def init(self):
         self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
         self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
       
+  # BEGIN -- HW 20170606
+
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        self.z_pro      = self.input.z_pro  # initial profile of potential temperature [K]
+
+        self.theta_pro  = self.input.theta_pro  # initial profile of potential temperature [K]
+
+
+        if ((self.theta_pro is not None) and (self.z_pro is not None)):
+
+            
+            indextheta = np.where(self.z_pro == self.h)
+            if len(indextheta) == 0:
+                raise RuntimeError("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
+                
+                
+            if indextheta[0][0] !=1:
+                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
+                stop
+
+            
+            if self.theta_pro[1] != self.theta_pro[0]:
+                print("error input profile consistency: two lowest profile levels should be equal.")
+            
+            # initialize theta from its profile when available
+            theta_old = self.theta
+            theta_new = self.theta_pro[indextheta[0][0]]
+            
+            
+            
+            if ((theta_old is not None) & (theta_old != theta_new)):
+                print("Warning: theta input was provided ("+str(theta_old)+\
+                    "kg kg-1), but it is now overwritten by the first level (index 0) of theta_pro which is different ("\
+                    +str(theta_new)+"K).")
+                                    
+            self.theta = theta_new
+
+            # make a profile of the stratification 
+            # please note that the stratification between z_pro[i] and z_pro[i+1] 
+            # is given by gammatheta_pro[i]
+
+            # self.gammatheta_pro = np.gradient(self.theta_pro) / np.gradient(self.z_pro)
+            with np.errstate(divide='ignore'):
+                self.gammatheta_pro = np.array(self.theta_pro[1:] - self.theta_pro[:-1]) \
+                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
+                           
+            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro)[0][-1]]
+        else:
+            self.gammatheta_pro = None
+
+
+        self.q_pro  = self.input.q_pro  # initial profile of potential temperature [K]
+
+        if ((self.q_pro is not None) and (self.z_pro is not None)):
+            
+            indexq = np.where(self.z_pro == self.h)
+            if len(indexq) == 0:
+                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
+                stop   
+                
+            if indexq[0][0] !=1:
+                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
+                stop
+
+            
+            if self.q_pro[1] != self.q_pro[0]:
+                print("error inpuy ptogilr consistency: two lowest profile levels should be equal.")
+            
+            # initialize q from its profile when available
+            q_old = self.q
+            q_new = self.q_pro[indexq[0][0]]
+            
+            
+            
+            if ((q_old is not None) & (q_old != q_new)):
+                print("Warning: q input was provided ("+str(q_old)+\
+                    "kg kg-1), but it is now overwritten by the first level (index 0) of q_pro which is different ("\
+                    +str(q_new)+"kg kg-1).")
+                                    
+            self.q = q_new
+
+            # make a profile of the stratification 
+            # please note that the stratification between z_pro[i] and z_pro[i+1] 
+            # is given by gammaq_pro[i]
+
+            # self.gammaq_pro = np.gradient(self.q_pro) / np.gradient(self.z_pro)
+            with np.errstate(divide='ignore'):
+                self.gammaq_pro = np.array(self.q_pro[1:] - self.q_pro[:-1]) \
+                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
+                           
+            self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
+        else:
+            self.gammaq_pro = None
+
+# END -- HW 20170606      
+        
+        
+        
+        
+        
         # CO2
         fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
         self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
@@ -282,7 +397,7 @@ def init(self):
 
         # initialize A-Gs surface scheme
         self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
- 
+
         # initialize cumulus parameterization
         self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
         self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
@@ -293,6 +408,8 @@ def init(self):
         # initialize time variables
         self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
         self.dt     = self.input.dt
+        self.dtcur      = self.dt
+        self.firsttime = True
         self.t      = 0
  
         # Some sanity checks for valid input
@@ -346,8 +463,23 @@ def timestep(self):
         if(self.sw_ml):
             self.run_mixed_layer()
  
+        #get first profile data point above mixed layer
+        zidx_first = np.where(self.z_pro > self.h)[0][0]
+        
+        if self.htend != 0.:
+            dtmax = ( self.z_pro[zidx_first] - self.h)/self.htend
+        else:
+            dtmax = +np.inf
+        
+        
+        self.substep =  (self.dtcur > dtmax)
+        if self.substep:
+            dtnext = self.dtcur - dtmax
+            self.dtcur = dtmax
+        # HW: this will be done multiple times in case of a substep is needed
         # store output before time integration
-        self.store()
+        if self.firsttime:
+            self.store()
   
         # time integrate land surface model
         if(self.sw_ls):
@@ -356,13 +488,25 @@ def timestep(self):
         # time integrate mixed-layer model
         if(self.sw_ml):
             self.integrate_mixed_layer()
+        
+        if self.substep:
+            self.dtcur = dtnext
+            self.firsttime = False
+            self.substeps += 1
+        else:
+            self.dtcur = self.dt
+            self.t += 1 
+            self.firsttime = True
+            self.substeps = 0
+        
+        
+        
   
     def statistics(self):
         # Calculate virtual temperatures 
         self.thetav   = self.theta  + 0.61 * self.theta * self.q
         self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
         self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
-
         # Mixed-layer top properties
         self.P_h    = self.Ps - self.rho * self.g * self.h
         self.T_h    = self.theta - self.g/self.cp * self.h
@@ -390,7 +534,7 @@ def statistics(self):
 
         if(it == itmax):
             print("LCL calculation not converged!!")
-            print("RHlcl = %f, zlcl=%f"%(RHlcl, self.lcl))
+            print("RHlcl = %f, zlcl=%f, theta=%f, q=%f"%(RHlcl, self.lcl,self.theta,self.q))
 
     def run_cumulus(self):
         # Calculate mixed-layer top relative humidity variance (Neggers et. al 2006/7)
@@ -417,10 +561,12 @@ def run_mixed_layer(self):
             # decompose ustar along the wind components
             self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
             self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
-      
+
+
+
         # calculate large-scale vertical velocity (subsidence)
         self.ws = -self.divU * self.h
-      
+              
         # calculate compensation to fix the free troposphere in case of subsidence 
         if(self.sw_fixft):
             w_th_ft  = self.gammatheta * self.ws
@@ -448,7 +594,6 @@ def run_mixed_layer(self):
             self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
         else:
             self.we    = -self.wthetave / self.dthetav
-
         # Don't allow boundary layer shrinking if wtheta < 0 
         if(self.we < 0):
             self.we = 0.
@@ -457,9 +602,9 @@ def run_mixed_layer(self):
         self.wthetae     = -self.we * self.dtheta
         self.wqe         = -self.we * self.dq
         self.wCO2e       = -self.we * self.dCO2
-  
+        
         self.htend       = self.we + self.ws + self.wf - self.M
-       
+        
         self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
         self.qtend       = (self.wq     - self.wqe     - self.wqM  ) / self.h + self.advq
         self.CO2tend     = (self.wCO2   - self.wCO2e   - self.wCO2M) / self.h + self.advCO2
@@ -501,14 +646,19 @@ def integrate_mixed_layer(self):
         dz0     = self.dz_h
   
         # integrate mixed-layer equations
-        self.h        = h0      + self.dt * self.htend
-        self.theta    = theta0  + self.dt * self.thetatend
-        self.dtheta   = dtheta0 + self.dt * self.dthetatend
-        self.q        = q0      + self.dt * self.qtend
-        self.dq       = dq0     + self.dt * self.dqtend
-        self.CO2      = CO20    + self.dt * self.CO2tend
-        self.dCO2     = dCO20   + self.dt * self.dCO2tend
-        self.dz_h     = dz0     + self.dt * self.dztend
+        
+            
+
+# END -- HW 20170606        
+        
+        self.h        = h0      + self.dtcur * self.htend
+        self.theta    = theta0  + self.dtcur * self.thetatend
+        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+        self.q        = q0      + self.dtcur * self.qtend
+        self.dq       = dq0     + self.dtcur * self.dqtend
+        self.CO2      = CO20    + self.dtcur * self.CO2tend
+        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
+        self.dz_h     = dz0     + self.dtcur * self.dztend
 
         # Limit dz to minimal value
         dz0 = 50
@@ -516,10 +666,65 @@ def integrate_mixed_layer(self):
             self.dz_h = dz0 
   
         if(self.sw_wind):
-            self.u        = u0      + self.dt * self.utend
-            self.du       = du0     + self.dt * self.dutend
-            self.v        = v0      + self.dt * self.vtend
-            self.dv       = dv0     + self.dt * self.dvtend
+            self.u        = u0      + self.dtcur * self.utend
+            self.du       = du0     + self.dtcur * self.dutend
+            self.v        = v0      + self.dtcur * self.vtend
+            self.dv       = dv0     + self.dtcur * self.dvtend
+
+
+        
+        # note that theta and q itself are updatet by class itself
+
+        #Afterwards, update the vertical profiles by removing any data points below the new h
+        if self.theta_pro is not None:
+            self.theta_pro = np.array([self.theta,self.theta,self.theta+self.dtheta]+list(self.theta_pro[self.z_pro > self.h]))
+
+        if self.q_pro is not None:
+            self.q_pro = np.array([self.q,self.q,self.q+self.dq]+list(self.q_pro[self.z_pro > self.h]))
+
+
+
+        if self.z_pro is not None:
+            self.z_pro = np.array([2.,self.h,self.h]+list(self.z_pro[self.z_pro > self.h]))
+
+        
+        if self.gammatheta_pro is not None:
+            
+                        # self.gammatheta_pro = np.gradient(self.theta_pro) / np.gradient(self.z_pro)
+            with np.errstate(divide='ignore'):
+                self.gammatheta_pro = np.array(self.theta_pro[1:] - self.theta_pro[:-1]) \
+                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
+                           
+            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro)[0][-1]]
+            
+            
+            
+            #self.gammatheta_pro = np.array([self.gammatheta,self.gammatheta,self.gammatheta]+list(self.gammatheta_pro[self.z_pro[:-1] > self.h]))
+
+        if self.gammaq_pro is not None:            
+                        
+            #self.gammaq_pro = np.gradient(self.q_pro) / np.gradient(self.z_pro)
+            with np.errstate(divide='ignore'):
+                self.gammaq_pro = (self.q_pro[1:] -self.q_pro[:-1])  / (self.z_pro[1:] -self.z_pro[:-1])
+            
+            self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
+            
+            
+            self.gammaq_pro = np.array([self.gammaq,self.gammaq,self.gammaq]+list(self.gammaq_pro[self.z_pro[:-1] > self.h]))
+
+
+#	# BEGIN -- HW 20170606
+#        # get new gammatheta(h) from its vertical profile when available:
+#        if(self.gammatheta_pro != None):
+#            #self.gammatheta = np.interp(self.h, self.z_pro, self.gammatheta_pro) 
+#            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro )[0][-1]]
+#
+#        # get gammaq(h) from its vertical profile when available:
+ #       if(self.gammaq_pro != None):
+ #           #self.gammaq = np.interp(self.h, self.z_pro, self.gammaq_pro) 
+ #           self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
+#
+        
  
     def run_radiation(self):
         sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
@@ -532,6 +737,8 @@ def run_radiation(self):
   
         self.Swin  = self.S0 * Tr * sinlea
         self.Swout = self.alpha * self.S0 * Tr * sinlea
+        
+        
         self.Lwin  = 0.8 * self.bolz * Ta ** 4.
         self.Lwout = self.bolz * self.Ts ** 4.
           
@@ -804,9 +1011,9 @@ def integrate_land_surface(self):
         wg0           = self.wg
         Wl0           = self.Wl
   
-        self.Tsoil    = Tsoil0  + self.dt * self.Tsoiltend
-        self.wg       = wg0     + self.dt * self.wgtend
-        self.Wl       = Wl0     + self.dt * self.Wltend
+        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
+        self.wg       = wg0     + self.dtcur * self.wgtend
+        self.Wl       = Wl0     + self.dtcur * self.Wltend
   
     # store model output
     def store(self):
@@ -814,6 +1021,12 @@ def store(self):
         self.out.t[t]          = t * self.dt / 3600. + self.tstart
         self.out.h[t]          = self.h
         
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the following loop:
+        #   for key in list(self.out.__dict__.keys()):
+        #       self.out.__dict__[key][t] = self[key]
+        
+        self.out.gammatheta[t] = self.gammatheta
+        self.out.gammaq[t]     = self.gammaq
         self.out.theta[t]      = self.theta
         self.out.thetav[t]     = self.thetav
         self.out.dtheta[t]     = self.dtheta
@@ -888,6 +1101,7 @@ def store(self):
         self.out.ac[t]         = self.ac
         self.out.M[t]          = self.M
         self.out.dz[t]         = self.dz_h
+        self.out.substeps[t]   = self.substeps
   
     # delete class variables to facilitate analysis in ipython
     def exitmodel(self):
@@ -1048,6 +1262,8 @@ def __init__(self, tsteps):
         self.h          = np.zeros(tsteps)    # ABL height [m]
         
         self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
         self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
         self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
         self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
@@ -1129,6 +1345,8 @@ def __init__(self, tsteps):
         self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
         self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
         self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
+        
+        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
 
 # class for storing mixed-layer model input data
 class model_input:
@@ -1147,6 +1365,10 @@ def __init__(self):
         self.fc         = None  # Coriolis parameter [s-1]
         
         self.theta      = None  # initial mixed-layer potential temperature [K]
+        self.theta_pro  = None  # optional/initial profile of potential temperature [K]
+
+        self.z_pro      = None  # height coordinate of the optional input profiles [m]
+
         self.dtheta     = None  # initial temperature jump at h [K]
         self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
         self.advtheta   = None  # advection of heat [K s-1]
@@ -1154,6 +1376,8 @@ def __init__(self):
         self.wtheta     = None  # surface kinematic heat flux [K m s-1]
         
         self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
+        self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+
         self.dq         = None  # initial specific humidity jump at h [kg kg-1]
         self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
         self.advq       = None  # advection of moisture [kg kg-1 s-1]
@@ -1238,3 +1462,8 @@ def __init__(self):
         # Cumulus parameters
         self.sw_cu      = None  # Cumulus parameterization switch
         self.dz_h       = None  # Transition layer thickness [m]
+        
+# BEGIN -- HW 20171027
+        self.cala       = None      # soil heat conductivity [W/(K*m)]
+        self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+# END -- HW 20171027
\ No newline at end of file

From f9c88f9b01f9dc7b9b7b863574f596870da9fcd5 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Fri, 10 Nov 2017 10:49:57 +0100
Subject: [PATCH 003/129] improve structure by providing a class4gl.py wrapper
 around model.py

---
 class4gl.py | 28 ++++++++++++++++++++++++++++
 data_air.py | 31 +++++++------------------------
 model.py    |  2 +-
 3 files changed, 36 insertions(+), 25 deletions(-)
 create mode 100644 class4gl.py

diff --git a/class4gl.py b/class4gl.py
new file mode 100644
index 0000000..147c934
--- /dev/null
+++ b/class4gl.py
@@ -0,0 +1,28 @@
+# class4gl extends the standard 'model' environment to be able to take global air profiles as input 
+
+from model import model as class4gl
+from model import model_output as class4gl_output
+import numpy as np
+
+class air_input(object):
+    def __init__(self):
+        self.status = 'init'
+        
+        
+    def set_air_input(self,INPUT):
+        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
+
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = ONE_COLUMN
+        self.__dict__.update(PARAMS.to_dict()['value'])
+        self.__dict__.update(ONE_COLUMN.to_dict('list'))
+        self.status = 'filled'
+        
+        #convert all list to arrays for CLASS
+        for key in self.__dict__.keys():
+            if type(self.__dict__[key]).__name__ == 'list':
+                self.__dict__[key] = np.array(self.__dict__[key])
+
+from model import model_input
+class4gl_input = type('class4gl_input', (model_input,air_input), dict(c='c'))
+
diff --git a/data_air.py b/data_air.py
index 011afe6..3427915 100644
--- a/data_air.py
+++ b/data_air.py
@@ -9,6 +9,9 @@
 import calendar
 import Pysolar
 import Pysolar.util
+
+
+
 #from urllib import request
 def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
     
@@ -122,7 +125,7 @@ def find_first(self,year=None,get_atm=False):
             self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
         
         if self.found and get_atm:
-            self.get_values_one_column_atm()
+            self.get_values_air_input()
         
     
     def find(self,DT,get_atm=False):
@@ -145,7 +148,7 @@ def find(self,DT,get_atm=False):
                 self.found = True
                 keepsearching = False
                 if get_atm:
-                    self.get_values_one_column_atm()
+                    self.get_values_air_input()
                     self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
             elif DTcurrent > DT:
                 keepsearching = False
@@ -177,11 +180,11 @@ def find_next(self,get_atm=False):
         if self.found:        
             self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
         if self.found and get_atm:
-            self.get_values_one_column_atm()
+            self.get_values_air_input()
        
 
 
-    def get_values_one_column_atm(self):
+    def get_values_air_input(self):
 
         # for iDT,DT in enumerate(DTS):
         
@@ -371,23 +374,3 @@ def get_values_one_column_atm(self):
         else:
             self.ONE_COLUMN = ONE_COLUMN
 
-class one_column(object):
-    def __init__(self):
-        self.status = 'init'
-        
-        
-    def set_one_column_atm(self,INPUT):
-        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
-
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = ONE_COLUMN
-        self.__dict__.update(PARAMS.to_dict()['value'])
-        self.__dict__.update(ONE_COLUMN.to_dict('list'))
-        self.status = 'filled'
-        
-        #convert all list to arrays for CLASS
-        for key in self.__dict__.keys():
-            if type(self.__dict__[key]).__name__ == 'list':
-                self.__dict__[key] = np.array(self.__dict__[key])
-
-
diff --git a/model.py b/model.py
index 8e8efdc..077e6c1 100644
--- a/model.py
+++ b/model.py
@@ -1466,4 +1466,4 @@ def __init__(self):
 # BEGIN -- HW 20171027
         self.cala       = None      # soil heat conductivity [W/(K*m)]
         self.crhoc      = None      # soil heat capacity  [J/K*m**3]
-# END -- HW 20171027
\ No newline at end of file
+# END -- HW 20171027

From a1ce7c02b06457d1ef80e4f210cd420b5701f60c Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 20 Aug 2018 14:02:06 +0200
Subject: [PATCH 004/129] this is the beta version of class4gl

---
 class4gl.py            | 1615 ++++++++++++++++++++++++++++++-
 data_air.py            |  209 ++--
 data_global.py         |  936 ++++++++++++++++++
 interface_functions.py |  506 ++++++++++
 interface_multi.py     | 2061 ++++++++++++++++++++++++++++++++++++++++
 model.py               | 1155 ++++++++++++++++++----
 ribtol_hw.py           |  165 ++++
 7 files changed, 6370 insertions(+), 277 deletions(-)
 create mode 100644 data_global.py
 create mode 100644 interface_functions.py
 create mode 100644 interface_multi.py
 create mode 100644 ribtol_hw.py

diff --git a/class4gl.py b/class4gl.py
index 147c934..7baaa51 100644
--- a/class4gl.py
+++ b/class4gl.py
@@ -1,28 +1,1611 @@
-# class4gl extends the standard 'model' environment to be able to take global air profiles as input 
+# -*- coding: utf-8 -*-
 
-from model import model as class4gl
+"""
+
+Created on Mon Jan 29 12:33:51 2018
+
+Module file for class4gl, which  extents the class-model to be able to take
+global air profiles as input. It exists of:
+
+CLASSES:
+    - an input object, namely class4gl_input. It includes:
+        - a function to read Wyoming sounding data from a yyoming stream object
+        - a function to read global data from a globaldata library object 
+    - the model object: class4gl
+    - ....    
+
+DEPENDENCIES:
+    - xarray
+    - numpy
+    - data_global
+    - Pysolar
+    - yaml
+
+@author: Hendrik Wouters
+
+"""
+
+
+
+""" Setup of envirnoment """
+
+# Standard modules of the stand class-boundary-layer model
+from model import model
 from model import model_output as class4gl_output
+from model import model_input
+from model import qsat
+#from data_soundings import wyoming 
+import Pysolar
+import yaml
+import logging
+import warnings
+import pytz
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+
+# Generic Python Packages
 import numpy as np
+import datetime as dt
+import pandas as pd
+import xarray as xr
+import io
+#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
+from data_global import data_global
+grav = 9.81
 
-class air_input(object):
+# this is just a generic input object
+class generic_input(object):
     def __init__(self):
-        self.status = 'init'
+        self.init = True
+
+
+# all units from all variables in CLASS(4GL) should be defined here!
+units = {
+         'h':'m',
+         'theta':'K', 
+         'q':'kg/kg',
+         'cc': '-',
+         'cveg': '-',
+         'wg': 'm3 m-3',
+         'w2': 'm3 m-3',
+         #'wg': 'kg/kg',
+         'Tsoil': 'K',
+         'T2': 'K',
+         'z0m': 'm',
+         'alpha': '-',
+         'LAI': '-',
+         'dhdt':'m/h',
+         'dthetadt':'K/h',
+         'dqdt':'kg/kg/h',
+         'BR': '-',
+         'EF': '-',
+}
+
+class class4gl_input(object):
+# this was the way it was defined previously.
+#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+
+    def __init__(self,set_pars_defaults=True,debug_level=None):
+
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        print('hello')
+        self.logger = logging.getLogger('class4gl_input')
+        print(self.logger)
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # # create logger
+        # self.logger = logging.getLogger('class4gl_input')
+        # self.logger.setLevel(debug_level)
+
+        # # create console handler and set level to debug
+        # ch = logging.StreamHandler()
+        # ch.setLevel(debug_level)
+
+        # # create formatter
+        # formatter = logging.Formatter('%(asctime)s - \
+        #                                %(name)s - \
+        #                                %(levelname)s - \
+        #                                %(message)s')
+        # add formatter to ch
+        # ch.setFormatter(formatter)
+     
+        # # add ch to logger
+        # self.logger.addHandler(ch)
+
+        # """ end set up logger """
+
+
+
+        # these are the standard model input single-value parameters for class
+        self.pars = model_input()
+
+        # diagnostic parameters of the initial profile
+        self.diag = dict()
+
+        # In this variable, we keep track of the different parameters from where it originates from. 
+        self.sources = {}
+
+        if set_pars_defaults:
+            self.set_pars_defaults()
+
+    def set_pars_defaults(self):
+
+        """ 
+        Create empty model_input and set up case
+        """
+        defaults = dict( 
+        dt         = 60.    , # time step [s] 
+        runtime    = 6*3600 ,  # total run time [s]
+        
+        # mixed-layer input
+        sw_ml      = True   ,  # mixed-layer model switch
+        sw_shearwe = False  ,  # shear growth mixed-layer switch
+        sw_fixft   = False  ,  # Fix the free-troposphere switch
+        h          = 200.   ,  # initial ABL height [m]
+        Ps         = 101300.,  # surface pressure [Pa]
+        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
+        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
+        
+        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
+        dtheta     = 1.     ,  # initial temperature jump at h [K]
+        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        advtheta   = 0.     ,  # advection of heat [K s-1]
+        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
+        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
+        
+        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
+        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
+        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
+        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
+        
+        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
+        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
+        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
+        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
+        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
+        sw_wind    = True  ,  # prognostic wind switch
+        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
+        du         = 0.     ,  # initial u-wind jump at h [m s-1]
+        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
+        advu       = 0.     ,  # advection of u-wind [m s-2]
+        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
+        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
+        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
+        advv       = 0.     ,  # advection of v-wind [m s-2]
+        sw_sl      = True   , # surface layer switch
+        ustar      = 0.3    ,  # surface friction velocity [m s-1]
+        z0m        = 0.02   ,  # roughness length for momentum [m]
+        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
+        sw_rad     = True   , # radiation switch
+        lat        = 51.97  ,  # latitude [deg]
+        lon        = -4.93  ,  # longitude [deg]
+        doy        = 268.   ,  # day of the year [-]
+        tstart     = 6.8    ,  # time of the day [h UTC]
+        cc         = 0.0    ,  # cloud cover fraction [-]
+        Q          = 400.   ,  # net radiation [W m-2] 
+        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
+        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
+        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
+        cveg       = 0.85   ,  # vegetation fraction [-]
+        Tsoil      = 295.   ,  # temperature top soil layer [K]
+        Ts         = 295.   ,    # initial surface temperature [K]
+        T2         = 296.   ,  # temperature deeper soil layer [K]
+        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
+        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
+        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
+        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
+        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
+        wfc        = 0.323  ,  # volumetric water content field capacity [-]
+        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
+        C1sat      = 0.132  ,  
+        C2ref      = 1.8    ,
+        LAI        = 2.     ,  # leaf area index [-]
+        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
+        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
+        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
+        alpha      = 0.25   ,  # surface albedo [-]
+        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
+        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
+        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
+        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
+        sw_cu      = False  ,  # Cumulus parameterization switch
+        dz_h       = 150.   ,  # Transition layer thickness [m]
+        cala       = None   ,  # soil heat conductivity [W/(K*m)]
+        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
+        sw_ls      = True   ,
+        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
+        sw_lit     = False,
+        )
+        pars = model_input()
+        for key in defaults:
+            pars.__dict__[key] = defaults[key]
+        
+        self.update(source='defaults',pars=pars)
+        
+    def clear(self):
+        """ this procudure clears the class4gl_input """
+
+        for key in list(self.__dict__.keys()):
+            del(self.__dict__[key])
+        self.__init__()
+
+    def dump(self,file):
+        """ this procedure dumps the class4gl_input object into a yaml file
+            
+            Input: 
+                - self.__dict__ (internal): the dictionary from which we read 
+            Output:
+                - file: All the parameters in self.__init__() are written to
+                the yaml file, including pars, air_ap, sources etc.
+        """
+        file.write('---\n')
+        index = file.tell()
+        file.write('# CLASS4GL input; format version: 0.1\n')
+
+        # write out the position of the current record
+        yaml.dump({'index':index}, file, default_flow_style=False)
+
+        # we do not include the none values
+        for key,data in self.__dict__.items():
+            #if ((type(data) == model_input) or (type(class4gl_input):
+            if key == 'pars':
+
+                pars = {'pars' : self.__dict__['pars'].__dict__}
+                parsout = {}
+                for key in pars.keys():
+                    if pars[key] is not None:
+                        parsout[key] = pars[key]
+
+                yaml.dump(parsout, file, default_flow_style=False)
+            elif type(data) == dict:
+                if key == 'sources':
+                    # in case of sources, we want to have a
+                    # condensed list format as well, so we leave out
+                    # 'default_flow_style=False'
+                    yaml.dump({key : data}, file)
+                else: 
+                    yaml.dump({key : data}, file,
+                              default_flow_style=False)
+            elif type(data) == pd.DataFrame:
+                # in case of dataframes (for profiles), we want to have a
+                # condensed list format as well, so we leave out
+                # 'default_flow_style=False'
+                yaml.dump({key: data.to_dict(orient='list')},file)
+
+                # # these are trials to get it into a more human-readable
+                # fixed-width format, but it is too complex
+                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
+                #file.write(stream)
+                
+                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
+                #file.write(key+': !!str |\n')
+                #file.write(str(data)+'\n')
+       
+    def load_yaml_dict(self,yaml_dict,reset=True):
+        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
+            
+            Input: 
+                - yaml_dict: the dictionary from which we read 
+                - reset: reset data before reading        
+            Output:
+                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
+        """
+        
+        if reset:
+            for key in list(self.__dict__.keys()):
+                del(self.__dict__[key])
+            self.__init__()
+
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                self.__dict__[key] = model_input()
+                self.__dict__[key].__dict__ = data
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            elif key == 'sources':
+                self.__dict__[key] = data
+            elif key == 'diag':
+                self.__dict__[key] = data
+            else: 
+                warnings.warn("Key '"+key+"' may not be implemented.")
+                self.__dict__[key] = data
+
+    def update(self,source,**kwargs):
+        """ this procedure is to make updates of input parameters and tracking
+        of their source more convenient. It implements the assignment of
+        parameter source/sensitivity experiment IDs ('eg.,
+        'defaults', 'sounding balloon', any satellite information, climate
+        models, sensitivity tests etc.). These are all stored in a convenient
+        way with as class4gl_input.sources.  This way, the user can always consult with
+        from where parameters data originates from.  
+        
+        Input:
+            - source:    name of the underlying dataset
+            - **kwargs: a dictionary of data input, for which the key values
+            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
+            the values is a again a dictionary/dataframe of datakeys/columns
+            ('wg','PRES','datetime', ...) and datavalues (either single values,
+            profiles ...), eg., 
+
+                pars = {'wg': 0.007  , 'w2', 0.005}
+                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
+                                     300.,...]}
+            
+        Output:
+            - self.__dict__[datatype] : object to which the parameters are
+                                        assigned. They can be consulted with
+                                        self.pars, self.profiles, etc.
+                                        
+            - self.sources[source] : It supplements the overview overview of
+                                     data sources can be consulted with
+                                     self.sources. The structure is as follows:
+                                     as:
+                self.sources = { 
+                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
+                'GLEAM' :  ['pars:wg','pars:w2', ...],
+                 ...
+                }
+        
+        """
+
+        #print(source,kwargs)
+
+        for key,data in kwargs.items():
+
+            #print(key)
+            # if the key is not in class4gl_input object, then just add it. In
+            # that case, the update procedures below will just overwrite it 
+            if key not in self.__dict__:
+                self.__dict__[key] = data
+
+
+            
+
+            #... we do an additional check to see whether there is a type
+            # match. I not then raise a key error
+            if (type(data) != type(self.__dict__[key]) \
+                # we allow dict input for model_input pars
+                and not ((key == 'pars') and (type(data) == dict) and \
+                (type(self.__dict__[key]) == model_input))):
+
+                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
+
+
+            # This variable keeps track of the added data that is supplemented
+            # by the current source. We add this to class4gl_input.sources
+            datakeys = []
+
+            #... and we update the class4gl_input data, and this depends on the
+            # data type
+
+            if type(self.__dict__[key]) == pd.DataFrame:
+                # If the data type is a dataframe, then we update the columns
+                for column in list(data.columns):
+                    #print(column)
+                    self.__dict__[key][column] = data[column]
+                    datakeys.append(column)
+                    
+
+            elif type(self.__dict__[key]) == model_input:
+                # if the data type is a model_input, then we update its internal
+                # dictionary of parameters
+                if type(data) == model_input:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data.__dict__}
+                    datakeys = list(data.__dict__.keys())
+                elif type(data) == dict:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data}
+                    datakeys = list(data.keys())
+                else:
+                    raise TypeError('input key '+key+' is not of the same type\
+                                    as the one in the class4gl_object')
+
+            elif type(self.__dict__[key]) == dict:
+                # if the data type is a dictionary, we update the
+                # dictionary 
+                self.__dict__[key] = {self.__dict__[key] , data}
+                datakeys = list(data.keys())
+
+
+            # if source entry is not existing yet, we add it
+            if source not in self.sources.keys():
+                self.sources[source] = []
+
+
+            # self.logger.debug('updating section "'+\
+            #                  key+' ('+' '.join(datakeys)+')'\
+            #                  '" from source \
+            #                  "'+source+'"')
+
+            # Update the source dictionary: add the provided data keys to the
+            # specified source list
+            for datakey in datakeys:
+                # At first, remove the occurences of the keys in the other
+                # source lists
+                for sourcekey,sourcelist in self.sources.items():
+                    if key+':'+datakey in sourcelist:
+                        self.sources[sourcekey].remove(key+':'+datakey)
+                # Afterwards, add it to the current source list
+                self.sources[source].append(key+':'+datakey)
+
+
+        # # in case the datatype is a class4gl_input_pars, we update its keys
+        # # according to **kwargs dictionary
+        # if type(self.__dict__[datatype]) == class4gl_input_pars:
+        #     # add the data parameters to the datatype object dictionary of the
+        #     # datatype
+        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
+        #                                        **kwargs}
+        # # in case, the datatype reflects a dataframe, we update the columns according
+        # # to the *args list
+        # elif type(self.__dict__[datatype]) == pd.DataFrame:
+        #     for dataframe in args:
+        #         for column in list(dataframe.columns):
+        #             self.__dict__[datatype][column] = dataframe[column]
+        
+
+    def get_profile(self,IOBJ, *args, **argv):
+        # if type(IOBJ) == wyoming:
+        self.get_profile_wyoming(IOBJ,*args,**argv)
+        # else:
+        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
+        
+    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
+        """ 
+            Purpose: 
+                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
+
+            Input:
+                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
+                function will take the profile at the stream's current
+                position. 
+                2. air_ap_mode: which air profile do we take? 
+                    - b : best
+                    - l : according to lower limit for the mixed-layer height
+                            estimate
+                    - u : according to upper limit for the mixed-layer height
+                            estimate
+
+
+            Output:
+                1. all single-value parameters are stored in the
+                   class4gl_input.pars object
+                2. the souding profiles are stored in the in the
+                   class4gl_input.air_balloon dataframe
+                3. modified sounding profiles for which the mixed layer height
+                   is fitted
+                4. ...
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = 'wyoming'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = air_ap_mode
+
+        """ Temporary variables used for output """
+        # single value parameters derived from the sounding profile
+        dpars = dict()
+        # profile values
+        air_balloon = pd.DataFrame()
+        # fitted profile values
+        air_ap = pd.DataFrame()
+        
+        string = wy_strm.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = wy_strm.current.find_next('pre').find_next('pre').text
+        
+        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
+        dpars = {**dpars,
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+               }
+        
+        # we get weird output when it's a numpy Timestamp, so we convert it to
+        # pd.datetime type
+
+        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
+        dpars['STNID'] = dpars['Station number']
+
+        # altitude above ground level
+        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        # absolute humidity in g/kg
+        air_balloon['q']= (air_balloon.MIXR/1000.) \
+                              / \
+                             (air_balloon.MIXR/1000.+1.)
+        # convert wind speed from knots to m/s
+        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
+        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        
+        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+
+        
+
+        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+
+        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
+        air_balloon['p'] = air_balloon.PRES*100.
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        #is_valid = (air_balloon.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_balloon.index[is_valid].values
+        print(valid_indices)
+
+        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+
+        air_balloon['t'] = air_balloon['TEMP']+273.15
+        air_balloon['theta'] = (air_balloon.t) * \
+                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+
+
+        if np.isnan(dpars['h']):
+            dpars['Ps'] = np.nan
+
+
+
+
+        if ~np.isnan(dpars['h']):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u 
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+            
+
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        air_ap_head['HGHT'] = air_ap_head['z'] \
+                                + \
+                                np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = dpars['theta']
+        z_low =     dpars['h']
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
         
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+
+        
+        dpars['lat'] = dpars['Station latitude']
+        dpars['latitude'] = dpars['lat']
+        
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        dpars['longitude'] = dpars['Station longitude']
+        
+        dpars['ldatetime'] = dpars['datetime'] \
+                            + \
+                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
         
-    def set_air_input(self,INPUT):
-        PARAMS,ONE_COLUMN = INPUT.PARAMS,INPUT.ONE_COLUMN
 
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = ONE_COLUMN
-        self.__dict__.update(PARAMS.to_dict()['value'])
-        self.__dict__.update(ONE_COLUMN.to_dict('list'))
-        self.status = 'filled'
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+
+        # # we make a pars object that is similar to the destination object
+        # pars = model_input()
+        # for key,value in dpars.items():
+        #     pars.__dict__[key] = value
+
+
+        # we round the columns to a specified decimal, so that we get a clean
+        # output format for yaml
+        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
+                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
+                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+# 
+        for column,decimal in decimals.items():
+            air_balloon[column] = air_balloon[column].round(decimal)
+            air_ap[column] = air_ap[column].round(decimal)
+
+        self.update(source='wyoming',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+
+        
+    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
+    
+        """
+        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
+                 according to the position (lat lon) and the class datetime and timespan
+                 globaldata should be a globaldata multifile object
         
-        #convert all list to arrays for CLASS
+        Input: 
+            - globaldata: this is the library object
+            - only_keys: only extract specified keys
+            - exclude_keys: do not inherit specified keys
+        """
+        classdatetime      = np.datetime64(self.pars.datetime_daylight)
+        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
+                                           + \
+                                           dt.timedelta(seconds=self.pars.runtime)\
+                                          )
+
+
+        # # list of variables that we get from global ground data
+        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
+        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
+        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
+        #                 'texture', 'itex', 'isoil', 'BR',
+        #                 'b', 'cveg',
+        #                 'C1sat', 
+        #                 'C2ref', 'p', 'a',
+        #                 ] #globaldata.datasets.keys():
+
+        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
+        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
+
+
+        if type(globaldata) is not data_global:
+            raise TypeError("Wrong type of input library") 
+
+        # by default, we get all dataset keys
+        keys = list(globaldata.datasets.keys())
+
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
+        # # In case there is surface pressure, we also calculate the half-level
+        # # and full-level pressure fields
+        # if ('sp' in keys):
+        #     keys.append('pfull')
+        #     keys.append('phalf')
+
+        # If specified, we only take the keys that are in only_keys
+        if only_keys is not None:
+            for key in keys:
+                if key not in only_keys:
+                    keys.remove(key)
+                
+        # If specified, we take out keys that are in exclude keys
+        if exclude_keys is not None:
+            for key in keys:
+                if key in exclude_keys:
+                    keys.remove(key)
+
+        # we set everything to nan first in the pars section (non-profile parameters
+        # without lev argument), so that we can check afterwards whether the
+        # data is well-fetched or not.
+        for key in keys:
+            if not ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None) and \
+                ('lev' in globaldata.datasets[key].page[key].dims)):
+                self.update(source='globaldata',pars={key:np.nan})
+            # # we do not check profile input for now. We assume it is
+            # # available
+            #else:
+            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
+
+        self.logger.debug('getting keys "'+', '.join(keys)+'\
+                          from global data')
+
+        for key in keys:
+            # If we find it, then we obtain the variables
+            if ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None)):
+
+                # check first whether the dataset has a height coordinate (3d space)
+                if 'lev' in globaldata.datasets[key].page[key].dims:
+
+                    # first, we browse to the correct file that has the current time
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+                        globaldata.datasets[key].browse_page(time=classdatetime)
+
+                    
+                    if (globaldata.datasets[key].page is not None):
+                        # find longitude and latitude coordinates
+                        ilats = (np.abs(globaldata.datasets[key].page.lat -
+                                        self.pars.latitude) < 0.5)
+                        ilons = (np.abs(globaldata.datasets[key].page.lon -
+                                        self.pars.longitude) < 0.5)
+                        
+                        # if we have a time dimension, then we look up the required timesteps during the class simulation
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            itimes = ((globaldata.datasets[key].page.time >= \
+                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            # In case we didn't find any correct time, we take the
+                            # closest one.
+                            if np.sum(itimes) == 0.:
+
+
+                                classdatetimemean = \
+                                    np.datetime64(self.pars.datetime_daylight + \
+                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
+                                                ))
+
+                                dstimes = globaldata.datasets[key].page.time
+                                time = dstimes.sel(time=classdatetimemean,method='nearest')
+                                itimes = (globaldata.datasets[key].page.time ==
+                                          time)
+                                
+                        else:
+                            # we don't have a time coordinate so it doesn't matter
+                            # what itimes is
+                            itimes = 0
+
+                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+
+                        # over which dimensions we take a mean:
+                        dims = globaldata.datasets[key].page[key].dims
+                        namesmean = list(dims)
+                        namesmean.remove('lev')
+                        idxmean = [dims.index(namemean) for namemean in namesmean]
+                        
+                        value = \
+                        globaldata.datasets[key].page[key].isel(time=itimes,
+                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+                        # Ideally, source should be equal to the datakey of globaldata.library 
+                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+                        #  but therefore the globaldata class requires a revision to make this work
+                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+
+                else:
+                    # this procedure is for reading the ground fields (2d space). 
+                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
+
+    
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+    
+                       # first, we browse to the correct file
+                       #print(key)
+                       globaldata.datasets[key].browse_page(time=classdatetime)
+    
+                    if globaldata.datasets[key].page is not None:
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - self.pars.latitude))
+                        ilat = np.where((DIST) == np.min(DIST))[0][0]
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - self.pars.longitude))
+                        ilon = np.where((DIST) == np.min(DIST))[0][0]
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - (self.pars.latitude + 0.5)))
+                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmax = ilat
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - (self.pars.longitude  + 0.5)))
+                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmax = ilon
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lat.values\
+                                - (self.pars.latitude - 0.5)))
+                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmin = ilat
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lon.values\
+                                - (self.pars.longitude  - 0.5)))
+                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmin = ilon        
+                        
+                        if ilatmin < ilatmax:
+                            ilatrange = range(ilatmin,ilatmax+1)
+                        else:
+                            ilatrange = range(ilatmax,ilatmin+1)
+                            
+                        if ilonmin < ilonmax:
+                            ilonrange = range(ilonmin,ilonmax+1)
+                        else:
+                            ilonrange = range(ilonmax,ilonmin+1)     
+                            
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                idatetime += 1
+                            
+                            classdatetimeend = np.datetime64(\
+                                                             self.pars.datetime +\
+                                                             dt.timedelta(seconds=self.pars.runtime)\
+                                                            ) 
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+                            if key in ['wg', 'w2']:
+                                idatetime = idatetime - 1
+                                idatetimeend = idatetimeend - 1
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['Tsoil','T2']:
+                                idatetimeend = idatetime
+                            
+                            idts = range(idatetime,idatetimeend+1)
+                            
+                            count = 0
+                            self.__dict__[key] = 0.
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    for iidts in idts:
+                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
+                                        count += 1
+                            value = value/count
+                            self.update(source='globaldata',pars={key:value.item()})
+                                
+                        else:
+                                
+                            count = 0
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
+                                    count += 1
+                            value = value/count                        
+
+                            self.update(source='globaldata',pars={key:value.item()})
+
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            self.logger.debug('also update LAI based on LAIpixel and cveg') 
+            # I suppose LAI pixel is already determined in the previous
+            # procedure. Anyway...
+            key = 'LAIpixel'
+
+            if globaldata.datasets[key].page is not None:
+                # first, we browse to the correct file that has the current time
+                if 'time' in list(globaldata.datasets[key].page[key].dims):
+                    globaldata.datasets[key].browse_page(time=classdatetime)
+            
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - self.pars.latitude))
+                ilat = np.where((DIST) == np.min(DIST))[0][0]
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - self.pars.longitude))
+                ilon = np.where((DIST) == np.min(DIST))[0][0]
+                 
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude + 0.5)))
+                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmax = ilat
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values \
+                        - (self.pars.longitude  + 0.5)))
+                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmax = ilon
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude - 0.5)))
+                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmin = ilat
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - (self.pars.longitude  - 0.5)))
+                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmin = ilon        
+                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                
+                
+                if ilatmin < ilatmax:
+                    ilatrange = range(ilatmin,ilatmax+1)
+                else:
+                    ilatrange = range(ilatmax,ilatmin+1)
+                    
+                if ilonmin < ilonmax:
+                    ilonrange = range(ilonmin,ilonmax+1)
+                else:
+                    ilonrange = range(ilonmax,ilonmin+1)           
+                
+                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
+                LAIpixel = 0.
+                count = 0
+                for iilat in [ilat]: #ilatrange
+                    for iilon in [ilon]: #ilonrange
+                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
+                        
+                                        
+                        # if np.isnan(tarray[idatetime]):
+                        #     print("interpolating GIMMS LAIpixel nan value")
+                        #     
+                        #     mask = np.isnan(tarray)
+                        #     
+                        #     #replace each nan value with a interpolated value
+                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+                        #         
+                        #     else:
+                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
+                    
+                        #         tarray *= np.nan 
+                        
+                        count += 1
+                        #tarray_res += tarray
+                LAIpixel = LAIpixel/count
+                
+                count = 0
+                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
+  
+                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
+                #print('LAIpixel:',self.__dict__['LAIpixel'])
+                #print('cveg:',self.__dict__['cveg'])
+                
+                # finally, we rescale the LAI according to the vegetation
+                # fraction
+                value = 0. 
+                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
+                   value =self.pars.LAIpixel/self.pars.cveg
+                else:
+                    # in case of small vegetation fraction, we take just a standard 
+                    # LAI value. It doesn't have a big influence anyway for
+                    # small vegetation
+                    value = 2.
+                #print('LAI:',self.__dict__['LAI'])
+                self.update(source='globaldata',pars={'LAI':value}) 
+
+
+        # in case we have 'sp', we also calculate the 3d pressure fields at
+        # full level and half level
+        if ('sp' in keys) and ('sp' in self.pars.__dict__):
+            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
+
+            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # hydrostatic thickness of each model layer
+            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
+            # # dz = rhodz/(R * T / pfull)
+
+
+            # # subsidence multiplied by density. We calculate the subsidence of
+            # # the in class itself
+            # wrho = np.zeros_like(phalf)
+            # wrho[-1] = 0. 
+
+            # for ihlev in range(0,wrho.shape[0]-1):
+            #     # subsidence multiplied by density is the integral of
+            #     # divergences multiplied by the layer thicknessies
+            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
+            #                     self.air_ac['divU_y'][ihlev:]) * \
+            #                    delpdgrav[ihlev:]).sum()
+
+
+            
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'p':list(pfull)}))
+            self.update(source='globaldata',\
+                        air_ach=pd.DataFrame({'p':list(phalf)}))
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
+            # self.update(source='globaldata',\
+            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
+
+    def check_source(self,source,check_only_sections=None):
+        """ this procedure checks whether data of a specified source is valid.
+
+        INPUT:
+            source: the data source we want to check
+            check_only_sections: a string or list with sections to be checked
+        OUTPUT:
+            returns True or False
+        """
+
+        # we set source ok to false as soon as we find a invalid input
+        source_ok = True
+
+        # convert to a single-item list in case of a string
+        check_only_sections_def = (([check_only_sections]) if \
+                                   type(check_only_sections) is str else \
+                                    check_only_sections)
+                                  
+        if source not in self.sources.keys():
+            self.logger.info('Source '+source+' does not exist')
+            source_ok = False
+
+        for sectiondatakey in self.sources[source]:                             
+            section,datakey = sectiondatakey.split(':')                         
+            if ((check_only_sections_def is None) or \
+                (section in check_only_sections_def)):                          
+                checkdatakeys = []
+                if type(self.__dict__[section]) is pd.DataFrame:
+                    checkdata = self.__dict__[section]
+                elif type(self.__dict__[section]) is model_input:
+                    checkdata = self.__dict__[section].__dict__
+
+                if (datakey not in checkdata):                              
+                    # self.logger.info('Expected key '+datakey+\
+                    #                  ' is not in parameter input')                        
+                    source_ok = False                                           
+                elif (checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True):                    
+        
+                    # self.logger.info('Key value of "'+datakey+\
+                    #                  '" is invalid: ('+ \
+                    # str(self.__dict__[section].__dict__[datakey])+')')         
+                    source_ok = False
+
+        return source_ok
+
+    def check_source_globaldata(self):
+        """ this procedure checks whether all global parameter data is
+        available, according to the keys in the self.sources"""
+
+        source_globaldata_ok = True
+
+        #self.get_values_air_input()
+
+        # and now we can get the surface values
+        #class_settings = class4gl_input()
+        #class_settings.set_air_input(input_atm)
+        
+        # we only allow non-polar stations
+        if not (self.pars.lat <= 60.):
+            source_globaldata_ok = False
+            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+        
+        # check lat and lon
+        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
+            source_globaldata_ok = False
+            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+        else:
+            # we only check the ground parameter data (pars section). The 
+            # profile data (air_ap section) are supposed to be valid in any 
+            # case.
+            source_ok = self.check_source(source='globaldata',\
+                                          check_only_sections=['air_ac',\
+                                                               'air_ap',\
+                                                               'pars'])
+            if not source_ok:
+                source_globaldata_ok = False
+        
+            # Additional check: we exclude desert-like
+            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
+                source_globaldata_ok = False
+                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
+                source_globaldata_ok = False
+                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+            elif self.pars.cveg < 0.02:
+                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                source_globaldata_ok = False
+
+        return source_globaldata_ok
+
+
+class c4gli_iterator():
+    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
+    
+        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
+    """
+    def __init__(self,file):
+        # take file as IO stream
+        self.file = file
+        self.yaml_generator = yaml.load_all(file)
+        self.current_dict = {}
+        self.current_class4gl_input = class4gl_input()
+        separator = self.file.readline() # this is just dummy
+        self.header = file.readline()
+        if self.header != '# CLASS4GL record; format version: 0.1\n':
+            raise NotImplementedError("Wrong format version: '"+self.header+"'")
+    def __iter__(self):
+        return self
+    def __next__(self):
+        self.current_dict = self.yaml_generator.__next__()
+        self.current_class4gl_input.load_yaml_dict(self.current_dict)
+        return self.current_class4gl_input
+
+
+
+#get_cape and lift_parcel are adapted from the SkewT package
+    
+class gl_dia(object):
+    def get_lifted_index(self,timestep=-1):
+        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
+    
+#from SkewT
+#def get_lcl(startp,startt,startdp,nsteps=101):
+#    from numpy import interp
+#    #--------------------------------------------------------------------
+#    # Lift a parcel dry adiabatically from startp to LCL.
+#    # Init temp is startt in K, Init dew point is stwrtdp,
+#    # pressure levels are in Pa    
+#    #--------------------------------------------------------------------
+#
+#    assert startdp<=startt
+#
+#    if startdp==startt:
+#        return np.array([startp]),np.array([startt]),np.array([startdp]),
+#
+#    # Pres=linspace(startp,60000.,nsteps)
+#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
+#
+#    # Lift the dry parcel
+#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
+#    # Mixing ratio isopleth
+#    starte=VaporPressure(startdp)
+#    startw=MixRatio(starte,startp)
+#    e=Pres*startw/(.622+startw)
+#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
+#
+#    # Solve for the intersection of these lines (LCL).
+#    # interp requires the x argument (argument 2)
+#    # to be ascending in order!
+#    P_lcl=interp(0.,T_iso-T_dry,Pres)
+#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
+#
+#    # # presdry=linspace(startp,P_lcl)
+#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
+#
+#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
+#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
+#
+#    return P_lcl,T_lcl
+
+
+
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
+
+
+
+#from class
+def get_lcl(startp,startt,startqv):
+        # Find lifting condensation level iteratively
+    lcl = 20.
+    RHlcl = 0.5
+    
+    itmax = 30
+    it = 0
+    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it= self.DT) and (self.DT.year == DT.year)):
             self.DT = DT
@@ -184,7 +184,7 @@ def find_next(self,get_atm=False):
        
 
 
-    def get_values_air_input(self):
+    def get_values_air_input(self,latitude=None,longitude=None):
 
         # for iDT,DT in enumerate(DTS):
         
@@ -208,6 +208,7 @@ def get_values_air_input(self):
         #PARAMS.insert(0,'date',DT)
 
         PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
         
         THTV = np.array(ONE_COLUMN.THTV,dtype='float')
         #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
@@ -232,6 +233,9 @@ def get_values_air_input(self):
         #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
 
         BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        BLHV = np.max((BLHV,10.))
+        BLHVu = np.max((BLHVu,10.))
+        BLHVd = np.max((BLHVd,10.))
         #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
 
         #security values for mixed-layer jump values dthetav, dtheta and dq
@@ -245,23 +249,42 @@ def get_values_air_input(self):
             ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
             
             listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTV','THTA','QABS']):
-                if len(np.where(HAGL <= BLH)[0]) >= 4:
-                    meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL <= BLH][2:-1],dtype=np.float))
+            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+                
+                # get index of lowest valid observation. This seems to vary
+                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+                if len(idxvalid) > 0:
+                    #print('idxvalid',idxvalid)
+                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+                    else:
+                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
                 else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][1:2],dtype=np.float)
+                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+                    #print(col,meanabl)
+               
+                
+                # if col == 'PRES':
+                #     meanabl =  
             
                 new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
                 #THTVM = np.nanmean(THTV[HAGL <= BLH])
                 #print("new_pro_h",new_pro_h)
                 # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV']:
+                if col in ['THTA','THTV',]:
                     #for moisture
                     #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
                     #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
                     if len(listHAGLNEW) > 4:
                         #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta = np.max((0.1,(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) )
+                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+                        dtheta = np.max((0.1,dtheta_pre))
+                        #meanabl = meanabl - (dtheta - dtheta_pre)
+                        #print('dtheta_pre',dtheta_pre)
+                        #print('dtheta',dtheta)
+                        #print('meanabl',meanabl)
+                        #stop
+                        
                     else:
                         dtheta = np.nan
                 else:
@@ -283,9 +306,18 @@ def get_values_air_input(self):
             
         # we just make a copy of the fields, so that it can be read correctly by CLASS 
         for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(ONE_COLUMNNEW[-1].columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            
+            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+        
+            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+
 
         # assign fields adopted by CLASS
         if self.mode == 'o': #original 
@@ -299,14 +331,56 @@ def get_values_air_input(self):
         else:
             PARAMS.insert(0,'h',   BLHV)
             
-        PARAMS.insert(0,'day', PARAMS['datetime'][0].day)
-        PARAMS.insert(0,'tstart', PARAMS['datetime'][0].hour)
-        PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-        PARAMS.insert(0,'lon', np.float(PARAMS['Station longitude'][0]))
-        PARAMS['ldatetime'] = PARAMS.datetime.value - dt.timedelta(hours=PARAMS.lon.value/360.*24.) 
-        PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-        PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-        PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+
+        try:
+            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+        except:
+            print("could not convert latitude coordinate")
+            PARAMS.insert(0,'latitude', np.nan)
+            PARAMS.insert(0,'lat', np.nan)
+        try:
+            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+            PARAMS.insert(0,'lon', 0.)
+        except:
+            print("could not convert longitude coordinate")
+            PARAMS.insert(0,'longitude', np.nan)
+            PARAMS.insert(0,'lon', 0.)
+
+        if latitude is not None:
+            print('overwriting latitude with specified value')
+            PARAMS['latitude'] = np.float(latitude)
+            PARAMS['lat'] = np.float(latitude)
+        if longitude is not None:
+            print('overwriting longitude with specified value')
+            PARAMS['longitude'] = np.float(longitude)
+        try:
+            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            # This is the nearest datetime when sun is up (for class)
+            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+            # apply the same time shift for UTC datetime
+            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+            
+        except:
+            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+
+        
+
+        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+        # as we are forcing lon equal to zero this is is expressed in local suntime
+        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+
            
         ONE_COLUMNb = ONE_COLUMNNEW[0]
         ONE_COLUMNu = ONE_COLUMNNEW[1]
@@ -325,8 +399,7 @@ def get_values_air_input(self):
 
         BLHVe = abs(BLHV - BLHVu)
         BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-        PARAMS.insert(0,'OK',((BLHVe < 100.) and (len(np.where(~pd.isnull(ONE_COLUMN['THTV'][ONE_COLUMN['HAGL'] > 5000.]))[0]) >0 )))
-        
+
         #PARAMS.insert(0,'dq',0.)
         
         PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
@@ -335,42 +408,66 @@ def get_values_air_input(self):
         #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
         
         if self.mode == 'o': #original 
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
-            
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
         elif self.mode == 'b': # best BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNb['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNb['THTA'])[2]-list(ONE_COLUMNb['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNb['QABS'])[2]-list(ONE_COLUMNb['QABS'])[1]))
-            
-        elif self.mode == 'u':# upper BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNu['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNu['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNu['THTA'])[2]-list(ONE_COLUMNu['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNu['QABS'])[2]-list(ONE_COLUMNu['QABS'])[1]))
-            
-        elif self.mode == 'd': # lower BLH
-            PARAMS.insert(0,'theta',np.float(list(ONE_COLUMNd['THTA'])[1]))
-            PARAMS.insert(0,'q',np.float(list(ONE_COLUMNd['QABS'])[1]))
-            PARAMS.insert(0,'dtheta',np.float(list(ONE_COLUMNd['THTA'])[2]-list(ONE_COLUMNd['THTA'])[1]))
-            PARAMS.insert(0,'dq',np.float(list(ONE_COLUMNd['QABS'])[2]-list(ONE_COLUMNd['QABS'])[1]))
-            
+            USE_ONECOLUMN = ONE_COLUMNb
+            BLCOLUMN = ONE_COLUMNb
+        elif self.mode == 'u': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNu
+            BLCOLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNd
+            BLCOLUMN = ONE_COLUMNd
+        else:
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb
+
+        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
+        # print(BLCOLUMN['HAGL'][lt6000])
+        # print(BLCOLUMN['HAGL'][lt2500])
+        # 
+        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+
+        #print(BLCOLUMN['HAGL'][lt2500])
+        PARAMS.insert(0,'OK',
+                      ((BLHVe < 200.) and 
+                       ( len(np.where(lt6000)[0]) > 5) and
+                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+                      )
+                     )
+
+        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+        
         
         PARAMS = PARAMS.T
 
         
         self.PARAMS = PARAMS
-        if self.mode == 'o': #original 
-            self.ONE_COLUMN = ONE_COLUMN
-        elif self.mode == 'b': # best BLH
-            self.ONE_COLUMN = ONE_COLUMNb
-        elif self.mode == 'u':# upper BLH
-            self.ONE_COLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # lower BLH
-            self.ONE_COLUMN=ONE_COLUMNd
-        else:
-            self.ONE_COLUMN = ONE_COLUMN
+        self.ONE_COLUMN = USE_ONECOLUMN
+        # if self.mode == 'o': #original 
+        #     self.ONE_COLUMN = ONE_COLUMN
+        # elif self.mode == 'b': # best BLH
+        #     self.ONE_COLUMN = ONE_COLUMNb
+        # elif self.mode == 'u':# upper BLH
+        #     self.ONE_COLUMN = ONE_COLUMNu
+        # elif self.mode == 'd': # lower BLH
+        #     self.ONE_COLUMN=ONE_COLUMNd
+        # else:
+        #     self.ONE_COLUMN = ONE_COLUMN
 
diff --git a/data_global.py b/data_global.py
new file mode 100644
index 0000000..9c3d9b5
--- /dev/null
+++ b/data_global.py
@@ -0,0 +1,936 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: Hendrik Wouters
+
+Purpose: provides class routines for ground and atmosphere conditions used for
+the CLASS miced-layer model
+
+Usage:
+    from data_global import data_global
+    from class4gl import class4gl_input
+    from data_soundings import wyoming
+
+    # create a data_global object and load initial data pages
+    globaldata = data_global()
+    globaldata.load_datasets()
+    # create a class4gl_input object
+    c4gli = class4gl_input()
+    # Initialize it with profile data. We need to do this first. Actually this
+    # will set the coordinate parameters (datetime, latitude, longitude) in
+    # class4gl_input.pars.__dict__, which is required to read point data from
+    # the data_global object.
+
+    # open a Wyoming stream for a specific station
+    wy_strm = wyoming(STNM=91376)
+    # load the first profile
+    wy_strm.find_first()
+    # load the profile data into the class4gl_input object
+    c4gli.get_profile_wyoming(wy_strm)
+    
+    # and finally, read the global input data for this profile
+    c4gli.get_global_input(globaldata)
+
+
+"""
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+#import pynacolada as pcd
+import pandas as pd
+import xarray as xr
+import os
+import glob
+import sys
+import errno
+import warnings
+import logging
+
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+class book(object):
+    """ this is a class for a dataset spread over multiple files. It has a
+    similar purpose  open_mfdataset, but only 1 file (called current 'page')
+    one is loaded at a time. This saves precious memory.  """
+    def __init__(self,fn,concat_dim = None,debug_level=None):
+        self.logger = logging.getLogger('book')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # filenames are expanded as a list and sorted by filename
+        self.pages = glob.glob(fn); self.pages.sort()
+        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
+        if len(self.pages) == 0:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
+        self.ipage = -1; self.page = None
+        self.renames = {} # each time when opening a file, a renaming should be done.
+        self.set_page(0)
+
+        # we consider that the outer dimension is the one we concatenate
+        self.concat_dim = concat_dim
+        if self.concat_dim is None:
+            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
+
+    # this wraps the xarray sel-commmand
+    def sel(*args, **kwargs):
+        for dim in kwargs.keys():
+            if dim == self.concat_dim:
+                self.browse_page(**{dim: kwargs[dim]})
+        return page.sel(*args,**kwargs)
+
+
+    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
+    #def __getattr__(self,attr):
+    #    orig_attr = self.page.__getattribute__(attr)
+    #    if callable(orig_attr):
+    #        def hooked(*args, **kwargs):
+    #            for dim in kwargs.keys():
+    #                if dim == self.concat_dim:
+    #                    self.browse_page(**{dim: kwargs[dim]})
+    #
+    #            result = orig_attr(*args, **kwargs)
+    #            # prevent wrapped_class from becoming unwrapped
+    #            if result == self.page:
+    #                return self
+    #            self.post()
+    #            return result
+    #        return hooked
+    #    else:
+    #        return orig_attr
+
+    def set_renames(self,renames):
+        #first, we convert back to original names, and afterwards, we apply the update of the renames.
+        reverse_renames = dict((v,k) for k,v in self.renames.items())
+        self.renames = renames
+        if self.page is not None:
+            self.page = self.page.rename(reverse_renames)
+            self.page = self.page.rename(self.renames)
+
+    def set_page(self,ipage,page=None):
+        """ this sets the right page according to ipage:
+                - We do not switch the page if we are already at the right one
+                - we set the correct renamings (level -> lev, latitude -> lat,
+                etc.)
+                - The dataset is also squeezed.
+        """
+
+        if ((ipage != self.ipage) or (page is not None)):
+
+            if self.page is not None:
+                self.page.close()
+
+            self.ipage = ipage
+            if page is not None:
+                self.page = page
+            else:
+                if self.ipage == -1:
+                   self.page = None
+                else:
+                    #try:
+
+                    self.logger.info("Switching to page "+str(self.ipage)+': '\
+                                     +self.pages[self.ipage])
+                    self.page = xr.open_dataset(self.pages[self.ipage])
+
+
+            # do some final corrections to the dataset to make them uniform
+            if self.page is not None:
+               if 'latitude' in self.page.dims:
+#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
+               if 'level' in self.page.dims:
+                   self.page = self.page.rename({'level':'lev'})
+
+               self.page = self.page.rename(self.renames)
+               self.page = self.page.squeeze(drop=True)
+
+    def browse_page(self,rewind=2,**args):
+
+        # at the moment, this is only tested with files that are stacked according to the time dimension.
+        dims = args.keys()
+
+
+        if self.ipage == -1:
+            self.set_page(0)
+
+        found = False
+        iipage = 0
+        startipage = self.ipage - rewind
+        while (iipage < len(self.pages)) and not found:
+            ipage = (iipage+startipage) % len(self.pages)
+            for dim in args.keys():
+                this_file = True
+
+                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
+                if 'dims' not in self.__dict__:
+                    self.dims = {}
+                if dim not in self.dims.keys():
+                    self.dims[dim] = [None]*len(self.pages)
+
+                if self.dims[dim][ipage] is None:
+                    self.logger.info('Loading coordinates of dimension "'+dim+\
+                                     '" of page "' +str(ipage)+'".')
+                    self.set_page(ipage)
+                    # print(ipage)
+                    # print(dim)
+                    # print(dim,self.page[dim].values)
+                    self.dims[dim][ipage] = self.page[dim].values
+
+                # determine current time range of the current page
+                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
+                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
+
+                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
+                    this_file = False
+
+            if this_file:
+                found = True
+                self.set_page(ipage)
+            else:
+
+                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
+                #    iipage = len(self.pages) # we stop searching
+
+                iipage += 1
+
+        if not found:
+            self.logger.info("Page not found. Setting to page -1")
+            #iipage = len(self.pages) # we stop searching further
+            self.set_page(-1)
+
+        if self.ipage != -1:
+            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
+        else:
+            self.logger.debug("I'm now at page "+ str(self.ipage))
+
+
+class data_global(object):
+    def __init__(self,sources= {
+        # # old gleam
+        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
+        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
+        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
+        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
+        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
+        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
+        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
+        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
+        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
+        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
+        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
+        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
+        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
+        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
+        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
+        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
+        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
+        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
+        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
+        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
+        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
+        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
+        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
+        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
+        },debug_level=None):
+        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
+        self.sources = sources
+        self.datarefs = {}
+        self.datasets = {}
+        self.datetime = dt.datetime(1981,1,1)
+
+        self.logger = logging.getLogger('data_global')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+        self.debug_level = debug_level
+
+        warnings.warn('omitting pressure field p and advection')
+
+    def in_library(self,fn):
+        if fn not in self.library.keys():
+            return False
+        else:
+            print("Warning: "+fn+" is already in the library.")
+            return True
+
+    def add_to_library(self,fn):
+        if not self.in_library(fn):
+            print("opening: "+fn)
+            self.library[fn] = \
+                book(fn,concat_dim='time',debug_level=self.debug_level)
+
+            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
+            #if 'latitude' in self.library[fn].variables:
+            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+
+    # default procedure for loading datasets into the globaldata library
+    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
+        if type(varssource) is str:
+            varssource = [varssource]
+        if type(varsdest) is str:
+            varsdest = [varsdest]
+
+        self.add_to_library(input_fn)
+
+        if varssource is None:
+            varssource = []
+            for var in self.sources[input_fn].variables:
+                avoid = \
+                ['lat','lon','latitude','longitude','time','lev','level']
+                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
+                    varssource.append(var)
+
+        if varsdest is None:
+            varsdest = varssource
+
+        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        for ivar,vardest in enumerate(varsdest):
+            varsource = varssource[ivar]
+            print('setting '+vardest+' as '+varsource+' from '+input_fn)
+
+            if vardest in self.datarefs.keys():
+                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
+            #self.add_to_library(fn,varsource,vardest)
+            if vardest != varsource:
+                libkey = input_fn+'.'+varsource+'.'+vardest
+                if libkey not in self.library.keys():
+                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
+                    self.library[libkey] = book(input_fn,\
+                                                debug_level=self.debug_level)
+                    self.library[libkey].set_renames({varsource: vardest})
+
+                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+            else:
+                self.datarefs[vardest] = input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+
+            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
+            #     print('Warning: '+ vardest "not in " + input_fn)
+
+
+
+    def load_datasets(self,sources = None,recalc=0):
+
+        if sources is None:
+            sources = self.sources
+        for key in sources.keys():
+            #datakey,vardest,*args = key.split(':')
+            datakey,vardest = key.split(':')
+            #print(datakey)
+
+            fnvarsource = sources[key].split(':')
+            if len(fnvarsource) > 2:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource,fnargs = fnvarsource
+                fnargs = [fnargs]
+            elif len(fnvarsource) > 1:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource = fnvarsource
+                fnargs = []
+            else:
+                fn = sources[key]
+                varsource = vardest
+            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
+
+    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
+            # the default way of loading a 2d dataset
+            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
+                self.load_dataset_default(fn,varsource,vardest)
+            elif datakey == 'IGBPDIS':
+                if vardest == 'alpha':
+                    ltypes = ['W','B','H','TC']
+                    for ltype in ltypes:
+                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
+                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
+
+
+                    # landfr = {}
+                    # for ltype in ['W','B','H','TC']:
+                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
+
+
+
+                    keytemp = 'alpha'
+                    fnkeytemp = fn+':IGBPDIS:alpha'
+                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
+                        self.library[fnkeytemp]  = book(fnkeytemp,
+                                                        debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+                    else:
+                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
+                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
+                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
+                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
+                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
+                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
+                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
+
+                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+
+                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
+                        for ltype in ltypes:
+                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
+
+                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
+                        print('writing file to: '+fnkeytemp)
+                        os.system('rm '+fnkeytemp)
+                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
+                        self.library[fnkeytemp].close()
+
+
+                        self.library[fnkeytemp]  = \
+                            book(fnkeytemp,debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+
+
+                else:
+                    self.load_dataset_default(fn,varsource,vardest)
+
+
+            elif datakey == 'GLAS':
+                self.load_dataset_default(fn,varsource,vardest)
+                if vardest == 'z0m':
+                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
+                elif vardest == 'z0h':
+                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
+            elif datakey == 'DSMW':
+
+
+                # Procedure of the thermal properties:
+                # 1. determine soil texture from DSMW/10.
+                # 2. soil type with look-up table (according to DWD/EXTPAR)
+                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
+                #    with parameter look-up table from Noilhan and Planton (1989).
+                #    Note: The look-up table is inspired on DWD/COSMO
+
+                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
+
+
+
+                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
+                self.load_dataset_default(fn,'DSMW')
+                print('calculating texture')
+                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
+                TEMP  = {}
+                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
+                TEMP3 = {}
+                for SPKEY in SPKEYS:
+
+
+                    keytemp = SPKEY+'_values'
+                    fnoutkeytemp = fnout+':DSMW:'+keytemp
+                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                    else:
+                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
+                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
+                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+                        # for faster computation, we need to get it to memory out of Dask.
+                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
+                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
+
+                # yes, I know I only check the last file.
+                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
+                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
+                        print('idx',idx,SPKEY)
+                        SEL = (TEMP2 == idx)
+                    #     print(idx,len(TEMP3))
+                        for SPKEY in SPKEYS:
+                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
+
+                    for SPKEY in SPKEYS:
+                        keytemp = SPKEY+'_values'
+                        fnoutkeytemp = fnout+':DSMW:'+keytemp
+                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
+                        os.system('rm '+fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].close()
+
+
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                keytemp = 'texture'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+                else:
+                    self.library[fn+':DSMW:texture'] = xr.Dataset()
+                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
+                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
+                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
+                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+
+                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
+
+                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
+                    zundef[zundef < 0] = np.nan
+                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
+                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
+
+                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+
+
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+                print('calculating texture type')
+
+
+
+                keytemp = 'itex'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+                else:
+                    self.library[fnoutkeytemp] = xr.Dataset()
+                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+                    X = self.datasets['texture'].page['texture'].values*100
+                    X[pd.isnull(X)] = -9
+
+
+                    self.datasets[keytemp][keytemp].values = X
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
+                    self.datasets['itex'].close()
+
+
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+
+                keytemp = 'isoil'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                isoil_reprocessed = False
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+                else:
+                    isoil_reprocessed = True
+                    print('calculating soil type')
+                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    ITEX = self.datasets['itex'].page['itex'].values
+                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
+                    LOOKUP = [
+                              [-10 ,9],# ocean
+                              [0 ,7],# fine textured, clay (soil type 7)
+                              [20,6],# medium to fine textured, loamy clay (soil type 6)
+                              [40,5],# medium textured, loam (soil type 5)
+                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                              [80,3],# coarse textured, sand (soil type 3)
+                              [100,9],# coarse textured, sand (soil type 3)
+                            ]
+                    for iitex,iisoil in LOOKUP:
+                        ISOIL[ITEX > iitex] = iisoil
+                        print('iitex,iisoil',iitex,iisoil)
+
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    LOOKUP = [
+                              [9001, 1 ], # ice, glacier (soil type 1)
+                              [9002, 2 ], # rock, lithosols (soil type 2)
+                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                              [9,    9 ], # undefined (ocean)
+                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                              [9000, 9 ], # undefined (inland lake)
+                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                            ]
+                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
+
+                    CODE_VALUES[ITEX == 901200] = 9012
+                    for icode,iisoil in LOOKUP:
+                        ISOIL[CODE_VALUES == icode] = iisoil
+
+                    self.datasets['isoil']['isoil'].values = ISOIL
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+                    print('saved inbetween file to: '+fnoutkeytemp)
+
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                #adopted from data_soil.f90 (COSMO5.0)
+                SP_LOOKUP = {
+                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
+                  # (by index)                                           loam                    loam                                water      ice
+                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+                  # Important note: For peat, the unknown values below are set equal to that of loam
+                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
+                  #error in table 2 of NP89: values need to be multiplied by e-6
+                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
+
+                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
+                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
+                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
+                }
+
+
+                # isoil_reprocessed = False
+                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+
+                #     self.library[fn+':DSMW:isoil'] = \
+                #             book(fnoutkeytemp,debug_level=self.debug_level)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+                # else:
+                #     isoil_reprocessed = True
+                #     print('calculating soil type')
+                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+
+
+
+                # this should become cleaner in future but let's hard code it for now.
+                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
+                print('calculating soil parameter')
+                DATATEMPSPKEY = {}
+                if (recalc < 1) and (isoil_reprocessed == False): 
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        keytemp = SPKEY
+                        fnoutkeytemp=fnout+':DSMW:'+keytemp
+                        self.library[fn+':DSMW:'+SPKEY] =\
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
+                        self.datarefs[SPKEY] =fnoutkeytemp
+                else:
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+
+                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
+                    ISOIL = self.datasets['isoil'].page['isoil'].values
+                    print(np.where(ISOIL>0.))
+                    for i in range(11):
+                        SELECT = (ISOIL == i)
+                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
+
+                        os.system('rm '+fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].close()
+                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
+
+                        self.library[fn+':DSMW:'+SPKEY] = \
+                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+
+
+            else:
+                self.load_dataset_default(fn,varsource,vardest)
+
+
+
+
+
+
+#
+#                 # only print the last parameter value in the plot
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'cala'
+#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'crhoc'
+#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#     key = "CERES"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         CERES_start_date = dt.datetime(2000,3,1)
+#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+#
+#         var = 'cc'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#         print(class_settings.lat,class_settings.lon)
+#
+#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
+#
+#         input_nc.close()
+#
+
+
+#     key = "GIMMS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+#         print("Reading Leag Area Index from "+input_fn)
+#         var = 'LAI'
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+#
+#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+#
+#         if np.isnan(tarray[idatetime]):
+#             print("interpolating GIMMS cveg nan value")
+#
+#             mask = np.isnan(tarray)
+#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+#             else:
+#                 print("Warning. Could not interpolate GIMMS cveg nan value")
+#
+#         class_settings.__dict__[var] = tarray[idatetime]
+#
+#         input_nc.close()
+#
+#     key = "IGBPDIS_ALPHA"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         var = 'alpha'
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+#         print("Reading albedo from "+input_fn)
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#
+#         landfr = {}
+#         for ltype in ['W','B','H','TC']:
+#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+#
+#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+#
+#         alpha=0.
+#         for ltype in landfr.keys():
+#             alpha += landfr[ltype]*aweights[ltype]
+#
+#
+#         class_settings.__dict__[var] = alpha
+#         input_nc.close()
+#
+#
+#     key = "ERAINT_ST"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         print("Reading soil temperature from "+input_fn)
+#
+#         var = 'Tsoil'
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         var = 'T2'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+#
+#
+#         input_nc.close()
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #var = 'T2'
+#     #valold = class_settings.__dict__[var]
+#     #
+#     #class_settings.__dict__[var] = 305.
+#     #class_settings.__dict__['Tsoil'] = 302.
+#     #valnew = class_settings.__dict__[var]
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #
+#     #var = 'Lambda'
+#     #valold = class_settings.__dict__[var]
+#
+#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
+#     ## I need to ask Chiel.
+#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+#     #
+#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
+#     #class_settings.__dict__[var] = valnew
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     key = "GLAS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+#         print("Reading canopy height for determining roughness length from "+input_fn)
+#         var = 'z0m'
+#
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+#
+#         lowerlimit = 0.01
+#         if testval < lowerlimit:
+#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+#             class_settings.__dict__[var] = lowerlimit
+#         else:
+#             class_settings.__dict__[var] = testval
+#
+#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+#
+#
+#         input_nc.close()
+
+
+
+
+
diff --git a/interface_functions.py b/interface_functions.py
new file mode 100644
index 0000000..3e483f3
--- /dev/null
+++ b/interface_functions.py
@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+#from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+#'_afternoon.yaml'
+def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+    filename = yaml_file.name
+    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+    #yaml_file = open(filename)
+
+    #print('going to next observation',filename)
+    yaml_file.seek(index_start)
+
+    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+
+    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer.write(buf)
+    filebuffer.close()
+    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+
+    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    print(command)
+    os.system(command)
+    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    record_dict = json.load(jsonstream)
+    jsonstream.close()
+    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+
+
+    if mode =='mod':
+        modelout = class4gl()
+        modelout.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        return modelout
+    elif mode == 'ini':
+
+ 
+        # datetimes are incorrectly converted to strings. We need to convert them
+        # again to datetimes
+        for key,value in record_dict['pars'].items():
+            # we don't want the key with columns that have none values
+            if value is not None: 
+                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+               # elif (type(value) == str):
+                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
+            if (value == 0.9e19) or (value == '.9e19'):
+                record_dict['pars'][key] = np.nan
+        for key in record_dict.keys():
+            #print(key)
+            if key in ['air_ap','air_balloon',]:
+                #NNprint('check')
+                for datakey,datavalue in record_dict[key].items():
+                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+
+        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        c4gli = class4gl_input()
+        print(c4gli.logger,'hello')
+        c4gli.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+
+class stations(object):
+    def __init__(self,path,suffix='ini',refetch_stations=False):
+
+        self.path = path
+
+        self.file = self.path+'/stations_list.csv'
+        if (os.path.isfile(self.file)) and (not refetch_stations):
+            self.table = pd.read_csv(self.file)
+        else:
+            self.table = self.get_stations(suffix=suffix)
+            self.table.to_csv(self.file)
+        
+        self.table = self.table.set_index('STNID')
+        #print(self.table)
+
+    def get_stations(self,suffix):
+        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        if len(stations_list_files) == 0:
+            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        stations_list_files.sort()
+        print(stations_list_files)
+        if len(stations_list_files) == 0:
+            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
+        stations_list = []
+        for stations_list_file in stations_list_files:
+            thisfile = open(stations_list_file,'r')
+            yamlgen = yaml.load_all(thisfile)
+            try:
+                first_record  = yamlgen.__next__()
+            except:
+                first_record = None
+            if first_record is not None:
+                stations_list.append({})
+                for column in ['STNID','latitude','longitude']:
+                    #print(first_record['pars'].keys())
+                    stations_list[-1][column] = first_record['pars'][column]
+                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
+            yamlgen.close()
+            thisfile.close()
+    
+        print(stations_list)
+        return pd.DataFrame(stations_list)
+
+class stations_iterator(object):
+    def __init__(self,stations):
+        self.stations = stations
+        self.ix = -1 
+    def __iter__(self):
+        return self
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.stations.table)) 
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_row(self,row):
+        self.ix = row
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_STNID(self,STNID):
+        self.ix = np.where((self.stations.table.index == STNID))[0][0]
+        print(self.ix)
+        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+    def close():
+        del(self.ix)
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.records))
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+# #'_afternoon.yaml'
+# def get_record_yaml(yaml_file,index_start,index_end):
+#     filename = yaml_file.name
+#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+#     #yaml_file = open(filename)
+# 
+#     #print('going to next observation',filename)
+#     yaml_file.seek(index_start)
+# 
+#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+# 
+#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+#     filebuffer.write(buf)
+#     filebuffer.close()
+#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+#     
+#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+# 
+#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+#     print(command)
+#     os.system(command)
+#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
+#     record_dict = json.load(jsonstream)
+#     jsonstream.close()
+#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+#  
+#     # datetimes are incorrectly converted to strings. We need to convert them
+#     # again to datetimes
+#     for key,value in record_dict['pars'].items():
+#         # we don't want the key with columns that have none values
+#         if value is not None: 
+#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
+#            # elif (type(value) == str):
+#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+#                 
+#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
+# 
+#         if (value == 0.9e19) or (value == '.9e19'):
+#             record_dict['pars'][key] = np.nan
+#     for key in record_dict.keys():
+#         print(key)
+#         if key in ['air_ap','air_balloon',]:
+#             print('check')
+#             for datakey,datavalue in record_dict[key].items():
+#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+# 
+#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+# 
+#     c4gli = class4gl_input()
+#     c4gli.load_yaml_dict(record_dict)
+#     return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
+
+    records = pd.DataFrame()
+    for STNID,station in stations.iterrows():
+        dictfnchunks = []
+        if getchunk is 'all':
+
+            # we try the old single-chunk filename format first (usually for
+            # original profile pairs)
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(fn):
+                chunk = 0
+                dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+            # otherwise, we use the new multi-chunk filename format
+            else:
+                chunk = 0
+                end_of_chunks = False
+                while not end_of_chunks:
+                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(fn):
+                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    else:
+                        end_of_chunks = True
+                    chunk += 1
+
+            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
+            # yamlfilenames = glob.glob(globyamlfilenames)
+            # yamlfilenames.sort()
+        else:
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
+            
+        if len(dictfnchunks) > 0:
+            for dictfnchunk in dictfnchunks:
+                yamlfilename = dictfnchunk['fn']
+                chunk = dictfnchunk['chunk']
+                print(chunk)
+
+                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                generate_pkl = False
+                if not os.path.isfile(pklfilename): 
+                    print('pkl file does not exist. I generate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                elif not (os.path.getmtime(yamlfilename) <  \
+                    os.path.getmtime(pklfilename)):
+                    print('pkl file older than yaml file, so I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+
+                if refetch_records:
+                    print('refetch_records flag is True. I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                if not generate_pkl:
+                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                   # irecord = 0
+                else:
+                    with open(yamlfilename) as yaml_file:
+
+                        dictout = {}
+
+                        next_record_found = False
+                        end_of_file = False
+                        while (not next_record_found) and (not end_of_file):
+                            linebuffer = yaml_file.readline()
+                            next_record_found = (linebuffer == '---\n')
+                            end_of_file = (linebuffer == '')
+                        next_tell = yaml_file.tell()
+                        
+                        while not end_of_file:
+
+                            print(' next record:',next_tell)
+                            current_tell = next_tell
+                            next_record_found = False
+                            yaml_file.seek(current_tell)
+                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            linebuffer = ''
+                            while ( (not next_record_found) and (not end_of_file)):
+                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                linebuffer = yaml_file.readline()
+                                next_record_found = (linebuffer == '---\n')
+                                end_of_file = (linebuffer == '')
+                            filebuffer.close()
+                            
+                            next_tell = yaml_file.tell()
+                            index_start = current_tell
+                            index_end = next_tell
+
+                            
+                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            print(command)
+                            
+                            os.system(command)
+                            #jsonoutput = subprocess.check_output(command,shell=True) 
+                            #print(jsonoutput)
+                            #jsonstream = io.StringIO(jsonoutput)
+                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            record = json.load(jsonstream)
+                            dictouttemp = {}
+                            for key,value in record['pars'].items():
+                                # we don't want the key with columns that have none values
+                                if value is not None: 
+                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                   if (type(value) in regular_numeric_types):
+                                        dictouttemp[key] = value
+                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                            recordindex = record['index']
+                            dictouttemp['chunk'] = chunk
+                            dictouttemp['index_start'] = index_start
+                            dictouttemp['index_end'] = index_end
+                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            for key,value in dictouttemp.items():
+                                if key not in dictout.keys():
+                                    dictout[key] = {}
+                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                            print(' obs record registered')
+                            jsonstream.close()
+                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                    records_station = pd.DataFrame.from_dict(dictout)
+                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
+                    print('writing table file ('+pklfilename+') for station '\
+                          +str(STNID))
+                    records_station.to_pickle(pklfilename)
+                    # else:
+                    #     os.system('rm '+pklfilename)
+                    records = pd.concat([records,records_station])
+    return records
+
+def stdrel(mod,obs,columns):
+    stdrel = pd.DataFrame(columns = columns)
+    for column in columns:
+        stdrel[column] = \
+                (mod.groupby('STNID')[column].transform('mean') -
+                 obs.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') + \
+                (mod[column] -
+                 mod.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') 
+    return stdrel
+
+def pct(obs,columns):
+    pct = pd.DataFrame(columns=columns)
+    for column in columns:
+        #print(column)
+        pct[column] = ""
+        pct[column] = obs[column].rank(pct=True)
+    return pct
+
+def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (obs_afternoon.ldatetime - \
+                               obs_morning.ldatetime).dt.seconds*3600.
+    return stats
+
diff --git a/interface_multi.py b/interface_multi.py
new file mode 100644
index 0000000..83148e5
--- /dev/null
+++ b/interface_multi.py
@@ -0,0 +1,2061 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+# from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+cdictpres = {'blue': (\
+                   (0.,    0.,  0.),
+                   (0.25,  0.25, 0.25),
+                   (0.5,  .70, 0.70),
+                   (0.75, 1.0, 1.0),
+                   (1,     1.,  1.),
+                   ),
+       'green': (\
+                   (0. ,   0., 0.0),
+                   (0.25,  0.50, 0.50),
+                   (0.5,  .70, 0.70),
+                   (0.75,  0.50, 0.50),
+                   (1  ,    0,  0.),
+                   ),
+       'red':  (\
+                  (0 ,  1.0, 1.0),
+                  (0.25 ,  1.0, 1.0),
+                   (0.5,  .70, 0.70),
+                  (0.75 , 0.25, 0.25),
+                  (1,    0., 0.),
+                  )}
+
+statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+os.system('module load Ruby')
+
+class c4gl_interface_soundings(object):
+    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+        """ creates an interactive interface for analysing class4gl experiments
+
+        INPUT:
+            path_exp : path of the experiment output
+            path_obs : path of the observations 
+            globaldata: global data that is being shown on the map
+            refetch_stations: do we need to build the list of the stations again?
+        OUTPUT:
+            the procedure returns an interface object with interactive plots
+
+        """
+        
+        # set the ground
+        self.globaldata = globaldata
+
+ 
+        self.path_exp = path_exp
+        self.path_obs = path_obs
+        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
+
+        # # get the list of stations
+        # stationsfile = self.path_exp+'/stations_list.csv'
+        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
+        #     stations = pd.read_csv(stationsfile)
+        # else:
+        #     stations = get_stations(self.path_exp)
+        #     stations.to_csv(stationsfile)
+
+        # stations = stations.set_index('STNID')
+
+        self.frames = {}
+
+        self.frames['stats'] = {}
+        self.frames['worldmap'] = {}
+                
+        self.frames['profiles'] = {}
+        self.frames['profiles'] = {}
+        self.frames['profiles']['DT'] = None
+        self.frames['profiles']['STNID'] = None
+
+        #self.frames['worldmap']['stationsfile'] = stationsfile
+        self.frames['worldmap']['stations'] = stations(self.path_exp, \
+                                                       suffix='ini',\
+                                                       refetch_stations=refetch_stations)
+
+        # Initially, the stats frame inherets the values/iterators of
+        # worldmap
+        for key in self.frames['worldmap'].keys():
+            self.frames['stats'][key] = self.frames['worldmap'][key]
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_ini'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='ini',\
+                                           refetch_records=refetch_records
+                                           )
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_mod'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='mod',\
+                                           refetch_records=refetch_records
+                                           )
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_obs,\
+                                           subset='afternoon',\
+                                           refetch_records=refetch_records
+                                           )
+
+        self.frames['stats']['records_all_stations_mod'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['records_all_stations_ini']['dates'] = \
+            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+
+
+        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+
+        self.frames['stats']['records_all_stations_obs_afternoon'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['viewkeys'] = ['h','theta','q']
+        print('Calculating table statistics')
+        self.frames['stats']['records_all_stations_mod_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_mod'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+
+        self.frames['stats']['inputkeys'] = inputkeys
+        
+        # self.frames['stats']['inputkeys'] = \
+        #     [ key for key in \
+        #       self.globaldata.datasets.keys() \
+        #       if key in \
+        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
+
+
+        # get units from the class4gl units database
+        self.units = dict(units)
+        # for those that don't have a definition yet, we just ask a question
+        # mark
+        for var in self.frames['stats']['inputkeys']:
+            self.units[var] = '?'
+
+        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
+        self.frames['stats']['records_all_stations_ini_pct'] = \
+                  pct(self.frames['stats']['records_all_stations_ini'], \
+                      columns = self.frames['stats']['inputkeys'])
+
+        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
+        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+        #     mod['
+
+        # 
+        # 
+        # \
+        #        self.frames['stats']['records_all_stations_mod'], \
+
+
+
+        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        #               columns = [ 'd'+key+'dt' for key in \
+        #                           self.frames['stats']['viewkeys']], \
+        #              )
+
+        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
+        #               obs = self.frames['stats']['records_all_stations_ini'], \
+        #               columns = self.frames['stats']['viewkeys'], \
+        #              )
+        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+        
+        print('filtering pathological data')
+        # some observational sounding still seem problematic, which needs to be
+        # investigated. In the meantime, we filter them
+        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+        # we filter ALL data frames!!!
+        for key in self.frames['stats'].keys():
+            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+               (self.frames['stats'][key].index.names == indextype):
+                self.frames['stats'][key] = self.frames['stats'][key][valid]
+        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+
+
+        print("filtering stations from interface that have no records")
+        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
+            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                    == STNID).sum() == 0):
+                print("dropping", STNID)
+                self.frames['worldmap']['stations'].table = \
+                        self.frames['worldmap']['stations'].table.drop(STNID)
+                    
+        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+        
+        # TO TEST: should be removed, since it's is also done just below
+        self.frames['stats']['stations_iterator'] = \
+            self.frames['worldmap']['stations_iterator'] 
+
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
+        self.next_station()
+
+        # self.goto_datetime_worldmap(
+        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+        #     'after')
+    def sel_station(self,STNID=None,rownumber=None):
+
+        if (STNID is not None) and (rownumber is not None):
+            raise ValueError('Please provide either STNID or rownumber, not both.')
+
+        if (STNID is None) and (rownumber is None):
+            raise ValueError('Please provide either STNID or rownumber.')
+            
+        if STNID is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
+            print(
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+            )
+            self.update_station()
+        elif rownumber is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
+            self.update_station()
+
+
+
+    def next_station(self,event=None,jump=1):
+        with suppress(StopIteration):
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+                = self.frames['worldmap']['stations_iterator'].__next__(jump)
+            # self.frames['worldmap']['stations_iterator'].close()
+            # del(self.frames['worldmap']['stations_iterator'])
+            # self.frames['worldmap']['stations_iterator'] = \
+            #                 selfself.frames['worldmap']['stations'].iterrows()
+            # self.frames['worldmap']['STNID'],\
+            # self.frames['worldmap']['current_station'] \
+            #     = self.frames['worldmap']['stations_iterator'].__next__()
+
+        self.update_station()
+
+    def prev_station(self,event=None):
+        self.next_station(jump = -1,event=event)
+    def update_station(self):
+        for key in ['STNID','current_station','stations_iterator']: 
+            self.frames['stats'][key] = self.frames['worldmap'][key] 
+
+
+
+        # generate index of the current station
+        self.frames['stats']['records_current_station_index'] = \
+            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+             == \
+             self.frames['stats']['current_station'].name)
+
+        # create the value table of the records of the current station
+        tab_suffixes = \
+                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        for tab_suffix in tab_suffixes:
+            self.frames['stats']['records_current_station'+tab_suffix] = \
+                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+        # go to first record of current station
+        self.frames['stats']['records_iterator'] = \
+                        records_iterator(self.frames['stats']['records_current_station_mod'])
+        (self.frames['stats']['STNID'] , \
+        self.frames['stats']['current_record_chunk'] , \
+        self.frames['stats']['current_record_index']) , \
+        self.frames['stats']['current_record_mod'] = \
+                        self.frames['stats']['records_iterator'].__next__()
+
+        for key in self.frames['stats'].keys():
+            self.frames['profiles'][key] = self.frames['stats'][key]
+
+        STNID = self.frames['profiles']['STNID']
+        chunk = self.frames['profiles']['current_record_chunk']
+        if 'current_station_file_ini' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_ini'].close()
+        self.frames['profiles']['current_station_file_ini'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+        if 'current_station_file_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_mod'].close()
+        self.frames['profiles']['current_station_file_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_afternoon'].close()
+        self.frames['profiles']['current_station_file_afternoon'] = \
+            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+        self.frames['profiles']['records_iterator'] = \
+                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+        (self.frames['profiles']['STNID'] , \
+        self.frames['profiles']['current_record_chunk'] , \
+        self.frames['profiles']['current_record_index']) , \
+        self.frames['profiles']['current_record_mod'] = \
+                        self.frames['profiles']['records_iterator'].__next__()
+
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+
+        self.update_record()
+
+    def next_record(self,event=None,jump=1):
+        with suppress(StopIteration):
+            (self.frames['profiles']['STNID'] , \
+            self.frames['profiles']['current_record_chunk'] , \
+            self.frames['profiles']['current_record_index']) , \
+            self.frames['profiles']['current_record_mod'] = \
+                      self.frames['profiles']['records_iterator'].__next__(jump)
+        # except (StopIteration):
+        #     self.frames['profiles']['records_iterator'].close()
+        #     del( self.frames['profiles']['records_iterator'])
+        #     self.frames['profiles']['records_iterator'] = \
+        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #     (self.frames['profiles']['STNID'] , \
+        #     self.frames['profiles']['current_record_index']) , \
+        #     self.frames['profiles']['current_record_mod'] = \
+        #                     self.frames['profiles']['records_iterator'].__next__()
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        self.update_record()
+
+    def prev_record(self,event=None):
+        self.next_record(jump=-1,event=event)
+
+    def update_record(self):
+        self.frames['profiles']['current_record_ini'] =  \
+            self.frames['profiles']['records_current_station_ini'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'],\
+                  self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon'] =  \
+            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'] , \
+                  self.frames['profiles']['current_record_index'])]
+
+        self.frames['profiles']['current_record_mod_stats'] = \
+                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                    self.frames['profiles']['STNID'], \
+                    self.frames['profiles']['current_record_chunk'], \
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_ini_pct'] = \
+                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        # frame
+        # note that the current station, record is the same as the stats frame for initialization
+
+        # select first 
+        #self.frames['profiles']['current_record_index'], \
+        #self.frames['profiles']['record_yaml_mod'] = \
+        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
+        #                   self.frames['stats']['current_record_index'])
+        self.frames['profiles']['record_yaml_mod'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_mod'], \
+               self.frames['profiles']['current_record_mod'].index_start,
+               self.frames['profiles']['current_record_mod'].index_end,
+               mode='mod')
+                                
+        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_ini'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_ini'], \
+               record_ini.index_start,
+               record_ini.index_end,
+                mode='ini')
+
+        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_obs_afternoon'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_afternoon'], \
+               record_afternoon.index_start,
+               record_afternoon.index_end,
+                mode='ini')
+
+
+        key = self.frames['worldmap']['inputkey']
+        # only redraw the map if the current world map has a time
+        # dimension
+        if 'time' in self.globaldata.datasets[key].page[key].dims:
+            self.goto_datetime_worldmap(
+                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                'after')
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap',
+                                                  'profiles'])
+        else:
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap_stations',
+                                                  'profiles'])
+
+    def abline(self,slope, intercept,axis):
+        """Plot a line from slope and intercept"""
+        #axis = plt.gca()
+        x_vals = np.array(axis.get_xlim())
+        y_vals = intercept + slope * x_vals
+        axis.plot(x_vals, y_vals, 'k--')
+
+    def plot(self):
+        import pylab as pl
+        from matplotlib.widgets import Button
+        import matplotlib.pyplot as plt
+        import matplotlib as mpl
+        '''
+        Definition of the axes for the sounding table stats
+        '''
+        
+        fig = pl.figure(figsize=(14,9))
+        axes = {} #axes
+        btns = {} #buttons
+
+        # frames, which sets attributes for a group of axes, buttens, 
+        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            label = 'stats_'+str(key)
+            axes[label] = fig.add_subplot(\
+                            len(self.frames['stats']['viewkeys']),\
+                            5,\
+                            5*ikey+1,label=label)
+            # Actually, the axes should be a part of the frame!
+            #self.frames['stats']['axes'] = axes[
+
+            # pointer to the axes' point data
+            axes[label].data = {}
+
+            # pointer to the axes' color fields
+            axes[label].fields = {}
+
+
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
+
+        label ='stats_colorbar'
+        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
+        axes[label].fields = {}
+
+        from matplotlib.colors import LinearSegmentedColormap
+        cdictpres = {'blue': (\
+                           (0.,    0.,  0.),
+                           (0.25,  0.25, 0.25),
+                           (0.5,  .70, 0.70),
+                           (0.75, 1.0, 1.0),
+                           (1,     1.,  1.),
+                           ),
+               'green': (\
+                           (0. ,   0., 0.0),
+                           (0.25,  0.50, 0.50),
+                           (0.5,  .70, 0.70),
+                           (0.75,  0.50, 0.50),
+                           (1  ,    0,  0.),
+                           ),
+               'red':  (\
+                          (0 ,  1.0, 1.0),
+                          (0.25 ,  1.0, 1.0),
+                           (0.5,  .70, 0.70),
+                          (0.75 , 0.25, 0.25),
+                          (1,    0., 0.),
+                          )}
+        
+        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+        label = 'times'
+               
+        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+
+
+        label = 'worldmap'
+               
+        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+        axes[label].lat = None
+        axes[label].lon = None
+
+        label = 'worldmap_colorbar'
+        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+        axes[label].fields = {}
+
+        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
+        label = 'worldmap_stations'
+        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label].data = {}
+
+        fig.canvas.mpl_connect('pick_event', self.on_pick)
+        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
+
+
+        """ buttons definitions """
+        
+        label = 'bprev_dataset'
+        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous dataset')
+        btns[label].on_clicked(self.prev_dataset)
+
+        label = 'bnext_dataset'
+        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next dataset')
+        btns[label].on_clicked(self.next_dataset)
+
+        label = 'bprev_datetime'
+        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous datetime')
+        btns[label].on_clicked(self.prev_datetime)
+
+        label = 'bnext_datetime'
+        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next datetime')
+        btns[label].on_clicked(self.next_datetime)
+
+
+        label = 'bprev_station'
+        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous station')
+        btns[label].on_clicked(self.prev_station)
+
+        label = 'bnext_station'
+        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next station')
+        btns[label].on_clicked(self.next_station)
+
+        label = 'bprev_record'
+        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous record')
+        btns[label].on_clicked(self.prev_record)
+
+        label = 'bnext_record'
+        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next record')
+        btns[label].on_clicked(self.next_record)
+
+
+        # self.nstatsview = nstatsview
+        # self.statsviewcmap = statsviewcmap
+        self.fig = fig
+        self.axes = axes
+        self.btns = btns
+        self.tbox = {}
+        # self.hover_active = False
+
+        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
+        #                                transform=plt.gcf().transFigure)
+
+        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
+                                          transform=plt.gcf().transFigure)
+
+        label = 'air_ap:theta'
+        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
+
+        label = 'air_ap:q'
+        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
+
+        label = 'out:h'
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+
+        label = 'out:theta'
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+
+        label = 'out:q'
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+
+        label = 'SEB'
+        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
+
+
+        self.hover_active = False
+        self.fig = fig
+        self.fig.show()
+        self.fig.canvas.draw()
+        self.refresh_plot_interface()
+
+
+    # def scan_stations(self):
+    #     blabla
+        
+
+
+    # def get_records(current_file):
+    #     records = pd.DataFrame()
+
+    #     # initial position
+    #     next_record_found = False
+    #     while(not next_record_found):
+    #         next_record_found = (current_file.readline() == '---\n')
+    #     next_tell = current_file.tell() 
+    #     end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #     while not end_of_file:
+    #         current_tell = next_tell
+    #         next_record_found = False
+    #         current_file.seek(current_tell)
+    #         while ( (not next_record_found) and (not end_of_file)):
+    #             current_line = current_file.readline()
+    #             next_record_found = (currentline == '---\n')
+    #             end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #         # we store the position of the next record
+    #         next_tell = current_file.tell() 
+    #         
+    #         # we get the current record. Unfortunately we need to reset the
+    #         # yaml record generator first.
+    #         current_yamlgen.close()
+    #         current_yamlgen = yaml.load_all(current_file)
+    #         current_file.seek(current_tell)
+    #         current_record_mod = current_yamlgen.__next__()
+    #     current_yamlgen.close()
+
+    #     return records
+
+       #      next_record_found = False
+       #      while(not record):
+       #          next_record_found = (self.current_file.readline() == '---\n')
+       #      self.current_tell0 = self.current_file.tell() 
+
+       #  
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell0 = self.current_file.tell() 
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell1 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell0)
+       #  self.r0 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell1)
+       #  next_record_found = False
+       #  while ( (not next_record_found) and (not end_of_file):
+       #      current_line = self.current_file.readline()
+       #      next_record_found = (currentline == '---\n')
+       #      end_of_file = (currentline == '') # an empty line means we are at the end
+
+       #  self.current_tell2 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell1)
+       #  self.r1 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell2)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell3 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell2)
+       #  self.r2 = self.current_yamlgen.__next__()
+
+       #  # go to position of next record in file
+       #  self.current_file.seek(self.current_tell3)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell4 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell3)
+       #  self.r3 = self.current_yamlgen.__next__()
+ 
+       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
+
+    def goto_datetime_worldmap(self,DT,shift=None):
+        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                self.frames['worldmap']['iDT'] += 1
+            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                self.frames['worldmap']['iDT'] -= 1 
+            # for gleam, we take the values of the previous day
+            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                self.frames['worldmap']['iDT'] -= 2 
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+        #else:
+        #    self.frames['worldmap'].pop('DT')
+
+    def next_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def prev_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def next_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+
+
+    def sel_dataset(self,inputkey):
+        self.frames['worldmap']['inputkey'] = inputkey
+        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
+        self.goto_datetime_worldmap(
+            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+            'after')# get nearest datetime of the current dataset to the profile
+        if "fig" in self.__dict__.keys():
+            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+       
+    # def prev_station(self,event=None):
+    #     self.istation = (self.istation - 1) % self.stations.shape[0]
+    #     self.update_station()
+
+
+
+
+    #def update_datetime(self):
+    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
+    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
+    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
+    #        print(self.worldmapfocus['DT'])
+    #        self.refresh_plot_interface(only='worldmap')
+
+    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
+
+        #print('r1')
+        for argkey in args.keys():
+            self.__dict__[arg] = args[argkey]
+
+        axes = self.axes
+        tbox = self.tbox
+        frames = self.frames
+        fig = self.fig
+ 
+        if (only is None) or ('worldmap' in only):
+            globaldata = self.globaldata
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+            else:
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+            keystotranspose = ['lat','lon']
+            for key in dict(datasetxr.dims).keys():
+                if key not in keystotranspose:
+                    keystotranspose.append(key)
+
+            datasetxr = datasetxr.transpose(*keystotranspose)
+            datasetxr = datasetxr.sortby('lat',ascending=False)
+
+            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+            lonleft = lonleft - 360.
+            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+            label = 'worldmap'
+            axes[label].clear()
+            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+
+        if (only is None) or ('worldmap' in only):
+            #if 'axmap' not in self.__dict__ :
+            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
+            #else:
+
+            #stations = self.stations
+
+
+            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
+            #     resolution = 'l', 
+            # area_thresh = 0.1,
+            #     llcrnrlon=-180., llcrnrlat=-90.0,
+            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
+            # 
+            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
+            # self.gmap.drawcountries(color='white',linewidth=0.3)
+            # #self.gmap.fillcontinents(color = 'gray')
+            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
+            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
+            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
+            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # #self.ax5.shadedrelief()
+
+           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
+
+
+            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+
+            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
+            if 'lev' in field.dims:
+                field = field.isel(lev=-1)
+
+            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+            axes[label].axis('off')
+
+            from matplotlib import cm
+            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+            
+            
+            title=frames['worldmap']['inputkey']
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            axes[label].set_title(title)
+
+            label ='worldmap_colorbar'
+            axes[label].clear()
+            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+
+
+            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+            # x,y = self.gmap(lons,lats)
+            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+
+        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+
+            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            store_xlim = {}
+            store_ylim = {}
+            for ikey, key in enumerate(statskeys_out):
+                if (only is not None) and ('stats_lightupdate' in only):
+                    store_xlim[key] = axes['stats_'+key].get_xlim()
+                    store_ylim[key] = axes['stats_'+key].get_ylim()
+                self.axes['stats_'+key].clear()    
+
+            label = 'times'
+            self.axes[label].clear()
+
+            key = 'dthetadt'
+            x = self.frames['stats']['records_all_stations_ini']['datetime']
+            #print(x)
+            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            #print(y)
+            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            #print(z)
+
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            self.axes[label].data[label] = self.axes[label].scatter(x.values,
+                                                                    y.values,
+                                                                    c=z.values,
+                                                                    cmap=self.statsviewcmap,
+                                                                    s=2,
+                                                                    vmin=0.,
+                                                                    vmax=1.,
+                                                                    alpha=alpha_cloud_pixels)
+
+            
+            x = self.frames['stats']['records_current_station_ini']['datetime']
+            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+
+            x = self.frames['profiles']['records_current_station_ini']['datetime']
+            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
+            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
+
+            for ikey, key in enumerate(statskeys_out):
+
+                # show data of all stations
+                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                qvalmax = x.quantile(0.999)
+                qvalmin = x.quantile(0.001)
+                print('applying extra filter over extreme values for plotting stats')
+                selx = (x >= qvalmin) & (x < qvalmax)
+                sely = (x >= qvalmin) & (x < qvalmax)
+                x = x[selx & sely]
+                y = y[selx & sely]
+                z = z[selx & sely]
+                self.axes['stats_'+key].data['stats_'+key] = \
+                       self.axes['stats_'+key].scatter(x,y, c=z,\
+                                cmap=self.statsviewcmap,\
+                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
+
+                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
+
+                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
+                y = self.frames['stats']['current_record_mod_stats'][key]
+                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
+                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
+                    axes['stats_'+key].annotate(text, \
+                                               xy=(x,y),\
+                                               xytext=(0.05,0.05),\
+                                               textcoords='axes fraction',\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               color='white',\
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                # self.axes['stats_'+key].data[key+'_current_record'] = \
+                #        self.axes['stats_'+key].scatter(x,y, c=z,\
+                #                 cmap=self.statsviewcmap,\
+                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
+
+                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
+                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
+                # # highlight data for curent station
+                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+
+                if ikey == len(statskeys_out)-1:
+                    self.axes['stats_'+key].set_xlabel('external')
+                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
+                axes['stats_'+key].set_ylabel('model')
+
+
+                if (only is not None) and ('stats_lightupdate' in only):
+                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
+                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
+                else:
+                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
+                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
+                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
+                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
+                self.abline(1,0,axis=self.axes['stats_'+key])
+
+        if (only is None) or ('stats_colorbar' in only):
+            label ='stats_colorbar'
+            axes[label].clear()
+            import matplotlib as mpl
+            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
+            self.axes[label].fields[label] = \
+             mpl.colorbar.ColorbarBase(self.axes[label],\
+                        orientation='horizontal',\
+                        label="percentile of "+self.frames['worldmap']['inputkey'],
+                        alpha=1.,
+                                cmap=self.statsviewcmap,\
+                                       norm=norm
+                         )
+
+        #print('r1')
+        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
+            #print('r2')
+            label = 'worldmap_stations'
+            axes[label].clear()
+            
+            stations = self.frames['worldmap']['stations'].table
+            globaldata = self.globaldata
+            
+            key = label
+
+            #print('r3')
+            if (stations is not None):
+                xlist = []
+                ylist = []
+                #print('r4')
+                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
+            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    xlist.append(x)
+                    ylist.append(y)
+                #picker is needed to make it clickable (pick_event)
+                axes[label].data[label] = axes[label].scatter(xlist,ylist,
+                                                              c='r', s=15,
+                                                              picker = 15,
+                                                              label=key,
+                                                              edgecolor='k',
+                                                              linewidth=0.8)
+
+            # cb.set_label('Wilting point [kg kg-3]')
+                #print('r5')
+
+                
+            #     xseries = []
+            #     yseries = []
+            #     for iSTN,STN in stations.iterrows():
+            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
+            #         xseries.append(x)                    
+            #         yseries.append(y)
+            #         
+            #         
+            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
+                    
+                if ('current_station' in frames['worldmap']):
+                    #print('r5')
+                    STN = frames['stats']['current_station']
+                    STNID = frames['stats']['STNID']
+                    #print('r5')
+
+                    x,y = len(axes['worldmap'].lon)* \
+                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
+                          len(axes['worldmap'].lat)* \
+                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    #print('r6')
+                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
+                    #                          == \
+                    #                          self.frames['worldmap']['STNID'])\
+                    #                         & \
+                    #                         (self.seltablestats['DT'] \
+                    #                          == self.axes['statsview0].focus['DT']) \
+                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
+                    #print('r7')
+                    text = 'STNID: '+ format(STNID,'10.0f') + \
+                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
+                            ', LON: '+format(STN['longitude'],'3.3f')+ \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+
+                            #+', VAL: '+format(VAL,'.3e')
+
+                    axes[label].scatter(x,y, c='r', s=30,\
+                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
+                    #print('r8')
+            
+                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
+                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
+                    #colorstation = max((min((1.,colorstation)),0.))
+                    colorstation =0.2
+                    from matplotlib import cm
+                    axes[label].annotate(text,
+                                         xy=(x,y),
+                                         xytext=(0.05,0.05),
+                                         textcoords='axes fraction', 
+                                         bbox=dict(boxstyle="round",
+                                         fc = cm.viridis(colorstation)),
+                                         arrowprops=dict(arrowstyle="->",
+                                                         linewidth=1.1),
+                                         color='white' if colorstation < 0.5 else 'black')
+                    #print('r9')
+
+                    # #pos = sc.get_offsets()[ind["ind"][0]]
+                    # 
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label].data[label+'statannotate'].set_text(text)
+                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
+                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
+            #print('r9')
+            axes[label].axis('off')
+            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
+            axes[label].set_ylim((len(axes['worldmap'].lat),0))
+            #print('r10')
+
+        if (only is None) or ('profiles' in only): 
+            #print('r11')
+
+            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
+            # # self.update_station(goto_first_sounding=False)
+            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
+            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
+            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
+            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
+
+            label = 'air_ap:theta'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # +\
+                # ' -> '+ \
+                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+            
+            
+            
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+            #print('r12')
+
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
+            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
+            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            #print('r13')
+            # 
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r14')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+
+            #print('r15')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+                          
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r16')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r17')
+            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            print(hmax)
+            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            if valid_mod:
+
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="mod "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+
+            #print('r18')
+            axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('theta [K]')
+
+            label = 'air_ap:q'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+
+            #print('r19')
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            if valid_mod:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            else:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            # 
+            #print('r20')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r21')
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            #print('r23')
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r24')
+            if valid_mod:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="fit ")#+\
+                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #+'LT')
+            #print('r25')
+            #axes[label].legend()
+
+            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            #axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('q [kg/kg]')
+
+            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+
+            # #pl.subplots_adjust(right=0.6)
+
+            # label = 'q_pro'
+            # axes[label].clear()
+
+            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
+            # 
+            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
+            # 
+            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
+
+            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
+            # #pl.subplots_adjust(right=0.6)
+            # axes[label].set_xlabel('specific humidity [kg/kg]')
+ 
+
+            #print('r26')
+            time = self.frames['profiles']['record_yaml_mod'].out.time
+            for ilabel,label in enumerate(['h','theta','q']):
+                axes["out:"+label].clear()
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].set_ylabel(label)
+                if ilabel == 2:
+                    axes["out:"+label].set_xlabel('local sun time [h]')
+                
+            #print('r27')
+            label = 'SEB'
+            axes[label].clear()
+            
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
+            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
+            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
+                
+            #print('r28')
+            
+            axes[label].legend()
+            
+            #         for ax in self.fig_timeseries_axes:
+#             ax.clear()
+#         
+#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
+#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
+#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
+#         #print(self.morning_sounding.c4gl.out.Swin)
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
+#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
+#         self.fig_timeseries_axes[3].legend()
+#         self.fig.canvas.draw()
+            
+
+
+
+
+
+
+        #self.ready()
+        #print('r29')
+        fig.canvas.draw()
+        #fig.show()
+
+        self.axes = axes
+        self.tbox = tbox
+        self.fig = fig
+
+    def on_pick(self,event):
+        #print("HELLO")
+        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
+        #self.axes['theta_pro'].clear()
+        #self.axes['q_pro'].clear()
+        
+
+        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
+        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
+        keys_to_axes = {}
+        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
+
+        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
+        keys_to_axes['worldmap'] = 'worldmap'
+        
+        axes = self.axes
+        #nstatsview = self.nstatsview
+        #statsviewcmap = self.statsviewcmap
+        stations = self.frames['worldmap']['stations'].table
+
+
+        #print("p1")
+        current = event
+        artist = event.artist
+        
+        selkey = artist.get_label()
+        
+        #print(keys_to_axes)
+        
+        label = keys_to_axes[selkey]
+        #print("HELLO",selkey,label)
+
+        # # Get to know in which axes we are
+        # label = None
+        # for axeskey in axes.keys():
+        #     if event.inaxes == axes[axeskey]:
+        #         label = axeskey
+        #         
+
+        # cont, pos = None, None
+        
+        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
+        ind = event.ind
+        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
+        d = axes[label].collections[0]
+        #d.set_offset_position('data')
+        xy = d.get_offsets()
+        x, y =  xy[:,0],xy[:,1]
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+
+        #print("p2")
+        if len(ind) > 0:
+            #print("p3")
+            pos = x[ind[0]], y[ind[0]]
+
+            #if label[:-1] == 'statsview':
+            #    #seltablestatsstdrel = self.seltablestatsstdrel
+            #    #seltablestatspct = self.seltablestatspct
+
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    
+            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
+            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+            #    
+            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
+            #el
+            if (label == 'worldmap') or (label == 'worldmap_stations'):
+                self.hover_active = False
+                if (self.frames['worldmap']['STNID'] !=
+                    self.frames['profiles']['STNID']):
+                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
+                # so we just need to perform update_station
+                    self.update_station()
+            elif (label[:5] == 'stats'):
+
+                self.hover_active = False
+                if (self.frames['stats']['STNID'] !=
+                self.frames['profiles']['STNID']) or \
+                   (self.frames['stats']['current_record_chunk'] != 
+                    self.frames['profiles']['current_record_chunk']) or \
+                   (self.frames['stats']['current_record_index'] != 
+                    self.frames['profiles']['current_record_index']):
+
+
+
+                    for key in ['STNID','current_station','stations_iterator']: 
+                        self.frames['worldmap'][key] = self.frames['stats'][key] 
+
+                    for key in self.frames['stats'].keys():
+                        self.frames['profiles'][key] = self.frames['stats'][key]
+
+                    STNID = self.frames['profiles']['STNID']
+                    chunk = self.frames['profiles']['current_record_chunk']
+                    if 'current_station_file_ini' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_ini'].close()
+                    self.frames['profiles']['current_station_file_ini'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+                    if 'current_station_file_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_mod'].close()
+                    self.frames['profiles']['current_station_file_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_afternoon'].close()
+                    self.frames['profiles']['current_station_file_afternoon'] = \
+                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+                    # go to hovered record of current station
+                    self.frames['profiles']['records_iterator'] = \
+                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # ... and go to the record of the profile window (last one that
+                    # was picked by the user)
+                    found = False
+                    EOF = False
+                    while (not found) and (not EOF):
+                        try:
+                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
+                            #print("hello*")
+                            #print(self.frames['profiles']['current_record_index'])
+                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
+                               (index == self.frames['profiles']['current_record_index']) and \
+                               (STNID == self.frames['profiles']['STNID']):
+                                #print('found!')
+                                found = True
+                        except StopIteration:
+                            EOF = True
+                    if found:
+                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_chunk'] = chunk
+                        self.frames['stats']['current_record_index'] = index
+                    # # for the profiles we make a distinct record iterator, so that the
+                    # # stats iterator can move independently
+                    # self.frames['profiles']['records_iterator'] = \
+                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # (self.frames['profiles']['STNID'] , \
+                    # self.frames['profiles']['current_record_index']) , \
+                    # self.frames['profiles']['current_record_mod'] = \
+                    #                 self.frames['profiles']['records_iterator'].__next__()
+
+
+                    # for the profiles we make a distinct record iterator, so that the
+                    # stats iterator can move independently
+
+                    self.update_record()
+
+
+
+    def on_plot_hover(self,event):
+        axes = self.axes
+        #print('h1')
+
+        # Get to know in which axes we are
+        label = None
+        for axeskey in axes.keys():
+            if event.inaxes == axes[axeskey]:
+                label = axeskey
+                
+        #print('h2')
+
+        cont, pos = None, None
+        #print (label)
+        
+        if label is not None:
+            if  ('data' in axes[label].__dict__.keys()) and \
+                (label in axes[label].data.keys()) and \
+                (axes[label].data[label] is not None):
+                
+                #print('h3')
+                cont, ind =  axes[label].data[label].contains(event)
+                selkey = axes[label].data[label].get_label()
+                if len(ind["ind"]) > 0:
+                    #print('h4')
+                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
+                    #print('pos',pos,selkey)
+
+
+                    #if label[:-1] == 'statsview':
+                    #    seltablestatsstdrel = self.seltablestatsstdrel
+                    #    seltablestatspct = self.seltablestatspct
+
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
+                    #    self.hover_active = True
+                    #    
+                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
+                    #    
+                    #el
+                    #print(label[:5])
+                    if (label[:5] == 'stats') or (label == 'times'):
+                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        
+
+                        if label[:5] == 'stats':
+                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            (self.frames['stats']['STNID'] ,
+                             self.frames['stats']['current_record_chunk'], 
+                             self.frames['stats']['current_record_index']) = \
+                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        # elif label[:5] == 'stats':
+                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                        #     (self.frames['stats']['STNID'] ,
+                        #      self.frames['stats']['current_record_chunk'], 
+                        #      self.frames['stats']['current_record_index']) = \
+                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+
+
+                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+                        
+                        # # TO TEST: should be removed, since it's is also done just below
+                        # self.frames['stats']['stations_iterator'] = \
+                        #     self.frames['worldmap']['stations_iterator'] 
+                
+                
+                        # self.goto_datetime_worldmap(
+                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+                        #     'after')
+
+
+                        # scrolling to the right station
+                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                        EOF = False
+                        found = False
+                        while (not found and not EOF):
+                            if (STNID == self.frames['stats']['STNID']):
+                                   found = True 
+                            if not found:
+                                try:
+                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                                except (StopIteration):
+                                    EOF = True
+                        if found:
+                        #    self.frames['stats']['STNID'] = STNID
+                            self.frames['stats']['current_station'] =  station
+
+                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
+                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
+
+
+                        # generate index of the current station
+                        self.frames['stats']['records_current_station_index'] = \
+                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                             == self.frames['stats']['STNID'])
+
+
+                        tab_suffixes = \
+                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            self.frames['stats']['records_current_station'+tab_suffix] = \
+                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+
+                        # go to hovered record of current station
+                        self.frames['stats']['records_iterator'] = \
+                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+
+
+                        # ... and go to the record of the profile window (last one that
+                        # was picked by the user)
+                        found = False
+                        EOF = False
+                        while (not found) and (not EOF):
+                            try:
+                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                                #print("hello*")
+                                #print(self.frames['profiles']['current_record_index'])
+                                if (index == self.frames['stats']['current_record_index']) and \
+                                   (chunk == self.frames['stats']['current_record_chunk']) and \
+                                   (STNID == self.frames['stats']['STNID']):
+                                    #print('found!')
+                                    found = True
+                            except StopIteration:
+                                EOF = True
+                        if found:
+                            #print('h5')
+                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_chunk'] = chunk
+                            self.frames['stats']['current_record_index'] = index
+
+                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
+                        tab_suffixes = \
+                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            #print(tab_suffix)
+                            #print(self.frames['stats']['records_current_station'+tab_suffix])
+                            self.frames['stats']['current_record'+tab_suffix] =  \
+                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                      (self.frames['stats']['STNID'] , \
+                                       self.frames['stats']['current_record_chunk'] , \
+                                       self.frames['stats']['current_record_index'])]
+
+
+                        self.hover_active = True
+                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                        # print('h13')
+                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
+                        #     self.goto_datetime_worldmap(
+                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                        #         'after')
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap',
+                        #                                           'profiles'])
+                        # else:
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap_stations',
+                        #                                           'profiles'])
+
+
+
+                    elif label in ['worldmap_stations','worldmap']:
+                        #print('h5')
+
+                        if (self.axes['worldmap'].lat is not None) and \
+                           (self.axes['worldmap'].lon is not None):
+
+
+                            #self.loading()
+                            self.fig.canvas.draw()
+                            self.fig.show()
+
+
+                            # get position of 
+                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
+                                                                 self.axes['worldmap'].lat[0]) + \
+                                           self.axes['worldmap'].lat[0],4)
+                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
+                                                                 self.axes['worldmap'].lon[0]) + \
+                                           self.axes['worldmap'].lon[0],4)
+                        
+                            stations = self.frames['worldmap']['stations'].table
+                            #print('h7')
+                        
+                            #reset stations iterator:
+                            # if 'stations_iterator' in self.frames['worldmap'].keys():
+                            #     self.frames['worldmap']['stations_iterator'].close()
+                            #     del(self.frames['worldmap']['stations_iterator'])
+                            # if 'stations_iterator' in self.frames['stats'].keys():
+                            #     self.frames['stats']['stations_iterator'].close()
+                            #     del(self.frames['stats']['stations_iterator'])
+                            self.frames['worldmap']['stations_iterator'] =\
+                               stations_iterator(self.frames['worldmap']['stations'])
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                            EOF = False
+                            found = False
+                            while (not found and not EOF):
+                                #print('h8',station.latitude,latmap)
+                                #print('h8',station.longitude,lonmap)
+                                if (round(station.latitude,3) == round(latmap,3)) and \
+                                    (round(station.longitude,3) == round(lonmap,3)):
+                                       found = True 
+                                if not found:
+                                    try:
+                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                                    except (StopIteration):
+                                        EOF = True
+                            if found:
+                                self.frames['worldmap']['STNID'] = STNID
+                                self.frames['worldmap']['current_station'] = \
+                                        station
+                        
+                            self.frames['stats']['stations_iterator'] = \
+                                self.frames['worldmap']['stations_iterator'] 
+                            #print('h8')
+                            # inherit station position for the stats frame...
+                            for key in self.frames['worldmap'].keys():
+                                self.frames['stats'][key] = self.frames['worldmap'][key]
+                                
+                            ## fetch records of current station...
+                            #self.frames['stats']['records_current_station_mod'] =\
+                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                            # ... and their indices
+                            self.frames['stats']['records_current_station_index'] = \
+                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                                     == \
+                                     self.frames['stats']['current_station'].name)
+
+
+                            tab_suffixes = \
+                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['records_current_station'+tab_suffix] = \
+                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+                            # ... create a record iterator ...
+                            #self.frames['stats']['records_iterator'].close()
+                            del(self.frames['stats']['records_iterator'])
+                            self.frames['stats']['records_iterator'] = \
+                                self.frames['stats']['records_current_station_mod'].iterrows()
+
+
+
+                        
+                            #print('h9')
+                            # ... and go to to the first record of the current station
+                            (self.frames['stats']['STNID'] , \
+                             self.frames['stats']['current_record_chunk'] , \
+                             self.frames['stats']['current_record_index']) , \
+                            self.frames['stats']['current_record_mod'] = \
+                                self.frames['stats']['records_iterator'].__next__()
+                        
+
+
+
+                            #print('h10')
+                            # cash the current record
+                            tab_suffixes = \
+                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['current_record'+tab_suffix] =  \
+                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                          (self.frames['stats']['STNID'] , \
+                                           self.frames['stats']['current_record_chunk'] , \
+                                           self.frames['stats']['current_record_index'])]
+
+                            #print('h11')
+                            
+                            self.hover_active = True
+                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                            #print('h13')
+
+                        
+
+            #if (stations is not None):
+            #    for iSTN,STN in stations.iterrows():
+            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
+            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
+
+        # self.fig.show()
+ 
+        # we are hovering on nothing, so we are going back to the position of
+        # the profile sounding
+        if pos is None:
+            if self.hover_active == True:
+                #print('h1*')
+                
+                #self.loading()
+                # to do: reset stations iterators
+
+                # get station and record index from the current profile
+                for key in ['STNID', 'current_station']:
+                    self.frames['stats'][key] = self.frames['profiles'][key]
+
+                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
+                self.frames['stats']['current_station'] = \
+                        self.frames['profiles']['current_station']
+                #print('h3a*')
+                self.frames['stats']['records_current_station_mod'] = \
+                        self.frames['profiles']['records_current_station_mod']
+                #print('h3b*')
+
+                # the next lines recreate the records iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+
+                # reset stations iterator...
+                #self.frames['stats']['records_iterator'].close()
+                del(self.frames['stats']['records_iterator'])
+                self.frames['stats']['records_iterator'] = \
+                    self.frames['stats']['records_current_station_mod'].iterrows()
+                #print('h4*')
+
+                # ... and go to the record of the profile window (last one that
+                # was picked by the user)
+                found = False
+                EOF = False
+                while (not found) and (not EOF):
+                    try:
+                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                        #print("hello*")
+                        #print(self.frames['profiles']['current_record_index'])
+                        #print(self.frames['profiles']['STNID'])
+                        #print(STNID,index)
+                        if (index == self.frames['profiles']['current_record_index']) and \
+                            (chunk == self.frames['profiles']['current_record_chunk']) and \
+                            (STNID == self.frames['profiles']['STNID']):
+                            #print('found!')
+                            found = True
+                    except StopIteration:
+                        EOF = True
+                if found:
+                    #print('h5*')
+                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_chunk'] = chunk
+                    self.frames['stats']['current_record_index'] = index
+
+                #print('h6*')
+
+
+
+                # # fetch records of current station...
+                # self.frames['stats']['records_current_station_mod'] =\
+                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                # ... and their indices
+                self.frames['stats']['records_current_station_index'] = \
+                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                         == \
+                         self.frames['stats']['current_station'].name)
+
+
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['records_current_station'+tab_suffix] = \
+                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+                
+
+                # cash the records of the current stations
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['current_record'+tab_suffix] =  \
+                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                              (self.frames['stats']['STNID'] , \
+                               self.frames['stats']['current_record_chunk'] , \
+                               self.frames['stats']['current_record_index'])]
+
+
+                # the next lines recreate the stations iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+                #print('h7*')
+
+                # reset the stations iterators
+                for framekey in ['stats','worldmap']:
+                    ##print(framekey)
+                    if 'stations_iterator' in self.frames[framekey]:
+                        #self.frames[framekey]['stations_iterator'].close()
+                        del(self.frames[framekey]['stations_iterator'])
+
+                self.frames['worldmap']['current_station'] = \
+                        self.frames['profiles']['current_station']
+
+                #recreate the stations iterator for the worldmap...
+                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+
+                # ... and go the position of the profile
+                #print('h8*')
+                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                EOF = False
+                found = False
+                while (not found and not EOF):
+                    if STNID == self.frames['profiles']['STNID'] :
+                        found = True 
+                    if not found:
+                        try:
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                        except (StopIteration):
+                            EOF = True
+                if found:
+                    self.frames['worldmap']['current_station'] = station
+                    self.frames['worldmap']['STNID'] = STNID
+                #print('h9*')
+                self.frames['stats']['stations_iterator'] = \
+                    self.frames['worldmap']['stations_iterator'] 
+
+                # the stats window now inherits the current station from the
+                # worldmap
+                for key in ['STNID','current_station','stations_iterator']: 
+                    self.frames['stats'][key] = self.frames['worldmap'][key] 
+                #print('h10*')
+
+                # # we now only need inherit station position and go to first record
+                # for key in self.frames['worldmap'].keys():
+                #     self.frames['stats'][key] = self.frames['worldmap'][key]
+
+                # self.frames['stats']['records_current_station'] =\
+                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
+
+                # #print(self.frames['stats']['records_current_station'])
+                # self.frames['stats']['records_iterator'] = \
+                #                 self.frames['stats']['records_current_station'].iterrows()
+                # (self.frames['stats']['STNID'] , \
+                # self.frames['stats']['current_record_index']) , \
+                # self.frames['stats']['current_record_mod'] = \
+                #                 self.frames['stats']['records_iterator'].__next__()
+                
+
+
+
+
+
+
+                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
+                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
+                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
+                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+                self.hover_active = False
+                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
+    # def loading(self):
+    #     self.tbox['loading'].set_text('Loading...')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+    #     sleep(0.1)
+    # def ready(self):
+    #     self.tbox['loading'].set_text('Ready')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+
+
+
diff --git a/model.py b/model.py
index 077e6c1..8760411 100644
--- a/model.py
+++ b/model.py
@@ -1,4 +1,4 @@
-#_ 
+# 
 # CLASS
 # Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
 # Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
@@ -25,18 +25,154 @@
 import copy as cp
 import numpy as np
 import sys
+import warnings
+import pandas as pd
+from ribtol_hw import zeta_hs2 , funcsche
+import logging
+#from SkewT.thermodynamics import Density
 #import ribtol
 
+grav = 9.81
 def esat(T):
     return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
 
 def qsat(T,p):
     return 0.622 * esat(T) / p
 
+
+def ribtol(Rib, zsl, z0m, z0h): 
+    Rib = np.float64(Rib)
+    zsl = np.float64(zsl)
+    z0m = np.float64(z0m)
+    z0h = np.float64(z0h)
+    #print(Rib,zsl,z0m,z0h)
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    #print(Rib,zsl,z0m,z0h)
+    while (abs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+        #print(L,fx/fxdif)
+        if(abs(L) > 1e12):
+            break
+
+    return L
+  
+def psim(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psim = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+  
+def psih(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * np.log( (1. + x*x) / 2.)
+        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+ 
 class model:
-    def __init__(self, model_input):
-        # initialize the different components of the model
-        self.input = cp.deepcopy(model_input)
+    def __init__(self, model_input = None,debug_level=None):
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        self.logger = logging.getLogger('model')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        """ initialize the different components of the model """ 
+
+        if model_input is not None:
+            # class4gl style input
+            if 'pars' in model_input.__dict__.keys():
+
+                # we make a reference to the full input first, so we can dump it
+                # afterwards
+                self.input_c4gl = model_input
+
+                # we copy the regular parameters first. We keep the classical input
+                # format as self.input so that we don't have to change the entire
+                # model code.
+                self.input = cp.deepcopy(model_input.pars)
+
+                # we copy other sections we are interested in, such as profile
+                # data, and store it also under input
+
+                # I know we mess up a bit the structure of the class4gl_input, but
+                # we will make it clean again at the time of dumping data
+
+                # So here, we copy the profile data into self.input
+                # 1. Air circulation data 
+                if 'sw_ac' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ac']:
+                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
+                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
+
+                    # correct pressure of levels according to surface pressure
+                    # error (so that interpolation is done in a consistent way)
+
+                    p_e = self.input.Ps - self.input.sp
+                    for irow in self.input.air_ac.index[::-1]:
+                       self.input.air_ac.p.iloc[irow] =\
+                        self.input.air_ac.p.iloc[irow] + p_e
+                       p_e = p_e -\
+                       (self.input.air_ac.p.iloc[irow]+p_e)/\
+                        self.input.air_ac.p.iloc[irow] *\
+                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+
+
+
+                # 2. Air circulation data 
+                if 'sw_ap' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ap']:
+                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
+
+            # standard class input
+            else:
+                self.input = cp.deepcopy(model_input)
+
+    def load_yaml_dict(self,yaml_dict):
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                for keydata,value in data.items():
+                    self.__dict__[keydata] = value
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            #elif key == 'sources':
+            #    self.__dict__[key] = data
+            elif key == 'out':
+                # lets convert it to a list of dictionaries
+                dictouttemp = pd.DataFrame(data).to_dict('list')
+            else: 
+                 warnings.warn("Key '"+key+"' is be implemented.")
+            #     self.__dict__[key] = data
+
+
+        self.tsteps = len(dictouttemp['h'])
+        self.out = model_output(self.tsteps)
+        for keydictouttemp in dictouttemp.keys():
+            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+
+
   
     def run(self):
         # initialize model variables
@@ -104,14 +240,19 @@ def init(self):
         self.sw_ls      = self.input.sw_ls      # land surface switch
         self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
         self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
+
+        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
+        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
+        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
   
         # initialize mixed-layer
         self.h          = self.input.h          # initial ABL height [m]
         self.Ps         = self.input.Ps         # surface pressure [Pa]
+        self.sp         = self.input.sp         # This is also surface pressure
+                                                #but derived from the global data [Pa]
         self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
         self.ws         = None                  # large-scale vertical velocity [m s-1]
         self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
-        self.fc         = self.input.fc         # coriolis parameter [s-1]
         self.we         = -1.                   # entrainment velocity [m s-1]
        
          # Temperature 
@@ -181,105 +322,6 @@ def init(self):
         self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
         self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
       
-  # BEGIN -- HW 20170606
-
-        # z-coordinate for vertical profiles of stratification above the mixed-layer height
-
-        self.z_pro      = self.input.z_pro  # initial profile of potential temperature [K]
-
-        self.theta_pro  = self.input.theta_pro  # initial profile of potential temperature [K]
-
-
-        if ((self.theta_pro is not None) and (self.z_pro is not None)):
-
-            
-            indextheta = np.where(self.z_pro == self.h)
-            if len(indextheta) == 0:
-                raise RuntimeError("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
-                
-                
-            if indextheta[0][0] !=1:
-                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
-                stop
-
-            
-            if self.theta_pro[1] != self.theta_pro[0]:
-                print("error input profile consistency: two lowest profile levels should be equal.")
-            
-            # initialize theta from its profile when available
-            theta_old = self.theta
-            theta_new = self.theta_pro[indextheta[0][0]]
-            
-            
-            
-            if ((theta_old is not None) & (theta_old != theta_new)):
-                print("Warning: theta input was provided ("+str(theta_old)+\
-                    "kg kg-1), but it is now overwritten by the first level (index 0) of theta_pro which is different ("\
-                    +str(theta_new)+"K).")
-                                    
-            self.theta = theta_new
-
-            # make a profile of the stratification 
-            # please note that the stratification between z_pro[i] and z_pro[i+1] 
-            # is given by gammatheta_pro[i]
-
-            # self.gammatheta_pro = np.gradient(self.theta_pro) / np.gradient(self.z_pro)
-            with np.errstate(divide='ignore'):
-                self.gammatheta_pro = np.array(self.theta_pro[1:] - self.theta_pro[:-1]) \
-                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
-                           
-            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro)[0][-1]]
-        else:
-            self.gammatheta_pro = None
-
-
-        self.q_pro  = self.input.q_pro  # initial profile of potential temperature [K]
-
-        if ((self.q_pro is not None) and (self.z_pro is not None)):
-            
-            indexq = np.where(self.z_pro == self.h)
-            if len(indexq) == 0:
-                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
-                stop   
-                
-            if indexq[0][0] !=1:
-                print("Error input profile consistency: mixed-layer height needs to be equal to the second level of the vertical profile input!")
-                stop
-
-            
-            if self.q_pro[1] != self.q_pro[0]:
-                print("error inpuy ptogilr consistency: two lowest profile levels should be equal.")
-            
-            # initialize q from its profile when available
-            q_old = self.q
-            q_new = self.q_pro[indexq[0][0]]
-            
-            
-            
-            if ((q_old is not None) & (q_old != q_new)):
-                print("Warning: q input was provided ("+str(q_old)+\
-                    "kg kg-1), but it is now overwritten by the first level (index 0) of q_pro which is different ("\
-                    +str(q_new)+"kg kg-1).")
-                                    
-            self.q = q_new
-
-            # make a profile of the stratification 
-            # please note that the stratification between z_pro[i] and z_pro[i+1] 
-            # is given by gammaq_pro[i]
-
-            # self.gammaq_pro = np.gradient(self.q_pro) / np.gradient(self.z_pro)
-            with np.errstate(divide='ignore'):
-                self.gammaq_pro = np.array(self.q_pro[1:] - self.q_pro[:-1]) \
-                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
-                           
-            self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
-        else:
-            self.gammaq_pro = None
-
-# END -- HW 20170606      
-        
-        
-        
         
         
         # CO2
@@ -304,7 +346,248 @@ def init(self):
         self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
         self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
         self.advv       = self.input.advv       # advection of v-wind [m s-2]
- 
+         
+  # BEGIN -- HW 20170606
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        if self.sw_ac:
+        # this is the data frame with the grided profile on the L60 grid
+        # (subsidence, and advection) 
+            self.air_ac      = self.input.air_ac  # full level air circulation
+                                                  # forcing
+            # self.air_ach     = self.input.air_ach # half level air circulation
+            #                                       # forcing
+            
+
+        if self.sw_ap:
+        # this is the data frame with the fitted profile (including HAGL,
+        # THTA,WSPD, SNDU,WNDV PRES ...)
+            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
+
+            # just for legacy reasons...
+            if 'z' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
+            if 'p' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
+
+            indexh = np.where(self.air_ap.z.values == self.h)
+            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
+                raise ValueError("Error input profile consistency: mixed- \
+                                 layer height needs to be equal to the second \
+                                 and third \
+                                 level of the vertical profile input!")
+            # initialize q from its profile when available
+            p_old = self.Ps
+            p_new = self.air_ap.p[indexh[0][0]]
+            
+            if ((p_old is not None) & (p_old != p_new)):
+                print("Warning: Ps input was provided ("+str(p_old)+\
+                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+                    +str(p_new)+"Pa).")
+                                    
+            self.Ps = p_new
+            # these variables/namings are more convenient to work with in the code
+            # we will update the original variables afterwards
+            #self.air_ap['q'] = self.air_ap.QABS/1000.
+
+            self.air_ap = \
+                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
+            # we require the temperature fields, since we need to consider
+            # advection
+            # if self.sw_ac:
+            #     #self.air_ap['theta'] = self.air_ap['t'] *
+
+            #     # we consider self.sp in case of air-circulation input (for
+            #     # consistence)
+            #     self.air_ap['t'] = \
+            #                 self.air_ap.theta *  \
+            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
+            # else:
+            # we consider self.Ps in case of balloon input only 
+            self.air_ap = self.air_ap.assign(t = lambda x: \
+                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
+
+            #self.air_ap['theta'] = self.air_ap.THTA
+            if 'u' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
+            if 'v' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
+
+            for var in ['theta','q','u','v']:
+
+                
+                if self.air_ap[var][1] != self.air_ap[var][0]:
+                    raise ValueError("Error input profile consistency: two \
+                                     lowest profile levels for "+var+" should \
+                                     be equal.")
+                
+                # initialize the value from its profile when available
+                value_old = self.__dict__[var]
+                value_new = self.air_ap[var][indexh[0][0]]
+                
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided \
+                                     ("+str(value_old)+ "kg kg-1), \
+                                     but it is now overwritten by the first \
+                                     level (index 0) of air_ap]var\ which is \
+                                     different (" +str(value_new)+"K).")
+                                        
+                self.__dict__[var] = value_new
+
+                # make a profile of the stratification 
+                # please note that the stratification between z_pro[i] and
+                # z_pro[i+1] is given by air_ap.GTHT[i]
+
+                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
+                # np.gradient(self.z_pro)
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
+
+
+                self.__dict__['gamma'+var] = \
+                    self.air_ap['gamma'+var][np.where(self.h >= \
+                                                     self.air_ap.z)[0][-1]]
+
+
+
+        # the variable p_pro is just for diagnosis of lifted index
+            
+            
+
+            # input Ph is wrong, so we correct it according to hydrostatic equation
+            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+
+            #if self.sw_ac:
+                # note that we use sp as surface pressure, which is determined
+                # from era-interim instead of the observations. This is to
+                # avoid possible failure of the interpolation routine
+                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
+                #                          + \
+                #                          list(self.air_ap.p[3:]))
+
+            # else:
+                # in the other case, it is updated at the time of calculting
+                # the statistics 
+
+# END -- HW 20170606      
+        #print(self.air_ap)
+
+        if self.sw_ac and not self.sw_ap:
+            raise ValueError("air circulation switch only possible when air \
+                             profiles are given")
+        
+        if self.sw_ac:
+
+            # # # we comment this out, because subsidence is calculated
+            # according to advection
+            # #interpolate subsidence towards the air_ap height coordinate
+            # self.air_ap['w'] = np.interp(self.air_ap.p,\
+            #                               self.air_ac.p,\
+            #                               self.air_ac.w) 
+            # #subsidence at the mixed-layer top
+            # self.w = self.air_ap.w[1]
+        
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+                # in case we didn't find any points, we just take the lowest one.
+                # actually, this can happen if ERA-INTERIM pressure levels are
+                # inconsistent with 
+                if in_ml.sum() == 0:
+                    warnings.warn(" no circulation points in the mixed layer \
+                                  found. We just take the bottom one.")
+                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+
+                for var in ['t','q','u','v']:
+    
+                   # calculation of the advection variables for the mixed layer
+                   # we weight by the hydrostatic thickness of each layer and
+                   # divide by the total thickness
+                   self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+
+                   # calculation of the advection variables for the profile above
+                   # (lowest 3 values are not used by class)
+                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
+                   self.air_ap['adv'+var] = \
+                           np.interp(self.air_ap.p,\
+                                     self.air_ac.p,\
+                                     self.air_ac['adv'+var+'_x']) \
+                           + \
+                           np.interp(self.air_ap.p, \
+                                       self.air_ac.p, \
+                                       self.air_ac['adv'+var+'_y'])
+
+                # as an approximation, we consider that advection of theta in the
+                # mixed layer is equal to advection of t. This is a sufficient
+                # approximation since theta and t are very similar at the surface
+                # pressure.
+                self.__dict__['advtheta'] = self.__dict__['advt']
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # self.wrho = np.interp(self.P_h,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) 
+            # self.ws   = self.air_ap.w.iloc[1]
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
+                self.air_ap = self.air_ap.assign(R = 0.)
+                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                     self.Rv*self.air_ap.q)
+                self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+                
+                self.air_ap = self.air_ap.assign(w = 0.)
+                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+                #print('hello w ini')
+
+                # Note: in case of sw_ac is False, we update it from prescribed
+                # divergence
+                self.ws   = self.air_ap.w[1]
+
+                # self.ws   = self.wrho/self.rho
+                # self.ws   = self.wrho/(self.P_h/ \
+                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
+                #                         self.theta) # this should be T!!!
+
+                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+                #                         + \
+                #                         self.air_ac['divU_y'][in_ml])* \
+                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                #             self.air_ac['delpdgrav'][in_ml].sum() \
+        
+
         # Tendencies 
         self.htend      = None                  # tendency of CBL [m s-1]
         self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
@@ -333,6 +616,8 @@ def init(self):
   
         # initialize radiation
         self.lat        = self.input.lat        # latitude [deg]
+        #self.fc         = self.input.fc         # coriolis parameter [s-1]
+        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
         self.lon        = self.input.lon        # longitude [deg]
         self.doy        = self.input.doy        # day of the year [-]
         self.tstart     = self.input.tstart     # time of the day [-]
@@ -441,54 +726,79 @@ def init(self):
             self.run_mixed_layer()
 
     def timestep(self):
+
+        self.dtmax = +np.inf
+        self.logger.debug('before stats') 
         self.statistics()
 
         # run radiation model
+        self.logger.debug('before rad') 
         if(self.sw_rad):
             self.run_radiation()
   
         # run surface layer model
         if(self.sw_sl):
+            self.logger.debug('before surface layer') 
             self.run_surface_layer()
         
         # run land surface model
         if(self.sw_ls):
+            self.logger.debug('before land surface') 
             self.run_land_surface()
  
         # run cumulus parameterization
         if(self.sw_cu):
+            self.logger.debug('before cumulus') 
             self.run_cumulus()
    
+        self.logger.debug('before mixed layer') 
         # run mixed-layer model
         if(self.sw_ml):
             self.run_mixed_layer()
+        self.logger.debug('after mixed layer') 
  
         #get first profile data point above mixed layer
-        zidx_first = np.where(self.z_pro > self.h)[0][0]
-        
-        if self.htend != 0.:
-            dtmax = ( self.z_pro[zidx_first] - self.h)/self.htend
-        else:
-            dtmax = +np.inf
-        
+        if self.sw_ap:
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                # here we correct for the fact that the upper profile also
+                # shifts in the vertical.
+
+                diffhtend = self.htend - self.air_ap.w[zidx_first]
+                if diffhtend > 0:
+                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            else:
+                if self.htend > 0:
+                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            #print(self.h,zidx_first,self.ws,self.air_ap.z)
+
         
-        self.substep =  (self.dtcur > dtmax)
+        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
+        self.logger.debug('before store') 
+        self.substep =  (self.dtcur > self.dtmax)
         if self.substep:
-            dtnext = self.dtcur - dtmax
-            self.dtcur = dtmax
+            dtnext = self.dtcur - self.dtmax
+            self.dtcur = self.dtmax
+
+        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
+
         # HW: this will be done multiple times in case of a substep is needed
         # store output before time integration
         if self.firsttime:
             self.store()
   
+        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
         # time integrate land surface model
         if(self.sw_ls):
             self.integrate_land_surface()
-  
+        self.logger.debug('before integrate mixed layer') 
         # time integrate mixed-layer model
         if(self.sw_ml):
-            self.integrate_mixed_layer()
-        
+            self.integrate_mixed_layer() 
+        self.logger.debug('after integrate mixed layer') 
         if self.substep:
             self.dtcur = dtnext
             self.firsttime = False
@@ -498,7 +808,7 @@ def timestep(self):
             self.t += 1 
             self.firsttime = True
             self.substeps = 0
-        
+        self.logger.debug('going to next step')
         
         
   
@@ -509,6 +819,10 @@ def statistics(self):
         self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
         # Mixed-layer top properties
         self.P_h    = self.Ps - self.rho * self.g * self.h
+        # else:
+            # in the other case, it is updated at the time that the profile is
+            # updated (and at the initialization
+
         self.T_h    = self.theta - self.g/self.cp * self.h
 
         #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
@@ -533,6 +847,7 @@ def statistics(self):
             it          += 1
 
         if(it == itmax):
+
             print("LCL calculation not converged!!")
             print("RHlcl = %f, zlcl=%f, theta=%f, q=%f"%(RHlcl, self.lcl,self.theta,self.q))
 
@@ -565,7 +880,12 @@ def run_mixed_layer(self):
 
 
         # calculate large-scale vertical velocity (subsidence)
-        self.ws = -self.divU * self.h
+        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
+            self.ws = -self.divU * self.h
+        # else:
+        #     in case the air circulation switch is turned on, subsidence is
+        #     calculated from the circulate profile at the initialization and
+        #     in the integrate_mixed_layer routine
               
         # calculate compensation to fix the free troposphere in case of subsidence 
         if(self.sw_fixft):
@@ -603,29 +923,105 @@ def run_mixed_layer(self):
         self.wqe         = -self.we * self.dq
         self.wCO2e       = -self.we * self.dCO2
         
-        self.htend       = self.we + self.ws + self.wf - self.M
+        htend_pre       = self.we + self.ws + self.wf - self.M
         
-        self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        self.qtend       = (self.wq     - self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
         
-        self.dthetatend  = self.gammatheta * (self.we + self.wf - self.M) - self.thetatend + w_th_ft
-        self.dqtend      = self.gammaq     * (self.we + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+ 
+        #print('thetatend_pre',thetatend_pre)
+        
+        #preliminary boundary-layer top chenage
+        #htend_pre = self.we + self.ws + self.wf - self.M
+        #preliminary change in temperature jump
+        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
+                          thetatend_pre + w_th_ft
+        
+        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
+        l_entrainment = True
+
+        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
+            l_entrainment = False
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! temperature jump is at the lower limit \
+                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+        elif dtheta_pre < 0.1:
+            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
+            l_entrainment = True
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          " Warning! Potential temperature jump at mixed- \
+                          layer height would become too low limiting timestep \
+                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+            self.dtmax = min(self.dtmax,dtmax_new)
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "next subtimestep, entrainment will be disabled")
+            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
+
+
+
+        # when entrainment is disabled, we just use the simplified formulation
+        # as in Wouters et al., 2013 (section 2.2.1)
+
+        self.dthetatend = l_entrainment*dthetatend_pre + \
+                        (1.-l_entrainment)*0.
+        self.thetatend = l_entrainment*thetatend_pre + \
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = l_entrainment*htend_pre + \
+                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
+        #stop
+
+
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+
+
+        # self.qtend = l_entrainment*qtend_pre + \
+        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
+        # self.CO2tend = l_entrainment*CO2tend_pre + \
+        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+
+
+        #     # part of the timestep for which the temperature mixed-layer jump
+        #     # was changing, and for which entrainment took place. For the other
+        #     # part, we don't assume entrainment anymore, and we use the
+        #     # simplified formulation  of Wouters et al., 2013
+
+        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
+        #   
+        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
+        #                      self.dthetatend + w_th_ft) + \
+        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
+        #     self.htend = fac*self.htend + \
+        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
+        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
+        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+
+        # else:
+        #     #self.htend = htend_pre
+        #     self.dthetatend = dthetatend_pre
+        #     self.thetatend = thetatend_pre
+        
+        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
      
         # assume u + du = ug, so ug - u = du
         if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + self.we * self.dv)  / self.h + self.advv
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
   
-            self.dutend      = self.gammau * (self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (self.we + self.wf - self.M) - self.vtend
+            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
         
         # tendency of the transition layer thickness
         if(self.ac > 0 or self.lcl - self.h < 300):
             self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
         else:
             self.dztend = 0.
+
    
     def integrate_mixed_layer(self):
         # set values previous time step
@@ -650,16 +1046,18 @@ def integrate_mixed_layer(self):
             
 
 # END -- HW 20170606        
-        
         self.h        = h0      + self.dtcur * self.htend
+        # print(self.h,self.htend)
+        # stop
         self.theta    = theta0  + self.dtcur * self.thetatend
+        #print(dtheta0,self.dtcur,self.dthetatend)
         self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
         self.q        = q0      + self.dtcur * self.qtend
         self.dq       = dq0     + self.dtcur * self.dqtend
         self.CO2      = CO20    + self.dtcur * self.CO2tend
         self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
         self.dz_h     = dz0     + self.dtcur * self.dztend
-
+            
         # Limit dz to minimal value
         dz0 = 50
         if(self.dz_h < dz0):
@@ -671,61 +1069,312 @@ def integrate_mixed_layer(self):
             self.v        = v0      + self.dtcur * self.vtend
             self.dv       = dv0     + self.dtcur * self.dvtend
 
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
 
-        
-        # note that theta and q itself are updatet by class itself
+            for var in ['t','q','u','v']:
+                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
 
-        #Afterwards, update the vertical profiles by removing any data points below the new h
-        if self.theta_pro is not None:
-            self.theta_pro = np.array([self.theta,self.theta,self.theta+self.dtheta]+list(self.theta_pro[self.z_pro > self.h]))
+            # take into account advection for the whole profile
+                
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
 
-        if self.q_pro is not None:
-            self.q_pro = np.array([self.q,self.q,self.q+self.dq]+list(self.q_pro[self.z_pro > self.h]))
+            var = 'z'
+            #print(self.air_ap[var])
+                #     print(self.air_ap['adv'+var])
 
 
 
-        if self.z_pro is not None:
-            self.z_pro = np.array([2.,self.h,self.h]+list(self.z_pro[self.z_pro > self.h]))
 
-        
-        if self.gammatheta_pro is not None:
-            
-                        # self.gammatheta_pro = np.gradient(self.theta_pro) / np.gradient(self.z_pro)
-            with np.errstate(divide='ignore'):
-                self.gammatheta_pro = np.array(self.theta_pro[1:] - self.theta_pro[:-1]) \
-                           / np.array(self.z_pro[1:] -  self.z_pro[:-1]    )
-                           
-            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro)[0][-1]]
-            
-            
-            
-            #self.gammatheta_pro = np.array([self.gammatheta,self.gammatheta,self.gammatheta]+list(self.gammatheta_pro[self.z_pro[:-1] > self.h]))
+            #moving the profile vertically according to the vertical wind
+                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
 
-        if self.gammaq_pro is not None:            
-                        
-            #self.gammaq_pro = np.gradient(self.q_pro) / np.gradient(self.z_pro)
-            with np.errstate(divide='ignore'):
-                self.gammaq_pro = (self.q_pro[1:] -self.q_pro[:-1])  / (self.z_pro[1:] -self.z_pro[:-1])
-            
-            self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
+
+            # air_apvarold = pd.Series(np.array(self.air_ap.z))
+            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
+            # stop
+
+
+                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
+                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
+
+            #As t is updated, we also need to recalculate theta (and R)
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+
+            # air_aptheta_old = pd.Series(self.air_ap['theta'])
+            self.air_ap['theta'] = \
+                        self.air_ap.t * \
+                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
+                                         self.dtcur * self.air_ap.w[zidx_first:]
+
+#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
+#            print(self.t, self.dtcur,self.dt,self.htend)
+
+            # # the pressure levels of the profiles are recalculated according to
+            # # there new height (after subsidence)
+            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
+            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
+            #         * self.dtcur *  self.air_ap.w[zidx_first:]
+
+            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
+                    self.dtcur * self.air_ap.wp[zidx_first:]
+
+            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
+        # note that theta and q itself are updatet by class itself
+
+    
+        if self.sw_ap:
+            # Just for model consistency preservation purposes, we set the
+            # theta variables of the mixed-layer to nan values, since the
+            # mixed-layer values should overwritte by the mixed-layer
+            # calculations of class.
+            self.air_ap['theta'][0:3] = np.nan 
+            self.air_ap['p'][0:3] = np.nan 
+            self.air_ap['q'][0:3] = np.nan 
+            self.air_ap['u'][0:3] = np.nan 
+            self.air_ap['v'][0:3] = np.nan 
+            self.air_ap['t'][0:3] = np.nan 
+            self.air_ap['z'][0:3] = np.nan 
+
+            # Update the vertical profiles: 
+            #   - new mixed layer properties( h, theta, q ...)
+            #   - any data points below the new ixed-layer height are removed
+
+            # Three data points at the bottom that describe the mixed-layer
+            # properties
+            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
+                                           # columns as air_ap
+            # air_ap_head['z'].iloc[0] = 2.
+            # air_ap_head['z'].iloc[1] = self.__dict__['h']
+            # air_ap_head['z'].iloc[2] = self.__dict__['h']
+            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
+                        [2.,self.__dict__['h'],self.__dict__['h']]
+            for var in ['theta','q','u','v']:
+
+                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
+                        [self.__dict__[var], \
+                         self.__dict__[var], \
+                         self.__dict__[var] + self.__dict__['d'+var]]
+                
+            #print(self.air_ap)
+
+            # This is the remaining profile considering the remaining
+            # datapoints above the mixed layer height
+            air_ap_tail = self.air_ap.iloc[3:]
+            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
+
+            # print('h',self.h)
+            # # only select samples monotonically increasing with height
+            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            # air_ap_tail = pd.DataFrame()
+            # theta_low = self.theta
+            # z_low =     self.h
+            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            # for ibottom in range(1,len(air_ap_tail_orig)):
+            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
+            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+            # make theta increase strong enough to avoid numerical
+            # instability
+            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            air_ap_tail = pd.DataFrame()
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            theta_low = self.theta
+            z_low =     self.h
+            ibottom = 0
+            itop = 0
+            # print(air_ap_tail_orig)
+            # stop
+
+            # HW: this is the lower limit that we use for gammatheta, which is
+            # there to avoid model crashes. Besides on this limit, the upper
+            # air profile is modified in a way that is still conserves total
+            # quantities of moisture and temperature. The limit is set by trial
+            # and error. The numerics behind the crash should be investigated
+            # so that a cleaner solution can be provided.
+            gammatheta_lower_limit = 0.002
+            while ((itop in range(0,1)) or (itop != ibottom)):
+                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+                if (
+                    #(z_mean > (z_low+0.2)) and \
+                    #(theta_mean > (theta_low+0.02) ) and \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                  (itop >= (len(air_ap_tail_orig)-1)) \
+                   :
+
+                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                    ibottom = itop+1
+                    theta_low = air_ap_tail.theta.iloc[-1]
+                    z_low =     air_ap_tail.z.iloc[-1]
+    
+
+                itop +=1
+                # elif  (itop > len(air_ap_tail_orig)-10):
+                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+                #print(itop,ibottom)
+
+            if itop > 1:
+                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! Temperature profile was too steep. \
+                                  Modifying profile: "+ \
+                                  str(itop - 1)+ " measurements were dropped \
+                                  and replaced with its average \
+                                  Modifying profile. \
+                                  mean with next profile point(s).") 
+
+
+            self.air_ap = pd.concat((air_ap_head,\
+                                     air_ap_tail,\
+                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
+                                                                      axis=1)
+
+            if  self.sw_ac:
+                qvalues = \
+                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
+
+                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
+                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
+                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+                self.P_h    = self.Ps - self.rho * self.g * self.h
+                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
+                        [self.Ps,  self.P_h, self.P_h-0.1]
+
+                self.air_ap.t = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
+
+
+        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
+
+
+
+
+        # else:
+            # in the other case, it is updated at the time the statistics are
+            # calculated 
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if in_ml.sum() == 0:
+                warnings.warn(" no circulation points in the mixed layer \
+                              found. We just take the bottom one.")
+                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+            for var in ['t','q','u','v']:
+
+                # calculation of the advection variables for the mixed-layer
+                # these will be used for the next timestep
+                # Warning: w is excluded for now.
+
+                self.__dict__['adv'+var] = \
+                        ((self.air_ac['adv'+var+'_x'][in_ml] \
+                         + \
+                         self.air_ac['adv'+var+'_y'][in_ml])* \
+                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                        self.air_ac['delpdgrav'][in_ml].sum()
+
+                # calculation of the advection variables for the profile above
+                # the mixed layer (also for the next timestep)
+                self.air_ap['adv'+var] = \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p,\
+                                              self.air_ac['adv'+var+'_x']) \
+                                    + \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p, \
+                                              self.air_ac['adv'+var+'_y'])
+                # if var == 't':
+                #     print(self.air_ap['adv'+var])
+                #     stop
+
+            # as an approximation, we consider that advection of theta in the
+            # mixed layer is equal to advection of t. This is a sufficient
+            # approximation since theta and t are very similar at the surface
+            # pressure.
+
+            self.__dict__['advtheta'] = self.__dict__['advt']
+
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            # update the vertical wind profile
+            self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                          self.air_ac.p, \
+                                          self.air_ac['wp'])
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
             
+            air_apwold = self.air_ap['w']
+            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+            #print('hello w upd')
+
+            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # # self.wrho = np.interp(self.P_h,\
+            # #                      self.air_ach.p,\
+            # #                      self.air_ach['wrho']) \
+
+
+
+            # Also update the vertical wind at the mixed-layer height
+            # (subsidence)
+            self.ws   = self.air_ap.w[1]
+        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
+
+            ## Finally, we update he 
+            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+            #                        + \
+            #                        self.air_ac['divU_y'][in_ml])* \
+            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+            #            self.air_ac['delpdgrav'][in_ml].sum() 
             
-            self.gammaq_pro = np.array([self.gammaq,self.gammaq,self.gammaq]+list(self.gammaq_pro[self.z_pro[:-1] > self.h]))
 
+        if self.sw_ap:
+            for var in ['theta','q','u','v']:
 
-#	# BEGIN -- HW 20170606
-#        # get new gammatheta(h) from its vertical profile when available:
-#        if(self.gammatheta_pro != None):
-#            #self.gammatheta = np.interp(self.h, self.z_pro, self.gammatheta_pro) 
-#            self.gammatheta = self.gammatheta_pro[np.where(self.h >= self.z_pro )[0][-1]]
-#
-#        # get gammaq(h) from its vertical profile when available:
- #       if(self.gammaq_pro != None):
- #           #self.gammaq = np.interp(self.h, self.z_pro, self.gammaq_pro) 
- #           self.gammaq = self.gammaq_pro[np.where(self.h >= self.z_pro)[0][-1]]
-#
-        
- 
+                # update of the slope (gamma) for the different variables, for
+                # the next timestep!
+
+                # there is an warning message that tells about dividing through
+                # zero, which we ignore
+
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                    # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap['gamma'+var] = gammavar
+
+                # Based on the above, update the gamma value at the mixed-layer
+                # top
+                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
+                                                                     self.air_ap.z)[0][-1]]
+
+            
     def run_radiation(self):
         sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
         sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
@@ -743,9 +1392,23 @@ def run_radiation(self):
         self.Lwout = self.bolz * self.Ts ** 4.
           
         self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
+        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
   
     def run_surface_layer(self):
-        ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+        # HW: I had to raise the minimum wind speed to make the simulation with
+        # the non-iterative solution stable (this solution was a wild guess, so I don't
+        # know the exact problem of the instability in case of very low wind
+        # speeds yet)
+        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        # version of 20180730 where there are still some runs crashing. Maybe
+        # an upper limit should be set on the monin-obukhov length instead of
+        # a lower limmit on the wind speed?
+        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        
         self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
         qsatsurf       = qsat(self.thetasurf, self.Ps)
         cq             = (1. + self.Cs * ueff * self.rs) ** -1.
@@ -755,23 +1418,56 @@ def run_surface_layer(self):
   
         zsl       = 0.1 * self.h
         self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
-        self.Rib  = min(self.Rib, 0.2)
+        
 
-        self.L     = self.ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
-        #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
- 
-        self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - self.psim(zsl / self.L) + self.psim(self.z0m / self.L)) ** 2.
-        self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - self.psim(zsl / self.L) + self.psim(self.z0m / self.L)) / (np.log(zsl / self.z0h) - self.psih(zsl / self.L) + self.psih(self.z0h / self.L))
-  
-        self.ustar = np.sqrt(self.Cm) * ueff
-        self.uw    = - self.Cm * ueff * self.u
-        self.vw    = - self.Cm * ueff * self.v
- 
-        # diagnostic meteorological variables
-        self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - self.psih(2. / self.L) + self.psih(self.z0h / self.L))
-        self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - self.psih(2. / self.L) + self.psih(self.z0h / self.L))
-        self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - self.psim(2. / self.L) + self.psim(self.z0m / self.L))
-        self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - self.psim(2. / self.L) + self.psim(self.z0m / self.L))
+
+        if self.sw_lit:
+            self.Rib  = min(self.Rib, 0.2)
+            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
+            self.zeta  = zsl/self.L
+            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
+            
+        
+            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
+            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
+            
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+        
+     
+            # diagnostic meteorological variables
+            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
+            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
+            
+            # diagnostic meteorological variables
+        else:
+            
+            ## circumventing any iteration with Wouters et al., 2012
+            self.zslz0m = np.max((zsl/self.z0m,10.))
+            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
+            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
+            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
+            self.L = zsl/self.zeta
+            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
+        
+            self.Cm = self.k**2.0/funm/funm
+            self.Cs = self.k**2.0/funm/funh
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+            
+            # extrapolation from mixed layer (instead of from surface) to 2meter
+            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
+            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
+            self.u2m    =                - self.uw     / self.ustar / self.k * funm
+            self.v2m    =                - self.vw     / self.ustar / self.k * funm
+        
+        
         self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
         self.e2m    = self.q2m * self.Ps / 0.622
      
@@ -782,6 +1478,7 @@ def ribtol(self, Rib, zsl, z0m, z0h):
         else:
             L  = -1.
             L0 = -2.
+        #print(Rib,zsl,z0m,z0h)
         
         while (abs(L - L0) > 0.001):
             L0      = L
@@ -793,8 +1490,8 @@ def ribtol(self, Rib, zsl, z0m, z0h):
                       - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
                                           (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
             L       = L - fx / fxdif
-
-            if(abs(L) > 1e15):
+            #print(L)
+            if(abs(L) > 1e12):
                 break
 
         return L
@@ -938,12 +1635,15 @@ def ags(self):
     def run_land_surface(self):
         # compute ra
         ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
+        #print('ueff',self.u,self.v,self.wstar)
 
         if(self.sw_sl):
           self.ra = (self.Cs * ueff)**-1.
         else:
           self.ra = ueff / max(1.e-3, self.ustar)**2.
 
+        #print('ra',self.ra,self.ustar,ueff)
+
         # first calculate essential thermodynamic variables
         self.esat    = esat(self.theta)
         self.qsat    = qsat(self.theta, self.Ps)
@@ -966,6 +1666,7 @@ def run_land_surface(self):
         self.rssoil = self.rssoilmin * f2 
  
         Wlmx = self.LAI * self.Wmax
+        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
         self.cliq = min(1., self.Wl / Wlmx) 
      
         # calculate skin temperature implictly
@@ -976,6 +1677,10 @@ def run_land_surface(self):
             / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
             + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
 
+        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        #print('Ts',self.rs)
+
         esatsurf      = esat(self.Ts)
         self.qsatsurf = qsat(self.Ts, self.Ps)
 
@@ -987,6 +1692,7 @@ def run_land_surface(self):
   
         self.LE     = self.LEsoil + self.LEveg + self.LEliq
         self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
+        #print('H',self.ra,self.Ts,self.theta)
         self.G      = self.Lambda * (self.Ts - self.Tsoil)
         self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
         self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
@@ -1003,6 +1709,7 @@ def run_land_surface(self):
   
         # calculate kinematic heat fluxes
         self.wtheta   = self.H  / (self.rho * self.cp)
+        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
         self.wq       = self.LE / (self.rho * self.Lv)
  
     def integrate_land_surface(self):
@@ -1018,14 +1725,29 @@ def integrate_land_surface(self):
     # store model output
     def store(self):
         t                      = self.t
-        self.out.t[t]          = t * self.dt / 3600. + self.tstart
+        
+        self.out.time[t]          = t * self.dt / 3600. + self.tstart
+
+        # in case we are at the end of the simulation, we store the vertical
+        # profiles to the output
+        
+        # if t == (len(self.out.time) - 1):
+        #     self.out.air_ac = self.air_ac
+        #     self.out.air_ap = self.air_ap
+
+        
+        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
+        #  for key in self.out.__dict__.keys():
+        #      if key in self.__dict__:
+        #          self.out.__dict__[key][t]  = self.__dict__[key]
+        
         self.out.h[t]          = self.h
         
-        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the following loop:
-        #   for key in list(self.out.__dict__.keys()):
-        #       self.out.__dict__[key][t] = self[key]
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
         
         self.out.gammatheta[t] = self.gammatheta
+        self.out.gammau[t]     = self.gammau
+        self.out.gammav[t]     = self.gammav
         self.out.gammaq[t]     = self.gammaq
         self.out.theta[t]      = self.theta
         self.out.thetav[t]     = self.thetav
@@ -1068,6 +1790,12 @@ def store(self):
         self.out.v2m[t]        = self.v2m
         self.out.e2m[t]        = self.e2m
         self.out.esat2m[t]     = self.esat2m
+
+
+        self.out.Tsoil[t]      = self.Tsoil
+        self.out.T2[t]         = self.T2
+        self.out.Ts[t]         = self.Ts
+        self.out.wg[t]         = self.wg
         
         self.out.thetasurf[t]  = self.thetasurf
         self.out.thetavsurf[t] = self.thetavsurf
@@ -1256,7 +1984,7 @@ def exitmodel(self):
 # class for storing mixed-layer model output data
 class model_output:
     def __init__(self, tsteps):
-        self.t          = np.zeros(tsteps)    # time [s]
+        self.time          = np.zeros(tsteps)    # time [s]
 
         # mixed-layer variables
         self.h          = np.zeros(tsteps)    # ABL height [m]
@@ -1264,6 +1992,8 @@ def __init__(self, tsteps):
         self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
         self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
         self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammau     = np.zeros(tsteps)
+        self.gammav     = np.zeros(tsteps)
         self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
         self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
         self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
@@ -1306,6 +2036,12 @@ def __init__(self, tsteps):
         self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
         self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
 
+        # ground variables
+        self.Tsoil       = np.zeros(tsteps)
+        self.T2          = np.zeros(tsteps)
+        self.Ts          = np.zeros(tsteps)
+        self.wg          = np.zeros(tsteps)
+
         # surface-layer variables
         self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
         self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
@@ -1346,11 +2082,19 @@ def __init__(self, tsteps):
         self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
         self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
         
+        
         self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
 
 # class for storing mixed-layer model input data
 class model_input:
     def __init__(self):
+
+        # # comment not valid
+        # we comment out the initialization, because there is a problem when
+        # inheriting values from one the another class4gl_iput. We also expect
+        # that the user specifies all the required parmameters (if not, an error
+        # is raised). 
+
         # general model variables
         self.runtime    = None  # duration of model run [s]
         self.dt         = None  # time step [s]
@@ -1365,9 +2109,9 @@ def __init__(self):
         self.fc         = None  # Coriolis parameter [s-1]
         
         self.theta      = None  # initial mixed-layer potential temperature [K]
-        self.theta_pro  = None  # optional/initial profile of potential temperature [K]
+        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
 
-        self.z_pro      = None  # height coordinate of the optional input profiles [m]
+        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
 
         self.dtheta     = None  # initial temperature jump at h [K]
         self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
@@ -1376,7 +2120,8 @@ def __init__(self):
         self.wtheta     = None  # surface kinematic heat flux [K m s-1]
         
         self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
-        self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
 
         self.dq         = None  # initial specific humidity jump at h [kg kg-1]
         self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
@@ -1464,6 +2209,6 @@ def __init__(self):
         self.dz_h       = None  # Transition layer thickness [m]
         
 # BEGIN -- HW 20171027
-        self.cala       = None      # soil heat conductivity [W/(K*m)]
-        self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+        # self.cala       = None      # soil heat conductivity [W/(K*m)]
+        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
 # END -- HW 20171027
diff --git a/ribtol_hw.py b/ribtol_hw.py
new file mode 100644
index 0000000..1946cc8
--- /dev/null
+++ b/ribtol_hw.py
@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan 12 10:46:20 2018
+
+@author: vsc42247
+"""
+
+
+
+# purpose of calc_cm_ch: calculate momentum and thermal turbulent diffusion coefficients of the surface layer with a non-iterative procedure (Wouters et al., 2012)
+
+# input:
+
+# zrib = bulk Richardson number = (g/T)* DT * z/(ua^2)
+#   with:
+#     g = 9.81 m/s2 the gravitational acceleration
+#     z = height (in meters) of the surface layer under consideration 
+#     T = (reference) temperature (in Kelvin) at height z 
+#     DT = (T - T_s) = temperature (in Kelvin) gradient between the surface and height z 
+#     u_a^2 = u^2 +  v^2 is the squared horizontal absolute wind speed 
+# zzz0m = ratio z/z0 between the height z and the momentum roughness length z0m
+# zkbm = ln(z0m/z0h), with z0m, z0h the momentum and thermal roughness length, respectively.
+
+# output: diffusion coefficients (CM and CH) which cna be used to determine surface-layer turbulent transport
+# u'w' = - CM ua^2.
+# w'T' = - CH ua DT 
+
+
+# Reference:
+# Wouters, H., De Ridder, K., and Lipzig, N. P. M.: Comprehensive
+# Parametrization of Surface-Layer Transfer Coefficients for Use
+# in Atmospheric Numerical Models, Bound.-Lay. Meteorol., 145,
+# 539–550, doi:10.1007/s10546-012-9744-3, 2012.
+
+import numpy as np
+
+def calc_cm_ch (zeta,zzz0m,zkbm):
+    krm = 0.4
+
+    #ZETA = zeta_hs2(zrib,zzz0m,zkbm)
+    FUNM,FUNH = funcsche(ZETA,zzz0m,zkbm)
+    CM = krm**2.0/FUNM/FUNM
+    CH = krm**2.0/FUNM/FUNH
+
+    # FUNMn,FUNHn = funcsche(0.,zzz0m,zkbm)
+    # CMn = krm**2.0/FUNMn/FUNMn
+    # CHn = krm**2.0/FUNMn/FUNHn
+
+    # print ZETA,FUNM,FUNH
+    # print 'CMCMN',CM/CMn
+    # print 'CHCHN',CH/CHn
+
+    return CM,CH
+
+
+def zeta_hs2(RiB,zzz0m,kBmin1):
+    #print(RiB,zzz0m,kBmin1)
+    mum=2.59
+    muh=0.95
+    nu=0.5
+    lam=1.5
+
+    betah = 5.0
+
+    zzz0h = zzz0m*np.exp(kBmin1)
+    zzzs = zzz0m*0.06 # to be changed!! r. 101 nog bekijken!!
+
+    L0M = np.log(zzz0m)
+    L0H = np.log(zzz0h)
+    facM = np.log(1.+lam/mum/zzzs)*np.exp(-mum*zzzs)/lam
+    facH = np.log(1.+lam/muh/zzzs)*np.exp(-muh*zzzs)/lam
+    L0Ms = L0M + facM 
+    L0Hs = L0H + facH
+
+    if RiB < 0.:
+        p = np.log(1.-RiB)
+        Q = -0.486 +0.219*p - 0.0331*p**2-4.93*np.exp(-L0H) - 3.65/L0H +\
+            0.38*p/L0H+ 14.8/L0H/L0H-0.946*p/L0H/L0H-10.0/L0H**3+ \
+            0.392*L0M/L0H-0.084*p*L0M/L0H+0.368*L0M/L0H/L0H
+        # print 'p: ',p
+        # print 'Q: ',Q
+        zeta = (1. + p*Q)* L0Ms**2/L0Hs * RiB
+    else:
+        betam = 4.76+7.03/zzz0m +0.24*zzz0m/zzz0h # to be changed
+        # betam = 5.0 + 1.59*10.**(-5.)*(np.exp(13.0-L0M)-1.0) \
+        #         +0.24*(np.exp(-kBmin1)-1.0) # to be changed!!
+        # print('betam',betam)
+        lL0M = np.log(L0M)
+        S0Ms = 1.-1./zzz0m + (1.+nu/mum/zzzs)*facM
+        S0Hs = 1.-1./zzz0h + (1.+nu/muh/zzzs)*facH
+        zetat = -0.316-0.515*np.exp(-L0H) + 25.8 *np.exp(-2.*L0H) + 4.36/L0H \
+                -6.39/L0H/L0H+0.834*lL0M - 0.0267*lL0M**2
+        # print('zetat',zetat)
+        RiBt = zetat *(L0Hs+ S0Hs*betah*zetat)/(L0Ms+S0Ms*betam*zetat)**2 
+        # print('RiBt',RiBt)
+
+        if (RiB > RiBt):
+            D = (L0Ms+S0Ms*betam*zetat)**3/\
+                (L0Ms*L0Hs+zetat*(2.*S0Hs * betah * L0Ms - S0Ms*betam*L0Hs))
+            zeta = zetat + D*(RiB-RiBt)
+        else:
+            r = RiB - S0Hs*betah/(S0Ms*betam)**2
+            B = S0Ms*betam*L0Hs- 2.*S0Hs*betah*L0Ms
+            C = 4.*(S0Ms*betam)**2 * L0Ms *(S0Hs*betah*L0Ms-S0Ms*betam*L0Hs)
+            zeta = - L0Ms / S0Ms/betam - B*C/(4.*(S0Ms*betam)**3 *(B**2+abs(C*r)))
+            if r != 0:
+                zeta = zeta + (B-np.sqrt(B**2+C*r) + B*C*r/(2.*(B**2+abs(C*r))))/(2.*(S0Ms*betam)**3*r)
+    # print('zeta',zeta)
+    return zeta
+
+def funcsche(zeta,zzz0,kBmin1):
+
+
+    mum=2.5
+    muh=0.9
+    nu=0.5
+    lam=1.5
+    
+    p2=3.141592/2.
+    
+    lnzzz0=np.log(zzz0)
+    zzzs=zzz0*0.06
+    zetamcorr=(1.+nu/(mum*zzzs))*zeta
+    zetam0=zeta/zzz0
+    zetahcorr=(1.+nu/(muh*zzzs))*zeta
+    zetah0=zeta/(zzz0*np.exp(kBmin1))
+    
+    if (zeta <= 0.):
+    
+        gamma=15.2
+        alfam=0.25
+        xx=(1.-gamma*zeta)**alfam
+        psim=2.*np.log((1.+xx)/2.)+np.log((1.+xx**2.)/2.)-2.*np.arctan(xx)+p2
+        xx0=(1.-gamma*zetam0)**alfam
+        psim0=2.*np.log((1.+xx0)/2.)+np.log((1.+xx0**2.)/2.)-2.*np.arctan(xx0)+p2
+        phimcorr=(1.-gamma*zetamcorr)**(-alfam)
+        
+        alfah=0.5
+        yy=(1.-gamma*zeta)**alfah
+        psih=2.*np.log((1.+yy)/2.)
+        yy0=(1.-gamma*zetah0)**alfah
+        psih0=2.*np.log((1.+yy0)/2.)
+        phihcorr=(1.-gamma*zetahcorr)**(-alfah)
+    else: 
+    
+        aa=6.1
+        bb=2.5
+        psim=-aa*np.log(zeta+(1.+zeta**bb)**(1./bb))
+        psim0=-aa*np.log(zetam0+(1.+zetam0**bb)**(1./bb))
+        phimcorr=1.+aa*(zetamcorr+zetamcorr**bb*(1.+zetamcorr**bb)**((1.-bb)/bb))/(zetamcorr+(1.+zetamcorr**bb)**(1./bb))
+        
+        cc=5.3
+        dd=1.1
+        psih=-cc*np.log(zeta+(1.+zeta**dd)**(1./dd))
+        psih0=-cc*np.log(zetah0+(1.+zetah0**dd)**(1./dd))
+        phihcorr=1.+cc*(zetahcorr+zetahcorr**dd*(1.+zetahcorr**dd)**((1.-dd)/dd))/(zetahcorr+(1.+zetahcorr**dd)**(1./dd))
+    
+    psistrm=phimcorr*(1./lam)*np.log(1.+lam/(mum*zzzs))*np.exp(-mum*zzzs)
+    psistrh=phihcorr*(1./lam)*np.log(1.+lam/(muh*zzzs))*np.exp(-muh*zzzs)
+    
+    funm=lnzzz0-psim+psim0 +psistrm
+    funh=lnzzz0+kBmin1-psih+psih0 +psistrh
+    return funm,funh
+

From 7b1c7bd6bf7faf8af52d5ff157518885bdce0579 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 20 Aug 2018 14:05:14 +0200
Subject: [PATCH 005/129] Create README.md

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 README.md

diff --git a/README.md b/README.md
new file mode 100644
index 0000000..2d11adc
--- /dev/null
+++ b/README.md
@@ -0,0 +1,4 @@
+# class4gl
+Chemistry Land-surface Atmosphere Soil Slab model (CLASS) | Python version
+
+This is the extension of class to be able to be used with global balloon soundings. 

From 23387b5165b8986531d5fac5026002a2f09991dc Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 20 Aug 2018 14:06:42 +0200
Subject: [PATCH 006/129] Create setup_global.py

---
 examples/setup_global.py | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 examples/setup_global.py

diff --git a/examples/setup_global.py b/examples/setup_global.py
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/examples/setup_global.py
@@ -0,0 +1 @@
+

From cdebdf0f8c870957e6b5a3b6009cdef2f30023e2 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 21 Aug 2018 21:30:24 +0200
Subject: [PATCH 007/129] initial commit

---
 MANIFEST                                      |   10 +
 bin/__init__.py                               |    7 +
 bin/setup/batch_setup_global.py               |   42 +
 bin/setup/setup_bllast.py                     |  719 ++++++
 bin/setup/setup_global.py                     |  310 +++
 bin/setup/setup_goamazon.py                   |  740 ++++++
 bin/setup/setup_humppa.py                     |  732 ++++++
 bin/setup/trash/setup_global_old.py           |  284 +++
 bin/simulations/batch_simulations.py          |   77 +
 bin/simulations/runmodel.py                   |  130 +
 bin/simulations/simulations.py                |  260 ++
 bin/simulations/simulations_iter.py           |  364 +++
 bin/simulations/simulations_iter_test.py      |  367 +++
 bin/simulations/trash/run_test.py             |  241 ++
 build/lib/bin/__init__.py                     |    7 +
 build/lib/lib/__init__.py                     |    7 +
 build/lib/lib/class4gl.py                     | 1611 ++++++++++++
 build/lib/lib/data_air.py                     |  473 ++++
 build/lib/lib/data_global.py                  |  936 +++++++
 build/lib/lib/interface_functions.py          |  506 ++++
 build/lib/lib/interface_multi.py              | 2061 +++++++++++++++
 build/lib/lib/model.py                        | 2214 +++++++++++++++++
 class4gl/__init__.py                          |    7 +
 class4gl/__pycache__/__init__.cpython-36.pyc  |  Bin 0 -> 376 bytes
 class4gl/__pycache__/class4gl.cpython-36.pyc  |  Bin 0 -> 28681 bytes
 class4gl/__pycache__/model.cpython-36.pyc     |  Bin 0 -> 36319 bytes
 class4gl/class4gl.py                          | 1611 ++++++++++++
 class4gl/data_air.py                          |  473 ++++
 class4gl/data_global.py                       |  936 +++++++
 class4gl/interface_functions.py               |  506 ++++
 class4gl/interface_multi.py                   | 2061 +++++++++++++++
 class4gl/model.py                             | 2214 +++++++++++++++++
 class4gl/ribtol/Makefile                      |    8 +
 class4gl/ribtol/MakefileMac                   |    9 +
 class4gl/ribtol/__init__.py                   |    7 +
 class4gl/ribtol/ribtol.cpp                    |   81 +
 class4gl/ribtol/ribtol.pyx                    |   48 +
 class4gl/ribtol/ribtol_hw.py                  |  165 ++
 class4gl/ribtol/setup.py                      |   12 +
 dist/class4gl-0.1dev.tar.gz                   |  Bin 0 -> 74685 bytes
 dist/class4gl-0.1dev/PKG-INFO                 |   14 +
 dist/class4gl-0.1dev/bin/__init__.py          |    7 +
 dist/class4gl-0.1dev/lib/__init__.py          |    7 +
 dist/class4gl-0.1dev/lib/class4gl.py          | 1611 ++++++++++++
 dist/class4gl-0.1dev/lib/data_air.py          |  473 ++++
 dist/class4gl-0.1dev/lib/data_global.py       |  936 +++++++
 .../lib/interface_functions.py                |  506 ++++
 dist/class4gl-0.1dev/lib/interface_multi.py   | 2061 +++++++++++++++
 dist/class4gl-0.1dev/lib/model.py             | 2214 +++++++++++++++++
 dist/class4gl-0.1dev/setup.py                 |    9 +
 examples/run_soundings/batch_run_soundings.py |   76 +
 examples/run_soundings/run.py                 |  264 ++
 examples/run_soundings/run_iter.py            |  364 +++
 examples/run_soundings/run_iter_test.py       |  367 +++
 examples/run_soundings/trash/run_test.py      |  241 ++
 examples/setup_global.py                      |    1 -
 examples/setup_soundings/setup_bllast.py      |  719 ++++++
 examples/setup_soundings/setup_global.py      |  310 +++
 examples/setup_soundings/setup_goamazon.py    |  740 ++++++
 examples/setup_soundings/setup_humppa.py      |  732 ++++++
 .../setup_soundings/trash/setup_global_old.py |  284 +++
 lib/class4gl.py                               | 1611 ++++++++++++
 lib/data_air.py                               |  473 ++++
 lib/data_global.py                            |  936 +++++++
 lib/interface_functions.py                    |  506 ++++
 lib/interface_multi.py                        | 2061 +++++++++++++++
 lib/model.py                                  | 2214 +++++++++++++++++
 lib/ribtol/Makefile                           |    8 +
 lib/ribtol/MakefileMac                        |    9 +
 lib/ribtol/ribtol.cpp                         |   81 +
 lib/ribtol/ribtol.pyx                         |   48 +
 lib/ribtol/ribtol_hw.py                       |  165 ++
 lib/ribtol/setup.py                           |   12 +
 setup.py                                      |   13 +-
 trash/data_ground.py                          |  393 +++
 75 files changed, 40693 insertions(+), 9 deletions(-)
 create mode 100644 MANIFEST
 create mode 100644 bin/__init__.py
 create mode 100644 bin/setup/batch_setup_global.py
 create mode 100644 bin/setup/setup_bllast.py
 create mode 100644 bin/setup/setup_global.py
 create mode 100644 bin/setup/setup_goamazon.py
 create mode 100644 bin/setup/setup_humppa.py
 create mode 100644 bin/setup/trash/setup_global_old.py
 create mode 100644 bin/simulations/batch_simulations.py
 create mode 100644 bin/simulations/runmodel.py
 create mode 100644 bin/simulations/simulations.py
 create mode 100644 bin/simulations/simulations_iter.py
 create mode 100644 bin/simulations/simulations_iter_test.py
 create mode 100644 bin/simulations/trash/run_test.py
 create mode 100644 build/lib/bin/__init__.py
 create mode 100644 build/lib/lib/__init__.py
 create mode 100644 build/lib/lib/class4gl.py
 create mode 100644 build/lib/lib/data_air.py
 create mode 100644 build/lib/lib/data_global.py
 create mode 100644 build/lib/lib/interface_functions.py
 create mode 100644 build/lib/lib/interface_multi.py
 create mode 100644 build/lib/lib/model.py
 create mode 100644 class4gl/__init__.py
 create mode 100644 class4gl/__pycache__/__init__.cpython-36.pyc
 create mode 100644 class4gl/__pycache__/class4gl.cpython-36.pyc
 create mode 100644 class4gl/__pycache__/model.cpython-36.pyc
 create mode 100644 class4gl/class4gl.py
 create mode 100644 class4gl/data_air.py
 create mode 100644 class4gl/data_global.py
 create mode 100644 class4gl/interface_functions.py
 create mode 100644 class4gl/interface_multi.py
 create mode 100644 class4gl/model.py
 create mode 100644 class4gl/ribtol/Makefile
 create mode 100644 class4gl/ribtol/MakefileMac
 create mode 100644 class4gl/ribtol/__init__.py
 create mode 100644 class4gl/ribtol/ribtol.cpp
 create mode 100644 class4gl/ribtol/ribtol.pyx
 create mode 100644 class4gl/ribtol/ribtol_hw.py
 create mode 100644 class4gl/ribtol/setup.py
 create mode 100644 dist/class4gl-0.1dev.tar.gz
 create mode 100644 dist/class4gl-0.1dev/PKG-INFO
 create mode 100644 dist/class4gl-0.1dev/bin/__init__.py
 create mode 100644 dist/class4gl-0.1dev/lib/__init__.py
 create mode 100644 dist/class4gl-0.1dev/lib/class4gl.py
 create mode 100644 dist/class4gl-0.1dev/lib/data_air.py
 create mode 100644 dist/class4gl-0.1dev/lib/data_global.py
 create mode 100644 dist/class4gl-0.1dev/lib/interface_functions.py
 create mode 100644 dist/class4gl-0.1dev/lib/interface_multi.py
 create mode 100644 dist/class4gl-0.1dev/lib/model.py
 create mode 100644 dist/class4gl-0.1dev/setup.py
 create mode 100644 examples/run_soundings/batch_run_soundings.py
 create mode 100644 examples/run_soundings/run.py
 create mode 100644 examples/run_soundings/run_iter.py
 create mode 100644 examples/run_soundings/run_iter_test.py
 create mode 100644 examples/run_soundings/trash/run_test.py
 delete mode 100644 examples/setup_global.py
 create mode 100644 examples/setup_soundings/setup_bllast.py
 create mode 100644 examples/setup_soundings/setup_global.py
 create mode 100644 examples/setup_soundings/setup_goamazon.py
 create mode 100644 examples/setup_soundings/setup_humppa.py
 create mode 100644 examples/setup_soundings/trash/setup_global_old.py
 create mode 100644 lib/class4gl.py
 create mode 100644 lib/data_air.py
 create mode 100644 lib/data_global.py
 create mode 100644 lib/interface_functions.py
 create mode 100644 lib/interface_multi.py
 create mode 100644 lib/model.py
 create mode 100644 lib/ribtol/Makefile
 create mode 100644 lib/ribtol/MakefileMac
 create mode 100644 lib/ribtol/ribtol.cpp
 create mode 100644 lib/ribtol/ribtol.pyx
 create mode 100644 lib/ribtol/ribtol_hw.py
 create mode 100644 lib/ribtol/setup.py
 create mode 100644 trash/data_ground.py

diff --git a/MANIFEST b/MANIFEST
new file mode 100644
index 0000000..534f2f4
--- /dev/null
+++ b/MANIFEST
@@ -0,0 +1,10 @@
+# file GENERATED by distutils, do NOT edit
+setup.py
+bin/__init__.py
+lib/__init__.py
+lib/class4gl.py
+lib/data_air.py
+lib/data_global.py
+lib/interface_functions.py
+lib/interface_multi.py
+lib/model.py
diff --git a/bin/__init__.py b/bin/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/bin/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/bin/setup/batch_setup_global.py b/bin/setup/batch_setup_global.py
new file mode 100644
index 0000000..4a3f623
--- /dev/null
+++ b/bin/setup/batch_setup_global.py
@@ -0,0 +1,42 @@
+
+
+''' 
+Purpose: 
+    launch array job to get sounding and other global forcing data in class4gl input format"
+Usage:
+    python start_setup_global.py
+
+Author:
+    Hendrik Wouters 
+
+'''
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if sys.argv[1] == 'qsub':
+# with qsub
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = len(STNlist) 
+print(PROCS)
+BATCHSIZE = math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
+# elif sys.argv[1] == 'wsub':
+#     
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
+
diff --git a/bin/setup/setup_bllast.py b/bin/setup/setup_bllast.py
new file mode 100644
index 0000000..af8c8bb
--- /dev/null
+++ b/bin/setup/setup_bllast.py
@@ -0,0 +1,719 @@
+# -*- coding: utf-8 -*-
+# Read data from BLLAST campaing and convert it to class4gl input
+
+# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
+#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 42.971834,
+                  "longitude" : 0.3671169,
+                  "name" : "the BLLAST experiment"
+                })
+current_station.name = 90001
+
+
+
+
+
+# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
+# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
+
+HOUR_FILES = \
+{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110619_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110620_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110625_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110702_1655_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
+}
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
+                                     #widths=[14]*19,
+                                     #skiprows=9,
+                                     #skipfooter=15,
+                                     #decimal='.',
+                                     #header=None,
+                                     #names = columns,
+                                     #na_values='-----')
+        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
+        print(air_balloon_in.columns)
+        rowmatches = {
+            't':      lambda x: x['TaRad']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['Press']*100.,
+            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
+            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
+            'z':      lambda x: x['Altitude'] -582.,
+            # from virtual temperature to absolute humidity
+            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+        # filter data so that potential temperature always increases with
+        # height 
+        cols = []
+        for column in air_ap_tail.columns:
+            #if column != 'z':
+                cols.append(column)
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        # 
+        # # we copy the pressure at ground level from balloon sounding. The
+        # # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
+        # write local solar time, we need to assign the timezone to UTC (which
+        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
+        # tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise']+dt.timedelta(hours=2))\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        print('ldatetime_daylight',dpars['ldatetime_daylight'])
+        print('ldatetime',dpars['ldatetime'])
+        print('lSunrise',dpars['lSunrise'])
+        dpars['day'] = dpars['ldatetime'].day
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        print('tstart',dpars['tstart'])
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='bllast',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
+    
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+
+    
+    c4gli_morning.pars.sw_ac = []
+    c4gli_morning.pars.sw_ap = True
+    c4gli_morning.pars.sw_lit = False
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/bin/setup/setup_global.py b/bin/setup/setup_global.py
new file mode 100644
index 0000000..79224d9
--- /dev/null
+++ b/bin/setup/setup_global.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+    one_run = False
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+                
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            try:
+                c4gli.get_profile_wyoming(wy_strm)
+                #print(STN['ID'],c4gli.pars.datetime)
+                #c4gli.get_global_input(globaldata)
+
+                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+                logic = dict()
+                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+                logic['daylight'] = \
+                    ((c4gli.pars.ldatetime_daylight - 
+                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
+                
+                logic['springsummer'] = (c4gli.pars.theta > 278.)
+                
+                # we take 3000 because previous analysis (ie., HUMPPA) has
+                # focussed towards such altitude
+                le3000 = (c4gli.air_balloon.z <= 3000.)
+                logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+
+                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+                
+                print('logic:', logic)
+                # the result
+                morning_ok = np.mean(list(logic.values()))
+                print(morning_ok,c4gli.pars.ldatetime)
+
+            except:
+                morning_ok =False
+                print('obtain morning not good')
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                print('MORNING OK!')
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                print('AFTERNOON PROFILE CLEARED')
+                try:
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+                    print('AFTERNOON PROFILE OK')
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # we will dump the latest afternoon sounding that fits the
+                    # minimum criteria specified by logic_afternoon
+                    print(current_date,current_date_afternoon)
+                    c4gli_afternoon_for_dump = None
+                    while ((current_date_afternoon == current_date) and \
+                           (wy_strm.current is not None)):
+                        logic_afternoon =dict()
+
+                        logic_afternoon['afternoon'] = \
+                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        logic_afternoon['daylight'] = \
+                          ((c4gli_afternoon.pars.ldatetime - \
+                            c4gli_afternoon.pars.ldatetime_daylight \
+                           ).total_seconds()/3600. <= 0.)
+
+
+                        le3000_afternoon = \
+                            (c4gli_afternoon.air_balloon.z <= 3000.)
+                        logic_afternoon['5measurements'] = \
+                            (np.sum(le3000_afternoon) >= 5) 
+
+                        # we only store the last afternoon sounding that fits these
+                        # minimum criteria
+
+                        afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                        print('logic_afternoon: ',logic_afternoon)
+                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                        if afternoon_ok == 1.:
+                            # # doesn't work :(
+                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                            
+                            # so we just create a new one from the same wyoming profile
+                            c4gli_afternoon_for_dump = class4gl_input()
+                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                        wy_strm.find_next()
+                        c4gli_afternoon.clear()
+                        c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                        if wy_strm.current is not None:
+                            current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                        else:
+                            # a dummy date: this will be ignored anyway
+                            current_date_afternoon = dt.date(1900,1,1)
+
+                        # Only in the case we have a good pair of soundings, we
+                        # dump them to disk
+                    if c4gli_afternoon_for_dump is not None:
+                        c4gli.update(source='pairs',pars={'runtime' : \
+                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                                 c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                        print('ALMOST...')
+                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                                
+        
+                            c4gli.get_global_input(globaldata)
+                            print('VERY CLOSE...')
+                            if c4gli.check_source_globaldata() and \
+                                (c4gli.check_source(source='wyoming',\
+                                                   check_only_sections='pars')):
+                                c4gli.dump(fileout)
+                                
+                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                
+                                
+                                # for keyEXP,dictEXP in experiments.items():
+                                #     
+                                #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                #     c4gl = class4gl(c4gli)
+                                #     # c4gl.run()
+                                #     
+                                #     c4gl.dump(c4glfiles[key])
+                                
+                                print('HIT!!!')
+                                one_run = True
+                except:
+                    print('get profile failed')
+                
+    if one_run:
+        STN.name = STN['ID']
+        all_records_morning = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='morning',
+                                      refetch_records=True,
+                                      )
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    else:
+        os.system('rm '+fnout)
+        os.system('rm '+fnout_afternoon)
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/bin/setup/setup_goamazon.py b/bin/setup/setup_goamazon.py
new file mode 100644
index 0000000..f9efe2c
--- /dev/null
+++ b/bin/setup/setup_goamazon.py
@@ -0,0 +1,740 @@
+# -*- coding: utf-8 -*-
+
+import xarray as xr
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+import glob
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : -3.21,
+                  "longitude" : -60.6,
+                  "name" : "the GOAMAZON experiment"
+                })
+current_station.name = 90002
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
+DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
+
+
+DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
+HOUR_FILES = {}
+for iDT, DT in enumerate(DTS):
+    morning_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
+    if len(possible_files)>0:
+        morning_file= possible_files[0]
+    afternoon_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
+    if len(possible_files)>0:
+        afternoon_file= possible_files[0]
+
+    if (morning_file is not None) and (afternoon_file is not None):
+        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
+                          'afternoon':[17.5,afternoon_file]}
+
+print(HOUR_FILES)
+
+# HOUR_FILES = \
+# {
+#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
+#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+# }
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
+        print(balloon_file)
+        
+        xrin = balloon_file
+        air_balloon = pd.DataFrame()
+
+        air_balloon['t'] = xrin.tdry.values+273.15
+        air_balloon['p'] = xrin.pres.values*100.
+        
+        air_balloon['u'] = xrin.u_wind.values
+        air_balloon['v'] = xrin.v_wind.values
+        air_balloon['WSPD'] = xrin['wspd'].values
+        
+        print(xrin.rh.values.shape)
+        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
+        
+
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        # air_balloon_in = pd.read_fwf(balloon_file,
+        #                              widths=[14]*19,
+        #                              skiprows=9,
+        #                              skipfooter=15,
+        #                              decimal=',',
+        #                              header=None,
+        #                              names = columns,
+        #                              na_values='-----')
+    
+
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
+            'rho': lambda x: x.p /x.t / x.R ,
+        }
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        print('alt in xrin?:','alt' in xrin)
+        if 'alt' in xrin:
+            air_balloon['z'] = xrin.alt.values
+        else:
+            air_balloon['z'] = 0.
+            for irow,row in air_balloon.iloc[1:].iterrows():
+                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
+                        
+             
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn =pair['morning'][1]
+    print(humpafn)
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn = pair['afternoon'][1]
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+"""
+stations_for_iter = stations(path_exp)
+for STNID,station in stations_iterator(stations_for_iter):
+    records_current_station_index = \
+            (records_ini.index.get_level_values('STNID') == STNID)
+    file_current_station_mod = STNID
+
+    with \
+    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        for (STNID,index),record_ini in records_iterator(records_ini):
+            c4gli_ini = get_record_yaml(file_station_ini, 
+                                        record_ini.index_start, 
+                                        record_ini.index_end,
+                                        mode='ini')
+            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+
+            record_mod = records_mod.loc[(STNID,index)]
+            c4gl_mod = get_record_yaml(file_station_mod, 
+                                        record_mod.index_start, 
+                                        record_mod.index_end,
+                                        mode='mod')
+            record_afternoon = records_afternoon.loc[(STNID,index)]
+            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+                                        record_afternoon.index_start, 
+                                        record_afternoon.index_end,
+                                        mode='ini')
+"""
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/bin/setup/setup_humppa.py b/bin/setup/setup_humppa.py
new file mode 100644
index 0000000..ff37628
--- /dev/null
+++ b/bin/setup/setup_humppa.py
@@ -0,0 +1,732 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 61.8448,
+                  "longitude" : 24.2882,
+                  "name" : "the HUMMPA experiment"
+                })
+current_station.name = 90000
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+
+HOUR_FILES = \
+{ dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
+  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[18,'humppa_071310_1800.txt']},
+  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
+  dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
+  dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
+  dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
+  dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
+  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[21,'humppa_071910_2100.txt']},
+#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
+  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
+  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[18,'humppa_072210_1800.txt']},
+ # something is wrong with ths profile
+ # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
+  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
+  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
+  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
+  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
+  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
+  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[18,'humppa_072910_1800.txt']},
+  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
+  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[15,'humppa_073110_1500.txt']},
+  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
+  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[18,'humppa_080210_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
+  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
+  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
+  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
+  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
+  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[18,'humppa_081010_1800.txt']},
+}
+
+
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_fwf(balloon_file,
+                                     widths=[14]*19,
+                                     skiprows=9,
+                                     skipfooter=15,
+                                     decimal=',',
+                                     header=None,
+                                     names = columns,
+                                     na_values='-----')
+    
+        rowmatches = {
+            't':      lambda x: x['T[C]']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['P[hPa]']*100.,
+            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'z':      lambda x: x['Altitude[m]'],
+            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/bin/setup/trash/setup_global_old.py b/bin/setup/trash/setup_global_old.py
new file mode 100644
index 0000000..d812684
--- /dev/null
+++ b/bin/setup/trash/setup_global_old.py
@@ -0,0 +1,284 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            c4gli.get_profile_wyoming(wy_strm)
+            #print(STN['ID'],c4gli.pars.datetime)
+            #c4gli.get_global_input(globaldata)
+
+            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+            logic = dict()
+            logic['morning'] =  (c4gli.pars.ldatetime.hour < 12.)
+            logic['daylight'] = \
+                ((c4gli.pars.ldatetime_daylight - 
+                  c4gli.pars.ldatetime).total_seconds()/3600. <= 5.)
+            
+            logic['springsummer'] = (c4gli.pars.theta > 278.)
+            
+            # we take 3000 because previous analysis (ie., HUMPPA) has
+            # focussed towards such altitude
+            le3000 = (c4gli.air_balloon.z <= 3000.)
+            logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+            try:
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+            except:
+                logic['mlerrlow'] = False
+                print('rmse probably failed')
+
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+            
+            print('logic:', logic)
+            # the result
+            morning_ok = np.mean(list(logic.values()))
+            print(morning_ok,c4gli.pars.ldatetime)
+            
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                if wy_strm.current is not None:
+                    current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                else:
+                    # a dummy date: this will be ignored anyway
+                    current_date_afternoon = dt.date(1900,1,1)
+
+                # we will dump the latest afternoon sounding that fits the
+                # minimum criteria specified by logic_afternoon
+                c4gli_afternoon_for_dump = None
+                while ((current_date_afternoon == current_date) and \
+                       (wy_strm.current is not None)):
+                    logic_afternoon =dict()
+
+                    logic_afternoon['afternoon'] = \
+                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                    logic_afternoon['daylight'] = \
+                      ((c4gli_afternoon.pars.ldatetime - \
+                        c4gli_afternoon.pars.ldatetime_daylight \
+                       ).total_seconds()/3600. <= 2.)
+
+
+                    le3000_afternoon = \
+                        (c4gli_afternoon.air_balloon.z <= 3000.)
+                    logic_afternoon['5measurements'] = \
+                        (np.sum(le3000_afternoon) >= 5) 
+
+                    # we only store the last afternoon sounding that fits these
+                    # minimum criteria
+
+                    afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                    print('logic_afternoon: ',logic_afternoon)
+                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                    if afternoon_ok == 1.:
+                        # # doesn't work :(
+                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                        
+                        # so we just create a new one from the same wyoming profile
+                        c4gli_afternoon_for_dump = class4gl_input()
+                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                    wy_strm.find_next()
+                    c4gli_afternoon.clear()
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # Only in the case we have a good pair of soundings, we
+                    # dump them to disk
+                if c4gli_afternoon_for_dump is not None:
+                    c4gli.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                             c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                    print('ALMOST...')
+                    if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                            
+        
+                        c4gli.get_global_input(globaldata)
+                        print('VERY CLOSE...')
+                        if c4gli.check_source_globaldata() and \
+                            (c4gli.check_source(source='wyoming',\
+                                               check_only_sections='pars')):
+                            c4gli.dump(fileout)
+                            
+                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                            
+                            
+                            # for keyEXP,dictEXP in experiments.items():
+                            #     
+                            #     c4gli.update(source=keyEXP,pars = dictEXP)
+                            #     c4gl = class4gl(c4gli)
+                            #     # c4gl.run()
+                            #     
+                            #     c4gl.dump(c4glfiles[key])
+                            
+                            print('HIT!!!')
+                
+                
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/bin/simulations/batch_simulations.py b/bin/simulations/batch_simulations.py
new file mode 100644
index 0000000..b5d4cc3
--- /dev/null
+++ b/bin/simulations/batch_simulations.py
@@ -0,0 +1,77 @@
+
+import argparse
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if 'path-soundings' in args.__dict__.keys():
+#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+# else:
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
+    parser.add_argument('--exec')
+    parser.add_argument('--experiments')#should be ';'-seperated list
+    parser.add_argument('--split-by',default=-1)
+    args = parser.parse_args()
+
+experiments = args.experiments.split(';')
+#SET = 'GLOBAL'
+SET = args.dataset
+print(args.experiments)
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+for expname in experiments:
+    #exp = EXP_DEFS[expname]
+    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+    os.system('rm -R '+path_exp)
+
+totalchunks = 0
+for istation,current_station in all_stations.iterrows():
+    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
+    totalchunks +=chunks_current_station
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
+                                       ',split_by='+str(args.split_by)+\
+                                       ',exec='+str(args.exec)+\
+                                       ',experiments='+str(args.experiments))
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/bin/simulations/runmodel.py b/bin/simulations/runmodel.py
new file mode 100644
index 0000000..fc4fd19
--- /dev/null
+++ b/bin/simulations/runmodel.py
@@ -0,0 +1,130 @@
+#
+# Example of how to run the Python code, and access the output
+# This case is identical to the default setup of CLASS (the version with interface) 
+#
+
+from pylab import *
+from model import *
+
+""" 
+Create empty model_input and set up case
+"""
+run1input = model_input()
+
+run1input.dt         = 60.       # time step [s]
+run1input.runtime    = 12*3600    # total run time [s]
+
+# mixed-layer input
+run1input.sw_ml      = True      # mixed-layer model switch
+run1input.sw_shearwe = False     # shear growth mixed-layer switch
+run1input.sw_fixft   = False     # Fix the free-troposphere switch
+run1input.h          = 200.      # initial ABL height [m]
+run1input.Ps         = 101300.   # surface pressure [Pa]
+run1input.divU       = 0.        # horizontal large-scale divergence of wind [s-1]
+run1input.fc         = 1.e-4     # Coriolis parameter [m s-1]
+
+run1input.theta      = 288.      # initial mixed-layer potential temperature [K]
+run1input.dtheta     = 1.        # initial temperature jump at h [K]
+run1input.gammatheta = 0.006     # free atmosphere potential temperature lapse rate [K m-1]
+run1input.advtheta   = 0.        # advection of heat [K s-1]
+run1input.beta       = 0.2       # entrainment ratio for virtual heat [-]
+run1input.wtheta     = 0.1       # surface kinematic heat flux [K m s-1]
+
+run1input.q          = 0.008     # initial mixed-layer specific humidity [kg kg-1]
+run1input.dq         = -0.001    # initial specific humidity jump at h [kg kg-1]
+run1input.gammaq     = 0.        # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+run1input.advq       = 0.        # advection of moisture [kg kg-1 s-1]
+run1input.wq         = 0.1e-3    # surface kinematic moisture flux [kg kg-1 m s-1]
+
+run1input.CO2        = 422.      # initial mixed-layer CO2 [ppm]
+run1input.dCO2       = -44.      # initial CO2 jump at h [ppm]
+run1input.gammaCO2   = 0.        # free atmosphere CO2 lapse rate [ppm m-1]
+run1input.advCO2     = 0.        # advection of CO2 [ppm s-1]
+run1input.wCO2       = 0.        # surface kinematic CO2 flux [ppm m s-1]
+
+run1input.sw_wind    = False     # prognostic wind switch
+run1input.u          = 6.        # initial mixed-layer u-wind speed [m s-1]
+run1input.du         = 4.        # initial u-wind jump at h [m s-1]
+run1input.gammau     = 0.        # free atmosphere u-wind speed lapse rate [s-1]
+run1input.advu       = 0.        # advection of u-wind [m s-2]
+
+run1input.v          = -4.0      # initial mixed-layer u-wind speed [m s-1]
+run1input.dv         = 4.0       # initial u-wind jump at h [m s-1]
+run1input.gammav     = 0.        # free atmosphere v-wind speed lapse rate [s-1]
+run1input.advv       = 0.        # advection of v-wind [m s-2]
+
+run1input.sw_sl      = False     # surface layer switch
+run1input.ustar      = 0.3       # surface friction velocity [m s-1]
+run1input.z0m        = 0.02      # roughness length for momentum [m]
+run1input.z0h        = 0.002     # roughness length for scalars [m]
+
+run1input.sw_rad     = False     # radiation switch
+run1input.lat        = 51.97     # latitude [deg]
+run1input.lon        = -4.93     # longitude [deg]
+run1input.doy        = 268.      # day of the year [-]
+run1input.tstart     = 6.8       # time of the day [h UTC]
+run1input.cc         = 0.0       # cloud cover fraction [-]
+run1input.Q          = 400.      # net radiation [W m-2] 
+run1input.dFz        = 0.        # cloud top radiative divergence [W m-2] 
+
+run1input.sw_ls      = False     # land surface switch
+run1input.ls_type    = 'js'      # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+run1input.wg         = 0.21      # volumetric water content top soil layer [m3 m-3]
+run1input.w2         = 0.21      # volumetric water content deeper soil layer [m3 m-3]
+run1input.cveg       = 0.85      # vegetation fraction [-]
+run1input.Tsoil      = 285.      # temperature top soil layer [K]
+run1input.T2         = 286.      # temperature deeper soil layer [K]
+run1input.a          = 0.219     # Clapp and Hornberger retention curve parameter a
+run1input.b          = 4.90      # Clapp and Hornberger retention curve parameter b
+run1input.p          = 4.        # Clapp and Hornberger retention curve parameter c
+run1input.CGsat      = 3.56e-6   # saturated soil conductivity for heat
+
+run1input.wsat       = 0.472     # saturated volumetric water content ECMWF config [-]
+run1input.wfc        = 0.323     # volumetric water content field capacity [-]
+run1input.wwilt      = 0.171     # volumetric water content wilting point [-]
+
+run1input.C1sat      = 0.132     
+run1input.C2ref      = 1.8
+
+run1input.LAI        = 2.        # leaf area index [-]
+run1input.gD         = 0.0       # correction factor transpiration for VPD [-]
+run1input.rsmin      = 110.      # minimum resistance transpiration [s m-1]
+run1input.rssoilmin  = 50.       # minimun resistance soil evaporation [s m-1]
+run1input.alpha      = 0.25      # surface albedo [-]
+
+run1input.Ts         = 290.      # initial surface temperature [K]
+
+run1input.Wmax       = 0.0002    # thickness of water layer on wet vegetation [m]
+run1input.Wl         = 0.0000    # equivalent water layer depth for wet vegetation [m]
+
+run1input.Lambda     = 5.9       # thermal diffusivity skin layer [-]
+
+run1input.c3c4       = 'c3'      # Plant type ('c3' or 'c4')
+
+run1input.sw_cu      = False     # Cumulus parameterization switch
+run1input.dz_h       = 150.      # Transition layer thickness [m]
+
+"""
+Init and run the model
+"""
+r1 = model(run1input)
+r1.run()
+
+"""
+Plot output
+"""
+figure()
+subplot(131)
+plot(r1.out.t, r1.out.h)
+xlabel('time [h]')
+ylabel('h [m]')
+
+subplot(132)
+plot(r1.out.t, r1.out.theta)
+xlabel('time [h]')
+ylabel('theta [K]')
+
+subplot(133)
+plot(r1.out.t, r1.out.q*1000.)
+xlabel('time [h]')
+ylabel('q [g kg-1]')
diff --git a/bin/simulations/simulations.py b/bin/simulations/simulations.py
new file mode 100644
index 0000000..719f9a5
--- /dev/null
+++ b/bin/simulations/simulations.py
@@ -0,0 +1,260 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--first-station-row')
+parser.add_argument('--last-station-row')
+parser.add_argument('--station-id') # run a specific station id
+parser.add_argument('--dataset')
+parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--error-handling',default='dump_on_success')
+parser.add_argument('--experiments')
+parser.add_argument('--split-by',default=-1)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+path_soundingsSET = args.path_soundings+'/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iter = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iter.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if args.station_id is not None:
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iterator.set_STNID(STNID)
+        run_stations = pd.DataFrame(run_station)
+    else:
+        run_stations = pd.DataFrame(all_stations)
+        if args.last_station_row is not None:
+            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            run_stations = run_stations.iloc[int(args.first_station):]
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_succes':
+                        try:
+                            c4gl.run()
+                            print('run not succesfull')
+                            onerun = True
+
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/bin/simulations/simulations_iter.py b/bin/simulations/simulations_iter.py
new file mode 100644
index 0000000..5dfbaff
--- /dev/null
+++ b/bin/simulations/simulations_iter.py
@@ -0,0 +1,364 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk',default=0)
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if 'global_chunk' in args.__dict__.keys():
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if 'last_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    
+    if 'first_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    if 'station_chunk' in args.__dict__.keys():
+        run_station_chunk = args.station_chunk
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+    for istation,current_station in run_stations.iterrows():
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                #if iexp == 11:
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+
+                        c4gli_morning.pars.itersteps = i
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                   #   timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    except:
+                        print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/bin/simulations/simulations_iter_test.py b/bin/simulations/simulations_iter_test.py
new file mode 100644
index 0000000..eefd475
--- /dev/null
+++ b/bin/simulations/simulations_iter_test.py
@@ -0,0 +1,367 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+                #if iexp == 11:
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+                        onerun = True
+
+                        c4gli_morning.pars.itersteps = i
+                    except:
+                        print('run not succesfull')
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                                  include_input=False,\
+                               #   timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/bin/simulations/trash/run_test.py b/bin/simulations/trash/run_test.py
new file mode 100644
index 0000000..767d960
--- /dev/null
+++ b/bin/simulations/trash/run_test.py
@@ -0,0 +1,241 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    parser.add_argument('--c4gl-path',default='')
+    args = parser.parse_args()
+
+if args.c4gl_path == '': 
+    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+else:
+    sys.path.insert(0, args.c4gl_path)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    try:
+                        c4gl.run()
+                    except:
+                        print('run not succesfull')
+                    onerun = True
+
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                              include_input=False,\
+                              #timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/build/lib/bin/__init__.py b/build/lib/bin/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/build/lib/bin/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/build/lib/lib/__init__.py b/build/lib/lib/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/build/lib/lib/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/build/lib/lib/class4gl.py b/build/lib/lib/class4gl.py
new file mode 100644
index 0000000..7baaa51
--- /dev/null
+++ b/build/lib/lib/class4gl.py
@@ -0,0 +1,1611 @@
+# -*- coding: utf-8 -*-
+
+"""
+
+Created on Mon Jan 29 12:33:51 2018
+
+Module file for class4gl, which  extents the class-model to be able to take
+global air profiles as input. It exists of:
+
+CLASSES:
+    - an input object, namely class4gl_input. It includes:
+        - a function to read Wyoming sounding data from a yyoming stream object
+        - a function to read global data from a globaldata library object 
+    - the model object: class4gl
+    - ....    
+
+DEPENDENCIES:
+    - xarray
+    - numpy
+    - data_global
+    - Pysolar
+    - yaml
+
+@author: Hendrik Wouters
+
+"""
+
+
+
+""" Setup of envirnoment """
+
+# Standard modules of the stand class-boundary-layer model
+from model import model
+from model import model_output as class4gl_output
+from model import model_input
+from model import qsat
+#from data_soundings import wyoming 
+import Pysolar
+import yaml
+import logging
+import warnings
+import pytz
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+
+# Generic Python Packages
+import numpy as np
+import datetime as dt
+import pandas as pd
+import xarray as xr
+import io
+#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
+from data_global import data_global
+grav = 9.81
+
+# this is just a generic input object
+class generic_input(object):
+    def __init__(self):
+        self.init = True
+
+
+# all units from all variables in CLASS(4GL) should be defined here!
+units = {
+         'h':'m',
+         'theta':'K', 
+         'q':'kg/kg',
+         'cc': '-',
+         'cveg': '-',
+         'wg': 'm3 m-3',
+         'w2': 'm3 m-3',
+         #'wg': 'kg/kg',
+         'Tsoil': 'K',
+         'T2': 'K',
+         'z0m': 'm',
+         'alpha': '-',
+         'LAI': '-',
+         'dhdt':'m/h',
+         'dthetadt':'K/h',
+         'dqdt':'kg/kg/h',
+         'BR': '-',
+         'EF': '-',
+}
+
+class class4gl_input(object):
+# this was the way it was defined previously.
+#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+
+    def __init__(self,set_pars_defaults=True,debug_level=None):
+
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        print('hello')
+        self.logger = logging.getLogger('class4gl_input')
+        print(self.logger)
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # # create logger
+        # self.logger = logging.getLogger('class4gl_input')
+        # self.logger.setLevel(debug_level)
+
+        # # create console handler and set level to debug
+        # ch = logging.StreamHandler()
+        # ch.setLevel(debug_level)
+
+        # # create formatter
+        # formatter = logging.Formatter('%(asctime)s - \
+        #                                %(name)s - \
+        #                                %(levelname)s - \
+        #                                %(message)s')
+        # add formatter to ch
+        # ch.setFormatter(formatter)
+     
+        # # add ch to logger
+        # self.logger.addHandler(ch)
+
+        # """ end set up logger """
+
+
+
+        # these are the standard model input single-value parameters for class
+        self.pars = model_input()
+
+        # diagnostic parameters of the initial profile
+        self.diag = dict()
+
+        # In this variable, we keep track of the different parameters from where it originates from. 
+        self.sources = {}
+
+        if set_pars_defaults:
+            self.set_pars_defaults()
+
+    def set_pars_defaults(self):
+
+        """ 
+        Create empty model_input and set up case
+        """
+        defaults = dict( 
+        dt         = 60.    , # time step [s] 
+        runtime    = 6*3600 ,  # total run time [s]
+        
+        # mixed-layer input
+        sw_ml      = True   ,  # mixed-layer model switch
+        sw_shearwe = False  ,  # shear growth mixed-layer switch
+        sw_fixft   = False  ,  # Fix the free-troposphere switch
+        h          = 200.   ,  # initial ABL height [m]
+        Ps         = 101300.,  # surface pressure [Pa]
+        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
+        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
+        
+        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
+        dtheta     = 1.     ,  # initial temperature jump at h [K]
+        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        advtheta   = 0.     ,  # advection of heat [K s-1]
+        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
+        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
+        
+        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
+        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
+        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
+        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
+        
+        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
+        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
+        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
+        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
+        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
+        sw_wind    = True  ,  # prognostic wind switch
+        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
+        du         = 0.     ,  # initial u-wind jump at h [m s-1]
+        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
+        advu       = 0.     ,  # advection of u-wind [m s-2]
+        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
+        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
+        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
+        advv       = 0.     ,  # advection of v-wind [m s-2]
+        sw_sl      = True   , # surface layer switch
+        ustar      = 0.3    ,  # surface friction velocity [m s-1]
+        z0m        = 0.02   ,  # roughness length for momentum [m]
+        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
+        sw_rad     = True   , # radiation switch
+        lat        = 51.97  ,  # latitude [deg]
+        lon        = -4.93  ,  # longitude [deg]
+        doy        = 268.   ,  # day of the year [-]
+        tstart     = 6.8    ,  # time of the day [h UTC]
+        cc         = 0.0    ,  # cloud cover fraction [-]
+        Q          = 400.   ,  # net radiation [W m-2] 
+        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
+        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
+        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
+        cveg       = 0.85   ,  # vegetation fraction [-]
+        Tsoil      = 295.   ,  # temperature top soil layer [K]
+        Ts         = 295.   ,    # initial surface temperature [K]
+        T2         = 296.   ,  # temperature deeper soil layer [K]
+        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
+        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
+        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
+        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
+        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
+        wfc        = 0.323  ,  # volumetric water content field capacity [-]
+        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
+        C1sat      = 0.132  ,  
+        C2ref      = 1.8    ,
+        LAI        = 2.     ,  # leaf area index [-]
+        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
+        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
+        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
+        alpha      = 0.25   ,  # surface albedo [-]
+        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
+        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
+        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
+        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
+        sw_cu      = False  ,  # Cumulus parameterization switch
+        dz_h       = 150.   ,  # Transition layer thickness [m]
+        cala       = None   ,  # soil heat conductivity [W/(K*m)]
+        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
+        sw_ls      = True   ,
+        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
+        sw_lit     = False,
+        )
+        pars = model_input()
+        for key in defaults:
+            pars.__dict__[key] = defaults[key]
+        
+        self.update(source='defaults',pars=pars)
+        
+    def clear(self):
+        """ this procudure clears the class4gl_input """
+
+        for key in list(self.__dict__.keys()):
+            del(self.__dict__[key])
+        self.__init__()
+
+    def dump(self,file):
+        """ this procedure dumps the class4gl_input object into a yaml file
+            
+            Input: 
+                - self.__dict__ (internal): the dictionary from which we read 
+            Output:
+                - file: All the parameters in self.__init__() are written to
+                the yaml file, including pars, air_ap, sources etc.
+        """
+        file.write('---\n')
+        index = file.tell()
+        file.write('# CLASS4GL input; format version: 0.1\n')
+
+        # write out the position of the current record
+        yaml.dump({'index':index}, file, default_flow_style=False)
+
+        # we do not include the none values
+        for key,data in self.__dict__.items():
+            #if ((type(data) == model_input) or (type(class4gl_input):
+            if key == 'pars':
+
+                pars = {'pars' : self.__dict__['pars'].__dict__}
+                parsout = {}
+                for key in pars.keys():
+                    if pars[key] is not None:
+                        parsout[key] = pars[key]
+
+                yaml.dump(parsout, file, default_flow_style=False)
+            elif type(data) == dict:
+                if key == 'sources':
+                    # in case of sources, we want to have a
+                    # condensed list format as well, so we leave out
+                    # 'default_flow_style=False'
+                    yaml.dump({key : data}, file)
+                else: 
+                    yaml.dump({key : data}, file,
+                              default_flow_style=False)
+            elif type(data) == pd.DataFrame:
+                # in case of dataframes (for profiles), we want to have a
+                # condensed list format as well, so we leave out
+                # 'default_flow_style=False'
+                yaml.dump({key: data.to_dict(orient='list')},file)
+
+                # # these are trials to get it into a more human-readable
+                # fixed-width format, but it is too complex
+                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
+                #file.write(stream)
+                
+                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
+                #file.write(key+': !!str |\n')
+                #file.write(str(data)+'\n')
+       
+    def load_yaml_dict(self,yaml_dict,reset=True):
+        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
+            
+            Input: 
+                - yaml_dict: the dictionary from which we read 
+                - reset: reset data before reading        
+            Output:
+                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
+        """
+        
+        if reset:
+            for key in list(self.__dict__.keys()):
+                del(self.__dict__[key])
+            self.__init__()
+
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                self.__dict__[key] = model_input()
+                self.__dict__[key].__dict__ = data
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            elif key == 'sources':
+                self.__dict__[key] = data
+            elif key == 'diag':
+                self.__dict__[key] = data
+            else: 
+                warnings.warn("Key '"+key+"' may not be implemented.")
+                self.__dict__[key] = data
+
+    def update(self,source,**kwargs):
+        """ this procedure is to make updates of input parameters and tracking
+        of their source more convenient. It implements the assignment of
+        parameter source/sensitivity experiment IDs ('eg.,
+        'defaults', 'sounding balloon', any satellite information, climate
+        models, sensitivity tests etc.). These are all stored in a convenient
+        way with as class4gl_input.sources.  This way, the user can always consult with
+        from where parameters data originates from.  
+        
+        Input:
+            - source:    name of the underlying dataset
+            - **kwargs: a dictionary of data input, for which the key values
+            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
+            the values is a again a dictionary/dataframe of datakeys/columns
+            ('wg','PRES','datetime', ...) and datavalues (either single values,
+            profiles ...), eg., 
+
+                pars = {'wg': 0.007  , 'w2', 0.005}
+                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
+                                     300.,...]}
+            
+        Output:
+            - self.__dict__[datatype] : object to which the parameters are
+                                        assigned. They can be consulted with
+                                        self.pars, self.profiles, etc.
+                                        
+            - self.sources[source] : It supplements the overview overview of
+                                     data sources can be consulted with
+                                     self.sources. The structure is as follows:
+                                     as:
+                self.sources = { 
+                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
+                'GLEAM' :  ['pars:wg','pars:w2', ...],
+                 ...
+                }
+        
+        """
+
+        #print(source,kwargs)
+
+        for key,data in kwargs.items():
+
+            #print(key)
+            # if the key is not in class4gl_input object, then just add it. In
+            # that case, the update procedures below will just overwrite it 
+            if key not in self.__dict__:
+                self.__dict__[key] = data
+
+
+            
+
+            #... we do an additional check to see whether there is a type
+            # match. I not then raise a key error
+            if (type(data) != type(self.__dict__[key]) \
+                # we allow dict input for model_input pars
+                and not ((key == 'pars') and (type(data) == dict) and \
+                (type(self.__dict__[key]) == model_input))):
+
+                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
+
+
+            # This variable keeps track of the added data that is supplemented
+            # by the current source. We add this to class4gl_input.sources
+            datakeys = []
+
+            #... and we update the class4gl_input data, and this depends on the
+            # data type
+
+            if type(self.__dict__[key]) == pd.DataFrame:
+                # If the data type is a dataframe, then we update the columns
+                for column in list(data.columns):
+                    #print(column)
+                    self.__dict__[key][column] = data[column]
+                    datakeys.append(column)
+                    
+
+            elif type(self.__dict__[key]) == model_input:
+                # if the data type is a model_input, then we update its internal
+                # dictionary of parameters
+                if type(data) == model_input:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data.__dict__}
+                    datakeys = list(data.__dict__.keys())
+                elif type(data) == dict:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data}
+                    datakeys = list(data.keys())
+                else:
+                    raise TypeError('input key '+key+' is not of the same type\
+                                    as the one in the class4gl_object')
+
+            elif type(self.__dict__[key]) == dict:
+                # if the data type is a dictionary, we update the
+                # dictionary 
+                self.__dict__[key] = {self.__dict__[key] , data}
+                datakeys = list(data.keys())
+
+
+            # if source entry is not existing yet, we add it
+            if source not in self.sources.keys():
+                self.sources[source] = []
+
+
+            # self.logger.debug('updating section "'+\
+            #                  key+' ('+' '.join(datakeys)+')'\
+            #                  '" from source \
+            #                  "'+source+'"')
+
+            # Update the source dictionary: add the provided data keys to the
+            # specified source list
+            for datakey in datakeys:
+                # At first, remove the occurences of the keys in the other
+                # source lists
+                for sourcekey,sourcelist in self.sources.items():
+                    if key+':'+datakey in sourcelist:
+                        self.sources[sourcekey].remove(key+':'+datakey)
+                # Afterwards, add it to the current source list
+                self.sources[source].append(key+':'+datakey)
+
+
+        # # in case the datatype is a class4gl_input_pars, we update its keys
+        # # according to **kwargs dictionary
+        # if type(self.__dict__[datatype]) == class4gl_input_pars:
+        #     # add the data parameters to the datatype object dictionary of the
+        #     # datatype
+        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
+        #                                        **kwargs}
+        # # in case, the datatype reflects a dataframe, we update the columns according
+        # # to the *args list
+        # elif type(self.__dict__[datatype]) == pd.DataFrame:
+        #     for dataframe in args:
+        #         for column in list(dataframe.columns):
+        #             self.__dict__[datatype][column] = dataframe[column]
+        
+
+    def get_profile(self,IOBJ, *args, **argv):
+        # if type(IOBJ) == wyoming:
+        self.get_profile_wyoming(IOBJ,*args,**argv)
+        # else:
+        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
+        
+    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
+        """ 
+            Purpose: 
+                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
+
+            Input:
+                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
+                function will take the profile at the stream's current
+                position. 
+                2. air_ap_mode: which air profile do we take? 
+                    - b : best
+                    - l : according to lower limit for the mixed-layer height
+                            estimate
+                    - u : according to upper limit for the mixed-layer height
+                            estimate
+
+
+            Output:
+                1. all single-value parameters are stored in the
+                   class4gl_input.pars object
+                2. the souding profiles are stored in the in the
+                   class4gl_input.air_balloon dataframe
+                3. modified sounding profiles for which the mixed layer height
+                   is fitted
+                4. ...
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = 'wyoming'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = air_ap_mode
+
+        """ Temporary variables used for output """
+        # single value parameters derived from the sounding profile
+        dpars = dict()
+        # profile values
+        air_balloon = pd.DataFrame()
+        # fitted profile values
+        air_ap = pd.DataFrame()
+        
+        string = wy_strm.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = wy_strm.current.find_next('pre').find_next('pre').text
+        
+        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
+        dpars = {**dpars,
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+               }
+        
+        # we get weird output when it's a numpy Timestamp, so we convert it to
+        # pd.datetime type
+
+        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
+        dpars['STNID'] = dpars['Station number']
+
+        # altitude above ground level
+        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        # absolute humidity in g/kg
+        air_balloon['q']= (air_balloon.MIXR/1000.) \
+                              / \
+                             (air_balloon.MIXR/1000.+1.)
+        # convert wind speed from knots to m/s
+        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
+        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        
+        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+
+        
+
+        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+
+        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
+        air_balloon['p'] = air_balloon.PRES*100.
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        #is_valid = (air_balloon.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_balloon.index[is_valid].values
+        print(valid_indices)
+
+        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+
+        air_balloon['t'] = air_balloon['TEMP']+273.15
+        air_balloon['theta'] = (air_balloon.t) * \
+                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+
+
+        if np.isnan(dpars['h']):
+            dpars['Ps'] = np.nan
+
+
+
+
+        if ~np.isnan(dpars['h']):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u 
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+            
+
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        air_ap_head['HGHT'] = air_ap_head['z'] \
+                                + \
+                                np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = dpars['theta']
+        z_low =     dpars['h']
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+
+        
+        dpars['lat'] = dpars['Station latitude']
+        dpars['latitude'] = dpars['lat']
+        
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        dpars['longitude'] = dpars['Station longitude']
+        
+        dpars['ldatetime'] = dpars['datetime'] \
+                            + \
+                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+
+        # # we make a pars object that is similar to the destination object
+        # pars = model_input()
+        # for key,value in dpars.items():
+        #     pars.__dict__[key] = value
+
+
+        # we round the columns to a specified decimal, so that we get a clean
+        # output format for yaml
+        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
+                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
+                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+# 
+        for column,decimal in decimals.items():
+            air_balloon[column] = air_balloon[column].round(decimal)
+            air_ap[column] = air_ap[column].round(decimal)
+
+        self.update(source='wyoming',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+
+        
+    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
+    
+        """
+        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
+                 according to the position (lat lon) and the class datetime and timespan
+                 globaldata should be a globaldata multifile object
+        
+        Input: 
+            - globaldata: this is the library object
+            - only_keys: only extract specified keys
+            - exclude_keys: do not inherit specified keys
+        """
+        classdatetime      = np.datetime64(self.pars.datetime_daylight)
+        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
+                                           + \
+                                           dt.timedelta(seconds=self.pars.runtime)\
+                                          )
+
+
+        # # list of variables that we get from global ground data
+        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
+        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
+        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
+        #                 'texture', 'itex', 'isoil', 'BR',
+        #                 'b', 'cveg',
+        #                 'C1sat', 
+        #                 'C2ref', 'p', 'a',
+        #                 ] #globaldata.datasets.keys():
+
+        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
+        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
+
+
+        if type(globaldata) is not data_global:
+            raise TypeError("Wrong type of input library") 
+
+        # by default, we get all dataset keys
+        keys = list(globaldata.datasets.keys())
+
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
+        # # In case there is surface pressure, we also calculate the half-level
+        # # and full-level pressure fields
+        # if ('sp' in keys):
+        #     keys.append('pfull')
+        #     keys.append('phalf')
+
+        # If specified, we only take the keys that are in only_keys
+        if only_keys is not None:
+            for key in keys:
+                if key not in only_keys:
+                    keys.remove(key)
+                
+        # If specified, we take out keys that are in exclude keys
+        if exclude_keys is not None:
+            for key in keys:
+                if key in exclude_keys:
+                    keys.remove(key)
+
+        # we set everything to nan first in the pars section (non-profile parameters
+        # without lev argument), so that we can check afterwards whether the
+        # data is well-fetched or not.
+        for key in keys:
+            if not ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None) and \
+                ('lev' in globaldata.datasets[key].page[key].dims)):
+                self.update(source='globaldata',pars={key:np.nan})
+            # # we do not check profile input for now. We assume it is
+            # # available
+            #else:
+            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
+
+        self.logger.debug('getting keys "'+', '.join(keys)+'\
+                          from global data')
+
+        for key in keys:
+            # If we find it, then we obtain the variables
+            if ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None)):
+
+                # check first whether the dataset has a height coordinate (3d space)
+                if 'lev' in globaldata.datasets[key].page[key].dims:
+
+                    # first, we browse to the correct file that has the current time
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+                        globaldata.datasets[key].browse_page(time=classdatetime)
+
+                    
+                    if (globaldata.datasets[key].page is not None):
+                        # find longitude and latitude coordinates
+                        ilats = (np.abs(globaldata.datasets[key].page.lat -
+                                        self.pars.latitude) < 0.5)
+                        ilons = (np.abs(globaldata.datasets[key].page.lon -
+                                        self.pars.longitude) < 0.5)
+                        
+                        # if we have a time dimension, then we look up the required timesteps during the class simulation
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            itimes = ((globaldata.datasets[key].page.time >= \
+                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            # In case we didn't find any correct time, we take the
+                            # closest one.
+                            if np.sum(itimes) == 0.:
+
+
+                                classdatetimemean = \
+                                    np.datetime64(self.pars.datetime_daylight + \
+                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
+                                                ))
+
+                                dstimes = globaldata.datasets[key].page.time
+                                time = dstimes.sel(time=classdatetimemean,method='nearest')
+                                itimes = (globaldata.datasets[key].page.time ==
+                                          time)
+                                
+                        else:
+                            # we don't have a time coordinate so it doesn't matter
+                            # what itimes is
+                            itimes = 0
+
+                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+
+                        # over which dimensions we take a mean:
+                        dims = globaldata.datasets[key].page[key].dims
+                        namesmean = list(dims)
+                        namesmean.remove('lev')
+                        idxmean = [dims.index(namemean) for namemean in namesmean]
+                        
+                        value = \
+                        globaldata.datasets[key].page[key].isel(time=itimes,
+                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+                        # Ideally, source should be equal to the datakey of globaldata.library 
+                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+                        #  but therefore the globaldata class requires a revision to make this work
+                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+
+                else:
+                    # this procedure is for reading the ground fields (2d space). 
+                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
+
+    
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+    
+                       # first, we browse to the correct file
+                       #print(key)
+                       globaldata.datasets[key].browse_page(time=classdatetime)
+    
+                    if globaldata.datasets[key].page is not None:
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - self.pars.latitude))
+                        ilat = np.where((DIST) == np.min(DIST))[0][0]
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - self.pars.longitude))
+                        ilon = np.where((DIST) == np.min(DIST))[0][0]
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - (self.pars.latitude + 0.5)))
+                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmax = ilat
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - (self.pars.longitude  + 0.5)))
+                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmax = ilon
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lat.values\
+                                - (self.pars.latitude - 0.5)))
+                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmin = ilat
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lon.values\
+                                - (self.pars.longitude  - 0.5)))
+                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmin = ilon        
+                        
+                        if ilatmin < ilatmax:
+                            ilatrange = range(ilatmin,ilatmax+1)
+                        else:
+                            ilatrange = range(ilatmax,ilatmin+1)
+                            
+                        if ilonmin < ilonmax:
+                            ilonrange = range(ilonmin,ilonmax+1)
+                        else:
+                            ilonrange = range(ilonmax,ilonmin+1)     
+                            
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                idatetime += 1
+                            
+                            classdatetimeend = np.datetime64(\
+                                                             self.pars.datetime +\
+                                                             dt.timedelta(seconds=self.pars.runtime)\
+                                                            ) 
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+                            if key in ['wg', 'w2']:
+                                idatetime = idatetime - 1
+                                idatetimeend = idatetimeend - 1
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['Tsoil','T2']:
+                                idatetimeend = idatetime
+                            
+                            idts = range(idatetime,idatetimeend+1)
+                            
+                            count = 0
+                            self.__dict__[key] = 0.
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    for iidts in idts:
+                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
+                                        count += 1
+                            value = value/count
+                            self.update(source='globaldata',pars={key:value.item()})
+                                
+                        else:
+                                
+                            count = 0
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
+                                    count += 1
+                            value = value/count                        
+
+                            self.update(source='globaldata',pars={key:value.item()})
+
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            self.logger.debug('also update LAI based on LAIpixel and cveg') 
+            # I suppose LAI pixel is already determined in the previous
+            # procedure. Anyway...
+            key = 'LAIpixel'
+
+            if globaldata.datasets[key].page is not None:
+                # first, we browse to the correct file that has the current time
+                if 'time' in list(globaldata.datasets[key].page[key].dims):
+                    globaldata.datasets[key].browse_page(time=classdatetime)
+            
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - self.pars.latitude))
+                ilat = np.where((DIST) == np.min(DIST))[0][0]
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - self.pars.longitude))
+                ilon = np.where((DIST) == np.min(DIST))[0][0]
+                 
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude + 0.5)))
+                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmax = ilat
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values \
+                        - (self.pars.longitude  + 0.5)))
+                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmax = ilon
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude - 0.5)))
+                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmin = ilat
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - (self.pars.longitude  - 0.5)))
+                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmin = ilon        
+                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                
+                
+                if ilatmin < ilatmax:
+                    ilatrange = range(ilatmin,ilatmax+1)
+                else:
+                    ilatrange = range(ilatmax,ilatmin+1)
+                    
+                if ilonmin < ilonmax:
+                    ilonrange = range(ilonmin,ilonmax+1)
+                else:
+                    ilonrange = range(ilonmax,ilonmin+1)           
+                
+                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
+                LAIpixel = 0.
+                count = 0
+                for iilat in [ilat]: #ilatrange
+                    for iilon in [ilon]: #ilonrange
+                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
+                        
+                                        
+                        # if np.isnan(tarray[idatetime]):
+                        #     print("interpolating GIMMS LAIpixel nan value")
+                        #     
+                        #     mask = np.isnan(tarray)
+                        #     
+                        #     #replace each nan value with a interpolated value
+                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+                        #         
+                        #     else:
+                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
+                    
+                        #         tarray *= np.nan 
+                        
+                        count += 1
+                        #tarray_res += tarray
+                LAIpixel = LAIpixel/count
+                
+                count = 0
+                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
+  
+                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
+                #print('LAIpixel:',self.__dict__['LAIpixel'])
+                #print('cveg:',self.__dict__['cveg'])
+                
+                # finally, we rescale the LAI according to the vegetation
+                # fraction
+                value = 0. 
+                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
+                   value =self.pars.LAIpixel/self.pars.cveg
+                else:
+                    # in case of small vegetation fraction, we take just a standard 
+                    # LAI value. It doesn't have a big influence anyway for
+                    # small vegetation
+                    value = 2.
+                #print('LAI:',self.__dict__['LAI'])
+                self.update(source='globaldata',pars={'LAI':value}) 
+
+
+        # in case we have 'sp', we also calculate the 3d pressure fields at
+        # full level and half level
+        if ('sp' in keys) and ('sp' in self.pars.__dict__):
+            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
+
+            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # hydrostatic thickness of each model layer
+            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
+            # # dz = rhodz/(R * T / pfull)
+
+
+            # # subsidence multiplied by density. We calculate the subsidence of
+            # # the in class itself
+            # wrho = np.zeros_like(phalf)
+            # wrho[-1] = 0. 
+
+            # for ihlev in range(0,wrho.shape[0]-1):
+            #     # subsidence multiplied by density is the integral of
+            #     # divergences multiplied by the layer thicknessies
+            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
+            #                     self.air_ac['divU_y'][ihlev:]) * \
+            #                    delpdgrav[ihlev:]).sum()
+
+
+            
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'p':list(pfull)}))
+            self.update(source='globaldata',\
+                        air_ach=pd.DataFrame({'p':list(phalf)}))
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
+            # self.update(source='globaldata',\
+            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
+
+    def check_source(self,source,check_only_sections=None):
+        """ this procedure checks whether data of a specified source is valid.
+
+        INPUT:
+            source: the data source we want to check
+            check_only_sections: a string or list with sections to be checked
+        OUTPUT:
+            returns True or False
+        """
+
+        # we set source ok to false as soon as we find a invalid input
+        source_ok = True
+
+        # convert to a single-item list in case of a string
+        check_only_sections_def = (([check_only_sections]) if \
+                                   type(check_only_sections) is str else \
+                                    check_only_sections)
+                                  
+        if source not in self.sources.keys():
+            self.logger.info('Source '+source+' does not exist')
+            source_ok = False
+
+        for sectiondatakey in self.sources[source]:                             
+            section,datakey = sectiondatakey.split(':')                         
+            if ((check_only_sections_def is None) or \
+                (section in check_only_sections_def)):                          
+                checkdatakeys = []
+                if type(self.__dict__[section]) is pd.DataFrame:
+                    checkdata = self.__dict__[section]
+                elif type(self.__dict__[section]) is model_input:
+                    checkdata = self.__dict__[section].__dict__
+
+                if (datakey not in checkdata):                              
+                    # self.logger.info('Expected key '+datakey+\
+                    #                  ' is not in parameter input')                        
+                    source_ok = False                                           
+                elif (checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True):                    
+        
+                    # self.logger.info('Key value of "'+datakey+\
+                    #                  '" is invalid: ('+ \
+                    # str(self.__dict__[section].__dict__[datakey])+')')         
+                    source_ok = False
+
+        return source_ok
+
+    def check_source_globaldata(self):
+        """ this procedure checks whether all global parameter data is
+        available, according to the keys in the self.sources"""
+
+        source_globaldata_ok = True
+
+        #self.get_values_air_input()
+
+        # and now we can get the surface values
+        #class_settings = class4gl_input()
+        #class_settings.set_air_input(input_atm)
+        
+        # we only allow non-polar stations
+        if not (self.pars.lat <= 60.):
+            source_globaldata_ok = False
+            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+        
+        # check lat and lon
+        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
+            source_globaldata_ok = False
+            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+        else:
+            # we only check the ground parameter data (pars section). The 
+            # profile data (air_ap section) are supposed to be valid in any 
+            # case.
+            source_ok = self.check_source(source='globaldata',\
+                                          check_only_sections=['air_ac',\
+                                                               'air_ap',\
+                                                               'pars'])
+            if not source_ok:
+                source_globaldata_ok = False
+        
+            # Additional check: we exclude desert-like
+            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
+                source_globaldata_ok = False
+                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
+                source_globaldata_ok = False
+                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+            elif self.pars.cveg < 0.02:
+                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                source_globaldata_ok = False
+
+        return source_globaldata_ok
+
+
+class c4gli_iterator():
+    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
+    
+        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
+    """
+    def __init__(self,file):
+        # take file as IO stream
+        self.file = file
+        self.yaml_generator = yaml.load_all(file)
+        self.current_dict = {}
+        self.current_class4gl_input = class4gl_input()
+        separator = self.file.readline() # this is just dummy
+        self.header = file.readline()
+        if self.header != '# CLASS4GL record; format version: 0.1\n':
+            raise NotImplementedError("Wrong format version: '"+self.header+"'")
+    def __iter__(self):
+        return self
+    def __next__(self):
+        self.current_dict = self.yaml_generator.__next__()
+        self.current_class4gl_input.load_yaml_dict(self.current_dict)
+        return self.current_class4gl_input
+
+
+
+#get_cape and lift_parcel are adapted from the SkewT package
+    
+class gl_dia(object):
+    def get_lifted_index(self,timestep=-1):
+        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
+    
+#from SkewT
+#def get_lcl(startp,startt,startdp,nsteps=101):
+#    from numpy import interp
+#    #--------------------------------------------------------------------
+#    # Lift a parcel dry adiabatically from startp to LCL.
+#    # Init temp is startt in K, Init dew point is stwrtdp,
+#    # pressure levels are in Pa    
+#    #--------------------------------------------------------------------
+#
+#    assert startdp<=startt
+#
+#    if startdp==startt:
+#        return np.array([startp]),np.array([startt]),np.array([startdp]),
+#
+#    # Pres=linspace(startp,60000.,nsteps)
+#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
+#
+#    # Lift the dry parcel
+#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
+#    # Mixing ratio isopleth
+#    starte=VaporPressure(startdp)
+#    startw=MixRatio(starte,startp)
+#    e=Pres*startw/(.622+startw)
+#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
+#
+#    # Solve for the intersection of these lines (LCL).
+#    # interp requires the x argument (argument 2)
+#    # to be ascending in order!
+#    P_lcl=interp(0.,T_iso-T_dry,Pres)
+#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
+#
+#    # # presdry=linspace(startp,P_lcl)
+#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
+#
+#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
+#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
+#
+#    return P_lcl,T_lcl
+
+
+
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
+
+
+
+#from class
+def get_lcl(startp,startt,startqv):
+        # Find lifting condensation level iteratively
+    lcl = 20.
+    RHlcl = 0.5
+    
+    itmax = 30
+    it = 0
+    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHd
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self):
+       self.status = 'init'
+       self.found = False
+       self.DT = None
+       self.current = None
+       #self.mode = 'b'
+       self.profile_type = 'wyoming'  
+       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+         
+    def set_STNM(self,STNM):
+        self.__init__()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+        self.current = self.sounding_series.find('h2')
+        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+        
+        # go through other files and find first sounding when year is not specified
+        self.iFN=self.iFN+1
+        while keepsearching:
+            self.FN = self.FILES[self.iFN]
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            self.iFN=self.iFN+1
+            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+        self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        if self.found and get_atm:
+            self.get_values_air_input()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        #print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                if get_atm:
+                    self.get_values_air_input()
+                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        if self.found and get_atm:
+            self.get_values_air_input()
+       
+
+
+    def get_values_air_input(self,latitude=None,longitude=None):
+
+        # for iDT,DT in enumerate(DTS):
+        
+            #websource = urllib.request.urlopen(webpage)
+        #soup = BeautifulSoup(open(webpage), "html.parser")
+        
+       
+        #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        BLHV = np.max((BLHV,10.))
+        BLHVu = np.max((BLHVu,10.))
+        BLHVd = np.max((BLHVd,10.))
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+                
+                # get index of lowest valid observation. This seems to vary
+                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+                if len(idxvalid) > 0:
+                    #print('idxvalid',idxvalid)
+                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+                    else:
+                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+                    #print(col,meanabl)
+               
+                
+                # if col == 'PRES':
+                #     meanabl =  
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV',]:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+                        dtheta = np.max((0.1,dtheta_pre))
+                        #meanabl = meanabl - (dtheta - dtheta_pre)
+                        #print('dtheta_pre',dtheta_pre)
+                        #print('dtheta',dtheta)
+                        #print('meanabl',meanabl)
+                        #stop
+                        
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            
+            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+        
+            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+
+        try:
+            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+        except:
+            print("could not convert latitude coordinate")
+            PARAMS.insert(0,'latitude', np.nan)
+            PARAMS.insert(0,'lat', np.nan)
+        try:
+            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+            PARAMS.insert(0,'lon', 0.)
+        except:
+            print("could not convert longitude coordinate")
+            PARAMS.insert(0,'longitude', np.nan)
+            PARAMS.insert(0,'lon', 0.)
+
+        if latitude is not None:
+            print('overwriting latitude with specified value')
+            PARAMS['latitude'] = np.float(latitude)
+            PARAMS['lat'] = np.float(latitude)
+        if longitude is not None:
+            print('overwriting longitude with specified value')
+            PARAMS['longitude'] = np.float(longitude)
+        try:
+            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            # This is the nearest datetime when sun is up (for class)
+            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+            # apply the same time shift for UTC datetime
+            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+            
+        except:
+            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+
+        
+
+        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+        # as we are forcing lon equal to zero this is is expressed in local suntime
+        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
+        elif self.mode == 'b': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNb
+            BLCOLUMN = ONE_COLUMNb
+        elif self.mode == 'u': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNu
+            BLCOLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNd
+            BLCOLUMN = ONE_COLUMNd
+        else:
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb
+
+        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
+        # print(BLCOLUMN['HAGL'][lt6000])
+        # print(BLCOLUMN['HAGL'][lt2500])
+        # 
+        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+
+        #print(BLCOLUMN['HAGL'][lt2500])
+        PARAMS.insert(0,'OK',
+                      ((BLHVe < 200.) and 
+                       ( len(np.where(lt6000)[0]) > 5) and
+                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+                      )
+                     )
+
+        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+        
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = USE_ONECOLUMN
+        # if self.mode == 'o': #original 
+        #     self.ONE_COLUMN = ONE_COLUMN
+        # elif self.mode == 'b': # best BLH
+        #     self.ONE_COLUMN = ONE_COLUMNb
+        # elif self.mode == 'u':# upper BLH
+        #     self.ONE_COLUMN = ONE_COLUMNu
+        # elif self.mode == 'd': # lower BLH
+        #     self.ONE_COLUMN=ONE_COLUMNd
+        # else:
+        #     self.ONE_COLUMN = ONE_COLUMN
+
diff --git a/build/lib/lib/data_global.py b/build/lib/lib/data_global.py
new file mode 100644
index 0000000..9c3d9b5
--- /dev/null
+++ b/build/lib/lib/data_global.py
@@ -0,0 +1,936 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: Hendrik Wouters
+
+Purpose: provides class routines for ground and atmosphere conditions used for
+the CLASS miced-layer model
+
+Usage:
+    from data_global import data_global
+    from class4gl import class4gl_input
+    from data_soundings import wyoming
+
+    # create a data_global object and load initial data pages
+    globaldata = data_global()
+    globaldata.load_datasets()
+    # create a class4gl_input object
+    c4gli = class4gl_input()
+    # Initialize it with profile data. We need to do this first. Actually this
+    # will set the coordinate parameters (datetime, latitude, longitude) in
+    # class4gl_input.pars.__dict__, which is required to read point data from
+    # the data_global object.
+
+    # open a Wyoming stream for a specific station
+    wy_strm = wyoming(STNM=91376)
+    # load the first profile
+    wy_strm.find_first()
+    # load the profile data into the class4gl_input object
+    c4gli.get_profile_wyoming(wy_strm)
+    
+    # and finally, read the global input data for this profile
+    c4gli.get_global_input(globaldata)
+
+
+"""
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+#import pynacolada as pcd
+import pandas as pd
+import xarray as xr
+import os
+import glob
+import sys
+import errno
+import warnings
+import logging
+
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+class book(object):
+    """ this is a class for a dataset spread over multiple files. It has a
+    similar purpose  open_mfdataset, but only 1 file (called current 'page')
+    one is loaded at a time. This saves precious memory.  """
+    def __init__(self,fn,concat_dim = None,debug_level=None):
+        self.logger = logging.getLogger('book')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # filenames are expanded as a list and sorted by filename
+        self.pages = glob.glob(fn); self.pages.sort()
+        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
+        if len(self.pages) == 0:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
+        self.ipage = -1; self.page = None
+        self.renames = {} # each time when opening a file, a renaming should be done.
+        self.set_page(0)
+
+        # we consider that the outer dimension is the one we concatenate
+        self.concat_dim = concat_dim
+        if self.concat_dim is None:
+            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
+
+    # this wraps the xarray sel-commmand
+    def sel(*args, **kwargs):
+        for dim in kwargs.keys():
+            if dim == self.concat_dim:
+                self.browse_page(**{dim: kwargs[dim]})
+        return page.sel(*args,**kwargs)
+
+
+    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
+    #def __getattr__(self,attr):
+    #    orig_attr = self.page.__getattribute__(attr)
+    #    if callable(orig_attr):
+    #        def hooked(*args, **kwargs):
+    #            for dim in kwargs.keys():
+    #                if dim == self.concat_dim:
+    #                    self.browse_page(**{dim: kwargs[dim]})
+    #
+    #            result = orig_attr(*args, **kwargs)
+    #            # prevent wrapped_class from becoming unwrapped
+    #            if result == self.page:
+    #                return self
+    #            self.post()
+    #            return result
+    #        return hooked
+    #    else:
+    #        return orig_attr
+
+    def set_renames(self,renames):
+        #first, we convert back to original names, and afterwards, we apply the update of the renames.
+        reverse_renames = dict((v,k) for k,v in self.renames.items())
+        self.renames = renames
+        if self.page is not None:
+            self.page = self.page.rename(reverse_renames)
+            self.page = self.page.rename(self.renames)
+
+    def set_page(self,ipage,page=None):
+        """ this sets the right page according to ipage:
+                - We do not switch the page if we are already at the right one
+                - we set the correct renamings (level -> lev, latitude -> lat,
+                etc.)
+                - The dataset is also squeezed.
+        """
+
+        if ((ipage != self.ipage) or (page is not None)):
+
+            if self.page is not None:
+                self.page.close()
+
+            self.ipage = ipage
+            if page is not None:
+                self.page = page
+            else:
+                if self.ipage == -1:
+                   self.page = None
+                else:
+                    #try:
+
+                    self.logger.info("Switching to page "+str(self.ipage)+': '\
+                                     +self.pages[self.ipage])
+                    self.page = xr.open_dataset(self.pages[self.ipage])
+
+
+            # do some final corrections to the dataset to make them uniform
+            if self.page is not None:
+               if 'latitude' in self.page.dims:
+#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
+               if 'level' in self.page.dims:
+                   self.page = self.page.rename({'level':'lev'})
+
+               self.page = self.page.rename(self.renames)
+               self.page = self.page.squeeze(drop=True)
+
+    def browse_page(self,rewind=2,**args):
+
+        # at the moment, this is only tested with files that are stacked according to the time dimension.
+        dims = args.keys()
+
+
+        if self.ipage == -1:
+            self.set_page(0)
+
+        found = False
+        iipage = 0
+        startipage = self.ipage - rewind
+        while (iipage < len(self.pages)) and not found:
+            ipage = (iipage+startipage) % len(self.pages)
+            for dim in args.keys():
+                this_file = True
+
+                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
+                if 'dims' not in self.__dict__:
+                    self.dims = {}
+                if dim not in self.dims.keys():
+                    self.dims[dim] = [None]*len(self.pages)
+
+                if self.dims[dim][ipage] is None:
+                    self.logger.info('Loading coordinates of dimension "'+dim+\
+                                     '" of page "' +str(ipage)+'".')
+                    self.set_page(ipage)
+                    # print(ipage)
+                    # print(dim)
+                    # print(dim,self.page[dim].values)
+                    self.dims[dim][ipage] = self.page[dim].values
+
+                # determine current time range of the current page
+                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
+                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
+
+                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
+                    this_file = False
+
+            if this_file:
+                found = True
+                self.set_page(ipage)
+            else:
+
+                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
+                #    iipage = len(self.pages) # we stop searching
+
+                iipage += 1
+
+        if not found:
+            self.logger.info("Page not found. Setting to page -1")
+            #iipage = len(self.pages) # we stop searching further
+            self.set_page(-1)
+
+        if self.ipage != -1:
+            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
+        else:
+            self.logger.debug("I'm now at page "+ str(self.ipage))
+
+
+class data_global(object):
+    def __init__(self,sources= {
+        # # old gleam
+        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
+        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
+        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
+        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
+        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
+        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
+        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
+        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
+        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
+        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
+        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
+        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
+        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
+        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
+        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
+        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
+        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
+        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
+        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
+        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
+        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
+        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
+        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
+        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
+        },debug_level=None):
+        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
+        self.sources = sources
+        self.datarefs = {}
+        self.datasets = {}
+        self.datetime = dt.datetime(1981,1,1)
+
+        self.logger = logging.getLogger('data_global')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+        self.debug_level = debug_level
+
+        warnings.warn('omitting pressure field p and advection')
+
+    def in_library(self,fn):
+        if fn not in self.library.keys():
+            return False
+        else:
+            print("Warning: "+fn+" is already in the library.")
+            return True
+
+    def add_to_library(self,fn):
+        if not self.in_library(fn):
+            print("opening: "+fn)
+            self.library[fn] = \
+                book(fn,concat_dim='time',debug_level=self.debug_level)
+
+            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
+            #if 'latitude' in self.library[fn].variables:
+            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+
+    # default procedure for loading datasets into the globaldata library
+    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
+        if type(varssource) is str:
+            varssource = [varssource]
+        if type(varsdest) is str:
+            varsdest = [varsdest]
+
+        self.add_to_library(input_fn)
+
+        if varssource is None:
+            varssource = []
+            for var in self.sources[input_fn].variables:
+                avoid = \
+                ['lat','lon','latitude','longitude','time','lev','level']
+                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
+                    varssource.append(var)
+
+        if varsdest is None:
+            varsdest = varssource
+
+        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        for ivar,vardest in enumerate(varsdest):
+            varsource = varssource[ivar]
+            print('setting '+vardest+' as '+varsource+' from '+input_fn)
+
+            if vardest in self.datarefs.keys():
+                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
+            #self.add_to_library(fn,varsource,vardest)
+            if vardest != varsource:
+                libkey = input_fn+'.'+varsource+'.'+vardest
+                if libkey not in self.library.keys():
+                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
+                    self.library[libkey] = book(input_fn,\
+                                                debug_level=self.debug_level)
+                    self.library[libkey].set_renames({varsource: vardest})
+
+                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+            else:
+                self.datarefs[vardest] = input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+
+            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
+            #     print('Warning: '+ vardest "not in " + input_fn)
+
+
+
+    def load_datasets(self,sources = None,recalc=0):
+
+        if sources is None:
+            sources = self.sources
+        for key in sources.keys():
+            #datakey,vardest,*args = key.split(':')
+            datakey,vardest = key.split(':')
+            #print(datakey)
+
+            fnvarsource = sources[key].split(':')
+            if len(fnvarsource) > 2:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource,fnargs = fnvarsource
+                fnargs = [fnargs]
+            elif len(fnvarsource) > 1:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource = fnvarsource
+                fnargs = []
+            else:
+                fn = sources[key]
+                varsource = vardest
+            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
+
+    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
+            # the default way of loading a 2d dataset
+            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
+                self.load_dataset_default(fn,varsource,vardest)
+            elif datakey == 'IGBPDIS':
+                if vardest == 'alpha':
+                    ltypes = ['W','B','H','TC']
+                    for ltype in ltypes:
+                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
+                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
+
+
+                    # landfr = {}
+                    # for ltype in ['W','B','H','TC']:
+                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
+
+
+
+                    keytemp = 'alpha'
+                    fnkeytemp = fn+':IGBPDIS:alpha'
+                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
+                        self.library[fnkeytemp]  = book(fnkeytemp,
+                                                        debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+                    else:
+                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
+                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
+                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
+                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
+                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
+                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
+                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
+
+                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+
+                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
+                        for ltype in ltypes:
+                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
+
+                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
+                        print('writing file to: '+fnkeytemp)
+                        os.system('rm '+fnkeytemp)
+                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
+                        self.library[fnkeytemp].close()
+
+
+                        self.library[fnkeytemp]  = \
+                            book(fnkeytemp,debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+
+
+                else:
+                    self.load_dataset_default(fn,varsource,vardest)
+
+
+            elif datakey == 'GLAS':
+                self.load_dataset_default(fn,varsource,vardest)
+                if vardest == 'z0m':
+                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
+                elif vardest == 'z0h':
+                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
+            elif datakey == 'DSMW':
+
+
+                # Procedure of the thermal properties:
+                # 1. determine soil texture from DSMW/10.
+                # 2. soil type with look-up table (according to DWD/EXTPAR)
+                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
+                #    with parameter look-up table from Noilhan and Planton (1989).
+                #    Note: The look-up table is inspired on DWD/COSMO
+
+                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
+
+
+
+                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
+                self.load_dataset_default(fn,'DSMW')
+                print('calculating texture')
+                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
+                TEMP  = {}
+                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
+                TEMP3 = {}
+                for SPKEY in SPKEYS:
+
+
+                    keytemp = SPKEY+'_values'
+                    fnoutkeytemp = fnout+':DSMW:'+keytemp
+                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                    else:
+                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
+                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
+                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+                        # for faster computation, we need to get it to memory out of Dask.
+                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
+                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
+
+                # yes, I know I only check the last file.
+                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
+                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
+                        print('idx',idx,SPKEY)
+                        SEL = (TEMP2 == idx)
+                    #     print(idx,len(TEMP3))
+                        for SPKEY in SPKEYS:
+                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
+
+                    for SPKEY in SPKEYS:
+                        keytemp = SPKEY+'_values'
+                        fnoutkeytemp = fnout+':DSMW:'+keytemp
+                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
+                        os.system('rm '+fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].close()
+
+
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                keytemp = 'texture'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+                else:
+                    self.library[fn+':DSMW:texture'] = xr.Dataset()
+                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
+                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
+                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
+                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+
+                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
+
+                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
+                    zundef[zundef < 0] = np.nan
+                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
+                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
+
+                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+
+
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+                print('calculating texture type')
+
+
+
+                keytemp = 'itex'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+                else:
+                    self.library[fnoutkeytemp] = xr.Dataset()
+                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+                    X = self.datasets['texture'].page['texture'].values*100
+                    X[pd.isnull(X)] = -9
+
+
+                    self.datasets[keytemp][keytemp].values = X
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
+                    self.datasets['itex'].close()
+
+
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+
+                keytemp = 'isoil'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                isoil_reprocessed = False
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+                else:
+                    isoil_reprocessed = True
+                    print('calculating soil type')
+                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    ITEX = self.datasets['itex'].page['itex'].values
+                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
+                    LOOKUP = [
+                              [-10 ,9],# ocean
+                              [0 ,7],# fine textured, clay (soil type 7)
+                              [20,6],# medium to fine textured, loamy clay (soil type 6)
+                              [40,5],# medium textured, loam (soil type 5)
+                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                              [80,3],# coarse textured, sand (soil type 3)
+                              [100,9],# coarse textured, sand (soil type 3)
+                            ]
+                    for iitex,iisoil in LOOKUP:
+                        ISOIL[ITEX > iitex] = iisoil
+                        print('iitex,iisoil',iitex,iisoil)
+
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    LOOKUP = [
+                              [9001, 1 ], # ice, glacier (soil type 1)
+                              [9002, 2 ], # rock, lithosols (soil type 2)
+                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                              [9,    9 ], # undefined (ocean)
+                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                              [9000, 9 ], # undefined (inland lake)
+                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                            ]
+                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
+
+                    CODE_VALUES[ITEX == 901200] = 9012
+                    for icode,iisoil in LOOKUP:
+                        ISOIL[CODE_VALUES == icode] = iisoil
+
+                    self.datasets['isoil']['isoil'].values = ISOIL
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+                    print('saved inbetween file to: '+fnoutkeytemp)
+
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                #adopted from data_soil.f90 (COSMO5.0)
+                SP_LOOKUP = {
+                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
+                  # (by index)                                           loam                    loam                                water      ice
+                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+                  # Important note: For peat, the unknown values below are set equal to that of loam
+                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
+                  #error in table 2 of NP89: values need to be multiplied by e-6
+                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
+
+                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
+                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
+                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
+                }
+
+
+                # isoil_reprocessed = False
+                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+
+                #     self.library[fn+':DSMW:isoil'] = \
+                #             book(fnoutkeytemp,debug_level=self.debug_level)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+                # else:
+                #     isoil_reprocessed = True
+                #     print('calculating soil type')
+                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+
+
+
+                # this should become cleaner in future but let's hard code it for now.
+                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
+                print('calculating soil parameter')
+                DATATEMPSPKEY = {}
+                if (recalc < 1) and (isoil_reprocessed == False): 
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        keytemp = SPKEY
+                        fnoutkeytemp=fnout+':DSMW:'+keytemp
+                        self.library[fn+':DSMW:'+SPKEY] =\
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
+                        self.datarefs[SPKEY] =fnoutkeytemp
+                else:
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+
+                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
+                    ISOIL = self.datasets['isoil'].page['isoil'].values
+                    print(np.where(ISOIL>0.))
+                    for i in range(11):
+                        SELECT = (ISOIL == i)
+                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
+
+                        os.system('rm '+fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].close()
+                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
+
+                        self.library[fn+':DSMW:'+SPKEY] = \
+                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+
+
+            else:
+                self.load_dataset_default(fn,varsource,vardest)
+
+
+
+
+
+
+#
+#                 # only print the last parameter value in the plot
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'cala'
+#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'crhoc'
+#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#     key = "CERES"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         CERES_start_date = dt.datetime(2000,3,1)
+#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+#
+#         var = 'cc'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#         print(class_settings.lat,class_settings.lon)
+#
+#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
+#
+#         input_nc.close()
+#
+
+
+#     key = "GIMMS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+#         print("Reading Leag Area Index from "+input_fn)
+#         var = 'LAI'
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+#
+#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+#
+#         if np.isnan(tarray[idatetime]):
+#             print("interpolating GIMMS cveg nan value")
+#
+#             mask = np.isnan(tarray)
+#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+#             else:
+#                 print("Warning. Could not interpolate GIMMS cveg nan value")
+#
+#         class_settings.__dict__[var] = tarray[idatetime]
+#
+#         input_nc.close()
+#
+#     key = "IGBPDIS_ALPHA"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         var = 'alpha'
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+#         print("Reading albedo from "+input_fn)
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#
+#         landfr = {}
+#         for ltype in ['W','B','H','TC']:
+#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+#
+#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+#
+#         alpha=0.
+#         for ltype in landfr.keys():
+#             alpha += landfr[ltype]*aweights[ltype]
+#
+#
+#         class_settings.__dict__[var] = alpha
+#         input_nc.close()
+#
+#
+#     key = "ERAINT_ST"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         print("Reading soil temperature from "+input_fn)
+#
+#         var = 'Tsoil'
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         var = 'T2'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+#
+#
+#         input_nc.close()
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #var = 'T2'
+#     #valold = class_settings.__dict__[var]
+#     #
+#     #class_settings.__dict__[var] = 305.
+#     #class_settings.__dict__['Tsoil'] = 302.
+#     #valnew = class_settings.__dict__[var]
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #
+#     #var = 'Lambda'
+#     #valold = class_settings.__dict__[var]
+#
+#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
+#     ## I need to ask Chiel.
+#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+#     #
+#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
+#     #class_settings.__dict__[var] = valnew
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     key = "GLAS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+#         print("Reading canopy height for determining roughness length from "+input_fn)
+#         var = 'z0m'
+#
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+#
+#         lowerlimit = 0.01
+#         if testval < lowerlimit:
+#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+#             class_settings.__dict__[var] = lowerlimit
+#         else:
+#             class_settings.__dict__[var] = testval
+#
+#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+#
+#
+#         input_nc.close()
+
+
+
+
+
diff --git a/build/lib/lib/interface_functions.py b/build/lib/lib/interface_functions.py
new file mode 100644
index 0000000..3e483f3
--- /dev/null
+++ b/build/lib/lib/interface_functions.py
@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+#from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+#'_afternoon.yaml'
+def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+    filename = yaml_file.name
+    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+    #yaml_file = open(filename)
+
+    #print('going to next observation',filename)
+    yaml_file.seek(index_start)
+
+    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+
+    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer.write(buf)
+    filebuffer.close()
+    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+
+    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    print(command)
+    os.system(command)
+    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    record_dict = json.load(jsonstream)
+    jsonstream.close()
+    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+
+
+    if mode =='mod':
+        modelout = class4gl()
+        modelout.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        return modelout
+    elif mode == 'ini':
+
+ 
+        # datetimes are incorrectly converted to strings. We need to convert them
+        # again to datetimes
+        for key,value in record_dict['pars'].items():
+            # we don't want the key with columns that have none values
+            if value is not None: 
+                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+               # elif (type(value) == str):
+                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
+            if (value == 0.9e19) or (value == '.9e19'):
+                record_dict['pars'][key] = np.nan
+        for key in record_dict.keys():
+            #print(key)
+            if key in ['air_ap','air_balloon',]:
+                #NNprint('check')
+                for datakey,datavalue in record_dict[key].items():
+                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+
+        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        c4gli = class4gl_input()
+        print(c4gli.logger,'hello')
+        c4gli.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+
+class stations(object):
+    def __init__(self,path,suffix='ini',refetch_stations=False):
+
+        self.path = path
+
+        self.file = self.path+'/stations_list.csv'
+        if (os.path.isfile(self.file)) and (not refetch_stations):
+            self.table = pd.read_csv(self.file)
+        else:
+            self.table = self.get_stations(suffix=suffix)
+            self.table.to_csv(self.file)
+        
+        self.table = self.table.set_index('STNID')
+        #print(self.table)
+
+    def get_stations(self,suffix):
+        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        if len(stations_list_files) == 0:
+            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        stations_list_files.sort()
+        print(stations_list_files)
+        if len(stations_list_files) == 0:
+            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
+        stations_list = []
+        for stations_list_file in stations_list_files:
+            thisfile = open(stations_list_file,'r')
+            yamlgen = yaml.load_all(thisfile)
+            try:
+                first_record  = yamlgen.__next__()
+            except:
+                first_record = None
+            if first_record is not None:
+                stations_list.append({})
+                for column in ['STNID','latitude','longitude']:
+                    #print(first_record['pars'].keys())
+                    stations_list[-1][column] = first_record['pars'][column]
+                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
+            yamlgen.close()
+            thisfile.close()
+    
+        print(stations_list)
+        return pd.DataFrame(stations_list)
+
+class stations_iterator(object):
+    def __init__(self,stations):
+        self.stations = stations
+        self.ix = -1 
+    def __iter__(self):
+        return self
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.stations.table)) 
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_row(self,row):
+        self.ix = row
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_STNID(self,STNID):
+        self.ix = np.where((self.stations.table.index == STNID))[0][0]
+        print(self.ix)
+        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+    def close():
+        del(self.ix)
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.records))
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+# #'_afternoon.yaml'
+# def get_record_yaml(yaml_file,index_start,index_end):
+#     filename = yaml_file.name
+#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+#     #yaml_file = open(filename)
+# 
+#     #print('going to next observation',filename)
+#     yaml_file.seek(index_start)
+# 
+#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+# 
+#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+#     filebuffer.write(buf)
+#     filebuffer.close()
+#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+#     
+#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+# 
+#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+#     print(command)
+#     os.system(command)
+#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
+#     record_dict = json.load(jsonstream)
+#     jsonstream.close()
+#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+#  
+#     # datetimes are incorrectly converted to strings. We need to convert them
+#     # again to datetimes
+#     for key,value in record_dict['pars'].items():
+#         # we don't want the key with columns that have none values
+#         if value is not None: 
+#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
+#            # elif (type(value) == str):
+#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+#                 
+#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
+# 
+#         if (value == 0.9e19) or (value == '.9e19'):
+#             record_dict['pars'][key] = np.nan
+#     for key in record_dict.keys():
+#         print(key)
+#         if key in ['air_ap','air_balloon',]:
+#             print('check')
+#             for datakey,datavalue in record_dict[key].items():
+#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+# 
+#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+# 
+#     c4gli = class4gl_input()
+#     c4gli.load_yaml_dict(record_dict)
+#     return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
+
+    records = pd.DataFrame()
+    for STNID,station in stations.iterrows():
+        dictfnchunks = []
+        if getchunk is 'all':
+
+            # we try the old single-chunk filename format first (usually for
+            # original profile pairs)
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(fn):
+                chunk = 0
+                dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+            # otherwise, we use the new multi-chunk filename format
+            else:
+                chunk = 0
+                end_of_chunks = False
+                while not end_of_chunks:
+                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(fn):
+                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    else:
+                        end_of_chunks = True
+                    chunk += 1
+
+            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
+            # yamlfilenames = glob.glob(globyamlfilenames)
+            # yamlfilenames.sort()
+        else:
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
+            
+        if len(dictfnchunks) > 0:
+            for dictfnchunk in dictfnchunks:
+                yamlfilename = dictfnchunk['fn']
+                chunk = dictfnchunk['chunk']
+                print(chunk)
+
+                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                generate_pkl = False
+                if not os.path.isfile(pklfilename): 
+                    print('pkl file does not exist. I generate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                elif not (os.path.getmtime(yamlfilename) <  \
+                    os.path.getmtime(pklfilename)):
+                    print('pkl file older than yaml file, so I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+
+                if refetch_records:
+                    print('refetch_records flag is True. I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                if not generate_pkl:
+                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                   # irecord = 0
+                else:
+                    with open(yamlfilename) as yaml_file:
+
+                        dictout = {}
+
+                        next_record_found = False
+                        end_of_file = False
+                        while (not next_record_found) and (not end_of_file):
+                            linebuffer = yaml_file.readline()
+                            next_record_found = (linebuffer == '---\n')
+                            end_of_file = (linebuffer == '')
+                        next_tell = yaml_file.tell()
+                        
+                        while not end_of_file:
+
+                            print(' next record:',next_tell)
+                            current_tell = next_tell
+                            next_record_found = False
+                            yaml_file.seek(current_tell)
+                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            linebuffer = ''
+                            while ( (not next_record_found) and (not end_of_file)):
+                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                linebuffer = yaml_file.readline()
+                                next_record_found = (linebuffer == '---\n')
+                                end_of_file = (linebuffer == '')
+                            filebuffer.close()
+                            
+                            next_tell = yaml_file.tell()
+                            index_start = current_tell
+                            index_end = next_tell
+
+                            
+                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            print(command)
+                            
+                            os.system(command)
+                            #jsonoutput = subprocess.check_output(command,shell=True) 
+                            #print(jsonoutput)
+                            #jsonstream = io.StringIO(jsonoutput)
+                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            record = json.load(jsonstream)
+                            dictouttemp = {}
+                            for key,value in record['pars'].items():
+                                # we don't want the key with columns that have none values
+                                if value is not None: 
+                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                   if (type(value) in regular_numeric_types):
+                                        dictouttemp[key] = value
+                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                            recordindex = record['index']
+                            dictouttemp['chunk'] = chunk
+                            dictouttemp['index_start'] = index_start
+                            dictouttemp['index_end'] = index_end
+                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            for key,value in dictouttemp.items():
+                                if key not in dictout.keys():
+                                    dictout[key] = {}
+                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                            print(' obs record registered')
+                            jsonstream.close()
+                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                    records_station = pd.DataFrame.from_dict(dictout)
+                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
+                    print('writing table file ('+pklfilename+') for station '\
+                          +str(STNID))
+                    records_station.to_pickle(pklfilename)
+                    # else:
+                    #     os.system('rm '+pklfilename)
+                    records = pd.concat([records,records_station])
+    return records
+
+def stdrel(mod,obs,columns):
+    stdrel = pd.DataFrame(columns = columns)
+    for column in columns:
+        stdrel[column] = \
+                (mod.groupby('STNID')[column].transform('mean') -
+                 obs.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') + \
+                (mod[column] -
+                 mod.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') 
+    return stdrel
+
+def pct(obs,columns):
+    pct = pd.DataFrame(columns=columns)
+    for column in columns:
+        #print(column)
+        pct[column] = ""
+        pct[column] = obs[column].rank(pct=True)
+    return pct
+
+def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (obs_afternoon.ldatetime - \
+                               obs_morning.ldatetime).dt.seconds*3600.
+    return stats
+
diff --git a/build/lib/lib/interface_multi.py b/build/lib/lib/interface_multi.py
new file mode 100644
index 0000000..83148e5
--- /dev/null
+++ b/build/lib/lib/interface_multi.py
@@ -0,0 +1,2061 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+# from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+cdictpres = {'blue': (\
+                   (0.,    0.,  0.),
+                   (0.25,  0.25, 0.25),
+                   (0.5,  .70, 0.70),
+                   (0.75, 1.0, 1.0),
+                   (1,     1.,  1.),
+                   ),
+       'green': (\
+                   (0. ,   0., 0.0),
+                   (0.25,  0.50, 0.50),
+                   (0.5,  .70, 0.70),
+                   (0.75,  0.50, 0.50),
+                   (1  ,    0,  0.),
+                   ),
+       'red':  (\
+                  (0 ,  1.0, 1.0),
+                  (0.25 ,  1.0, 1.0),
+                   (0.5,  .70, 0.70),
+                  (0.75 , 0.25, 0.25),
+                  (1,    0., 0.),
+                  )}
+
+statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+os.system('module load Ruby')
+
+class c4gl_interface_soundings(object):
+    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+        """ creates an interactive interface for analysing class4gl experiments
+
+        INPUT:
+            path_exp : path of the experiment output
+            path_obs : path of the observations 
+            globaldata: global data that is being shown on the map
+            refetch_stations: do we need to build the list of the stations again?
+        OUTPUT:
+            the procedure returns an interface object with interactive plots
+
+        """
+        
+        # set the ground
+        self.globaldata = globaldata
+
+ 
+        self.path_exp = path_exp
+        self.path_obs = path_obs
+        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
+
+        # # get the list of stations
+        # stationsfile = self.path_exp+'/stations_list.csv'
+        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
+        #     stations = pd.read_csv(stationsfile)
+        # else:
+        #     stations = get_stations(self.path_exp)
+        #     stations.to_csv(stationsfile)
+
+        # stations = stations.set_index('STNID')
+
+        self.frames = {}
+
+        self.frames['stats'] = {}
+        self.frames['worldmap'] = {}
+                
+        self.frames['profiles'] = {}
+        self.frames['profiles'] = {}
+        self.frames['profiles']['DT'] = None
+        self.frames['profiles']['STNID'] = None
+
+        #self.frames['worldmap']['stationsfile'] = stationsfile
+        self.frames['worldmap']['stations'] = stations(self.path_exp, \
+                                                       suffix='ini',\
+                                                       refetch_stations=refetch_stations)
+
+        # Initially, the stats frame inherets the values/iterators of
+        # worldmap
+        for key in self.frames['worldmap'].keys():
+            self.frames['stats'][key] = self.frames['worldmap'][key]
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_ini'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='ini',\
+                                           refetch_records=refetch_records
+                                           )
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_mod'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='mod',\
+                                           refetch_records=refetch_records
+                                           )
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_obs,\
+                                           subset='afternoon',\
+                                           refetch_records=refetch_records
+                                           )
+
+        self.frames['stats']['records_all_stations_mod'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['records_all_stations_ini']['dates'] = \
+            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+
+
+        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+
+        self.frames['stats']['records_all_stations_obs_afternoon'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['viewkeys'] = ['h','theta','q']
+        print('Calculating table statistics')
+        self.frames['stats']['records_all_stations_mod_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_mod'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+
+        self.frames['stats']['inputkeys'] = inputkeys
+        
+        # self.frames['stats']['inputkeys'] = \
+        #     [ key for key in \
+        #       self.globaldata.datasets.keys() \
+        #       if key in \
+        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
+
+
+        # get units from the class4gl units database
+        self.units = dict(units)
+        # for those that don't have a definition yet, we just ask a question
+        # mark
+        for var in self.frames['stats']['inputkeys']:
+            self.units[var] = '?'
+
+        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
+        self.frames['stats']['records_all_stations_ini_pct'] = \
+                  pct(self.frames['stats']['records_all_stations_ini'], \
+                      columns = self.frames['stats']['inputkeys'])
+
+        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
+        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+        #     mod['
+
+        # 
+        # 
+        # \
+        #        self.frames['stats']['records_all_stations_mod'], \
+
+
+
+        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        #               columns = [ 'd'+key+'dt' for key in \
+        #                           self.frames['stats']['viewkeys']], \
+        #              )
+
+        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
+        #               obs = self.frames['stats']['records_all_stations_ini'], \
+        #               columns = self.frames['stats']['viewkeys'], \
+        #              )
+        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+        
+        print('filtering pathological data')
+        # some observational sounding still seem problematic, which needs to be
+        # investigated. In the meantime, we filter them
+        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+        # we filter ALL data frames!!!
+        for key in self.frames['stats'].keys():
+            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+               (self.frames['stats'][key].index.names == indextype):
+                self.frames['stats'][key] = self.frames['stats'][key][valid]
+        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+
+
+        print("filtering stations from interface that have no records")
+        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
+            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                    == STNID).sum() == 0):
+                print("dropping", STNID)
+                self.frames['worldmap']['stations'].table = \
+                        self.frames['worldmap']['stations'].table.drop(STNID)
+                    
+        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+        
+        # TO TEST: should be removed, since it's is also done just below
+        self.frames['stats']['stations_iterator'] = \
+            self.frames['worldmap']['stations_iterator'] 
+
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
+        self.next_station()
+
+        # self.goto_datetime_worldmap(
+        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+        #     'after')
+    def sel_station(self,STNID=None,rownumber=None):
+
+        if (STNID is not None) and (rownumber is not None):
+            raise ValueError('Please provide either STNID or rownumber, not both.')
+
+        if (STNID is None) and (rownumber is None):
+            raise ValueError('Please provide either STNID or rownumber.')
+            
+        if STNID is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
+            print(
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+            )
+            self.update_station()
+        elif rownumber is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
+            self.update_station()
+
+
+
+    def next_station(self,event=None,jump=1):
+        with suppress(StopIteration):
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+                = self.frames['worldmap']['stations_iterator'].__next__(jump)
+            # self.frames['worldmap']['stations_iterator'].close()
+            # del(self.frames['worldmap']['stations_iterator'])
+            # self.frames['worldmap']['stations_iterator'] = \
+            #                 selfself.frames['worldmap']['stations'].iterrows()
+            # self.frames['worldmap']['STNID'],\
+            # self.frames['worldmap']['current_station'] \
+            #     = self.frames['worldmap']['stations_iterator'].__next__()
+
+        self.update_station()
+
+    def prev_station(self,event=None):
+        self.next_station(jump = -1,event=event)
+    def update_station(self):
+        for key in ['STNID','current_station','stations_iterator']: 
+            self.frames['stats'][key] = self.frames['worldmap'][key] 
+
+
+
+        # generate index of the current station
+        self.frames['stats']['records_current_station_index'] = \
+            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+             == \
+             self.frames['stats']['current_station'].name)
+
+        # create the value table of the records of the current station
+        tab_suffixes = \
+                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        for tab_suffix in tab_suffixes:
+            self.frames['stats']['records_current_station'+tab_suffix] = \
+                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+        # go to first record of current station
+        self.frames['stats']['records_iterator'] = \
+                        records_iterator(self.frames['stats']['records_current_station_mod'])
+        (self.frames['stats']['STNID'] , \
+        self.frames['stats']['current_record_chunk'] , \
+        self.frames['stats']['current_record_index']) , \
+        self.frames['stats']['current_record_mod'] = \
+                        self.frames['stats']['records_iterator'].__next__()
+
+        for key in self.frames['stats'].keys():
+            self.frames['profiles'][key] = self.frames['stats'][key]
+
+        STNID = self.frames['profiles']['STNID']
+        chunk = self.frames['profiles']['current_record_chunk']
+        if 'current_station_file_ini' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_ini'].close()
+        self.frames['profiles']['current_station_file_ini'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+        if 'current_station_file_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_mod'].close()
+        self.frames['profiles']['current_station_file_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_afternoon'].close()
+        self.frames['profiles']['current_station_file_afternoon'] = \
+            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+        self.frames['profiles']['records_iterator'] = \
+                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+        (self.frames['profiles']['STNID'] , \
+        self.frames['profiles']['current_record_chunk'] , \
+        self.frames['profiles']['current_record_index']) , \
+        self.frames['profiles']['current_record_mod'] = \
+                        self.frames['profiles']['records_iterator'].__next__()
+
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+
+        self.update_record()
+
+    def next_record(self,event=None,jump=1):
+        with suppress(StopIteration):
+            (self.frames['profiles']['STNID'] , \
+            self.frames['profiles']['current_record_chunk'] , \
+            self.frames['profiles']['current_record_index']) , \
+            self.frames['profiles']['current_record_mod'] = \
+                      self.frames['profiles']['records_iterator'].__next__(jump)
+        # except (StopIteration):
+        #     self.frames['profiles']['records_iterator'].close()
+        #     del( self.frames['profiles']['records_iterator'])
+        #     self.frames['profiles']['records_iterator'] = \
+        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #     (self.frames['profiles']['STNID'] , \
+        #     self.frames['profiles']['current_record_index']) , \
+        #     self.frames['profiles']['current_record_mod'] = \
+        #                     self.frames['profiles']['records_iterator'].__next__()
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        self.update_record()
+
+    def prev_record(self,event=None):
+        self.next_record(jump=-1,event=event)
+
+    def update_record(self):
+        self.frames['profiles']['current_record_ini'] =  \
+            self.frames['profiles']['records_current_station_ini'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'],\
+                  self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon'] =  \
+            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'] , \
+                  self.frames['profiles']['current_record_index'])]
+
+        self.frames['profiles']['current_record_mod_stats'] = \
+                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                    self.frames['profiles']['STNID'], \
+                    self.frames['profiles']['current_record_chunk'], \
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_ini_pct'] = \
+                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        # frame
+        # note that the current station, record is the same as the stats frame for initialization
+
+        # select first 
+        #self.frames['profiles']['current_record_index'], \
+        #self.frames['profiles']['record_yaml_mod'] = \
+        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
+        #                   self.frames['stats']['current_record_index'])
+        self.frames['profiles']['record_yaml_mod'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_mod'], \
+               self.frames['profiles']['current_record_mod'].index_start,
+               self.frames['profiles']['current_record_mod'].index_end,
+               mode='mod')
+                                
+        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_ini'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_ini'], \
+               record_ini.index_start,
+               record_ini.index_end,
+                mode='ini')
+
+        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_obs_afternoon'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_afternoon'], \
+               record_afternoon.index_start,
+               record_afternoon.index_end,
+                mode='ini')
+
+
+        key = self.frames['worldmap']['inputkey']
+        # only redraw the map if the current world map has a time
+        # dimension
+        if 'time' in self.globaldata.datasets[key].page[key].dims:
+            self.goto_datetime_worldmap(
+                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                'after')
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap',
+                                                  'profiles'])
+        else:
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap_stations',
+                                                  'profiles'])
+
+    def abline(self,slope, intercept,axis):
+        """Plot a line from slope and intercept"""
+        #axis = plt.gca()
+        x_vals = np.array(axis.get_xlim())
+        y_vals = intercept + slope * x_vals
+        axis.plot(x_vals, y_vals, 'k--')
+
+    def plot(self):
+        import pylab as pl
+        from matplotlib.widgets import Button
+        import matplotlib.pyplot as plt
+        import matplotlib as mpl
+        '''
+        Definition of the axes for the sounding table stats
+        '''
+        
+        fig = pl.figure(figsize=(14,9))
+        axes = {} #axes
+        btns = {} #buttons
+
+        # frames, which sets attributes for a group of axes, buttens, 
+        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            label = 'stats_'+str(key)
+            axes[label] = fig.add_subplot(\
+                            len(self.frames['stats']['viewkeys']),\
+                            5,\
+                            5*ikey+1,label=label)
+            # Actually, the axes should be a part of the frame!
+            #self.frames['stats']['axes'] = axes[
+
+            # pointer to the axes' point data
+            axes[label].data = {}
+
+            # pointer to the axes' color fields
+            axes[label].fields = {}
+
+
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
+
+        label ='stats_colorbar'
+        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
+        axes[label].fields = {}
+
+        from matplotlib.colors import LinearSegmentedColormap
+        cdictpres = {'blue': (\
+                           (0.,    0.,  0.),
+                           (0.25,  0.25, 0.25),
+                           (0.5,  .70, 0.70),
+                           (0.75, 1.0, 1.0),
+                           (1,     1.,  1.),
+                           ),
+               'green': (\
+                           (0. ,   0., 0.0),
+                           (0.25,  0.50, 0.50),
+                           (0.5,  .70, 0.70),
+                           (0.75,  0.50, 0.50),
+                           (1  ,    0,  0.),
+                           ),
+               'red':  (\
+                          (0 ,  1.0, 1.0),
+                          (0.25 ,  1.0, 1.0),
+                           (0.5,  .70, 0.70),
+                          (0.75 , 0.25, 0.25),
+                          (1,    0., 0.),
+                          )}
+        
+        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+        label = 'times'
+               
+        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+
+
+        label = 'worldmap'
+               
+        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+        axes[label].lat = None
+        axes[label].lon = None
+
+        label = 'worldmap_colorbar'
+        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+        axes[label].fields = {}
+
+        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
+        label = 'worldmap_stations'
+        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label].data = {}
+
+        fig.canvas.mpl_connect('pick_event', self.on_pick)
+        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
+
+
+        """ buttons definitions """
+        
+        label = 'bprev_dataset'
+        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous dataset')
+        btns[label].on_clicked(self.prev_dataset)
+
+        label = 'bnext_dataset'
+        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next dataset')
+        btns[label].on_clicked(self.next_dataset)
+
+        label = 'bprev_datetime'
+        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous datetime')
+        btns[label].on_clicked(self.prev_datetime)
+
+        label = 'bnext_datetime'
+        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next datetime')
+        btns[label].on_clicked(self.next_datetime)
+
+
+        label = 'bprev_station'
+        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous station')
+        btns[label].on_clicked(self.prev_station)
+
+        label = 'bnext_station'
+        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next station')
+        btns[label].on_clicked(self.next_station)
+
+        label = 'bprev_record'
+        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous record')
+        btns[label].on_clicked(self.prev_record)
+
+        label = 'bnext_record'
+        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next record')
+        btns[label].on_clicked(self.next_record)
+
+
+        # self.nstatsview = nstatsview
+        # self.statsviewcmap = statsviewcmap
+        self.fig = fig
+        self.axes = axes
+        self.btns = btns
+        self.tbox = {}
+        # self.hover_active = False
+
+        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
+        #                                transform=plt.gcf().transFigure)
+
+        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
+                                          transform=plt.gcf().transFigure)
+
+        label = 'air_ap:theta'
+        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
+
+        label = 'air_ap:q'
+        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
+
+        label = 'out:h'
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+
+        label = 'out:theta'
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+
+        label = 'out:q'
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+
+        label = 'SEB'
+        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
+
+
+        self.hover_active = False
+        self.fig = fig
+        self.fig.show()
+        self.fig.canvas.draw()
+        self.refresh_plot_interface()
+
+
+    # def scan_stations(self):
+    #     blabla
+        
+
+
+    # def get_records(current_file):
+    #     records = pd.DataFrame()
+
+    #     # initial position
+    #     next_record_found = False
+    #     while(not next_record_found):
+    #         next_record_found = (current_file.readline() == '---\n')
+    #     next_tell = current_file.tell() 
+    #     end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #     while not end_of_file:
+    #         current_tell = next_tell
+    #         next_record_found = False
+    #         current_file.seek(current_tell)
+    #         while ( (not next_record_found) and (not end_of_file)):
+    #             current_line = current_file.readline()
+    #             next_record_found = (currentline == '---\n')
+    #             end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #         # we store the position of the next record
+    #         next_tell = current_file.tell() 
+    #         
+    #         # we get the current record. Unfortunately we need to reset the
+    #         # yaml record generator first.
+    #         current_yamlgen.close()
+    #         current_yamlgen = yaml.load_all(current_file)
+    #         current_file.seek(current_tell)
+    #         current_record_mod = current_yamlgen.__next__()
+    #     current_yamlgen.close()
+
+    #     return records
+
+       #      next_record_found = False
+       #      while(not record):
+       #          next_record_found = (self.current_file.readline() == '---\n')
+       #      self.current_tell0 = self.current_file.tell() 
+
+       #  
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell0 = self.current_file.tell() 
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell1 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell0)
+       #  self.r0 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell1)
+       #  next_record_found = False
+       #  while ( (not next_record_found) and (not end_of_file):
+       #      current_line = self.current_file.readline()
+       #      next_record_found = (currentline == '---\n')
+       #      end_of_file = (currentline == '') # an empty line means we are at the end
+
+       #  self.current_tell2 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell1)
+       #  self.r1 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell2)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell3 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell2)
+       #  self.r2 = self.current_yamlgen.__next__()
+
+       #  # go to position of next record in file
+       #  self.current_file.seek(self.current_tell3)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell4 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell3)
+       #  self.r3 = self.current_yamlgen.__next__()
+ 
+       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
+
+    def goto_datetime_worldmap(self,DT,shift=None):
+        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                self.frames['worldmap']['iDT'] += 1
+            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                self.frames['worldmap']['iDT'] -= 1 
+            # for gleam, we take the values of the previous day
+            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                self.frames['worldmap']['iDT'] -= 2 
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+        #else:
+        #    self.frames['worldmap'].pop('DT')
+
+    def next_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def prev_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def next_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+
+
+    def sel_dataset(self,inputkey):
+        self.frames['worldmap']['inputkey'] = inputkey
+        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
+        self.goto_datetime_worldmap(
+            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+            'after')# get nearest datetime of the current dataset to the profile
+        if "fig" in self.__dict__.keys():
+            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+       
+    # def prev_station(self,event=None):
+    #     self.istation = (self.istation - 1) % self.stations.shape[0]
+    #     self.update_station()
+
+
+
+
+    #def update_datetime(self):
+    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
+    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
+    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
+    #        print(self.worldmapfocus['DT'])
+    #        self.refresh_plot_interface(only='worldmap')
+
+    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
+
+        #print('r1')
+        for argkey in args.keys():
+            self.__dict__[arg] = args[argkey]
+
+        axes = self.axes
+        tbox = self.tbox
+        frames = self.frames
+        fig = self.fig
+ 
+        if (only is None) or ('worldmap' in only):
+            globaldata = self.globaldata
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+            else:
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+            keystotranspose = ['lat','lon']
+            for key in dict(datasetxr.dims).keys():
+                if key not in keystotranspose:
+                    keystotranspose.append(key)
+
+            datasetxr = datasetxr.transpose(*keystotranspose)
+            datasetxr = datasetxr.sortby('lat',ascending=False)
+
+            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+            lonleft = lonleft - 360.
+            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+            label = 'worldmap'
+            axes[label].clear()
+            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+
+        if (only is None) or ('worldmap' in only):
+            #if 'axmap' not in self.__dict__ :
+            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
+            #else:
+
+            #stations = self.stations
+
+
+            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
+            #     resolution = 'l', 
+            # area_thresh = 0.1,
+            #     llcrnrlon=-180., llcrnrlat=-90.0,
+            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
+            # 
+            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
+            # self.gmap.drawcountries(color='white',linewidth=0.3)
+            # #self.gmap.fillcontinents(color = 'gray')
+            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
+            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
+            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
+            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # #self.ax5.shadedrelief()
+
+           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
+
+
+            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+
+            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
+            if 'lev' in field.dims:
+                field = field.isel(lev=-1)
+
+            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+            axes[label].axis('off')
+
+            from matplotlib import cm
+            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+            
+            
+            title=frames['worldmap']['inputkey']
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            axes[label].set_title(title)
+
+            label ='worldmap_colorbar'
+            axes[label].clear()
+            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+
+
+            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+            # x,y = self.gmap(lons,lats)
+            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+
+        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+
+            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            store_xlim = {}
+            store_ylim = {}
+            for ikey, key in enumerate(statskeys_out):
+                if (only is not None) and ('stats_lightupdate' in only):
+                    store_xlim[key] = axes['stats_'+key].get_xlim()
+                    store_ylim[key] = axes['stats_'+key].get_ylim()
+                self.axes['stats_'+key].clear()    
+
+            label = 'times'
+            self.axes[label].clear()
+
+            key = 'dthetadt'
+            x = self.frames['stats']['records_all_stations_ini']['datetime']
+            #print(x)
+            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            #print(y)
+            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            #print(z)
+
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            self.axes[label].data[label] = self.axes[label].scatter(x.values,
+                                                                    y.values,
+                                                                    c=z.values,
+                                                                    cmap=self.statsviewcmap,
+                                                                    s=2,
+                                                                    vmin=0.,
+                                                                    vmax=1.,
+                                                                    alpha=alpha_cloud_pixels)
+
+            
+            x = self.frames['stats']['records_current_station_ini']['datetime']
+            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+
+            x = self.frames['profiles']['records_current_station_ini']['datetime']
+            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
+            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
+
+            for ikey, key in enumerate(statskeys_out):
+
+                # show data of all stations
+                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                qvalmax = x.quantile(0.999)
+                qvalmin = x.quantile(0.001)
+                print('applying extra filter over extreme values for plotting stats')
+                selx = (x >= qvalmin) & (x < qvalmax)
+                sely = (x >= qvalmin) & (x < qvalmax)
+                x = x[selx & sely]
+                y = y[selx & sely]
+                z = z[selx & sely]
+                self.axes['stats_'+key].data['stats_'+key] = \
+                       self.axes['stats_'+key].scatter(x,y, c=z,\
+                                cmap=self.statsviewcmap,\
+                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
+
+                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
+
+                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
+                y = self.frames['stats']['current_record_mod_stats'][key]
+                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
+                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
+                    axes['stats_'+key].annotate(text, \
+                                               xy=(x,y),\
+                                               xytext=(0.05,0.05),\
+                                               textcoords='axes fraction',\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               color='white',\
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                # self.axes['stats_'+key].data[key+'_current_record'] = \
+                #        self.axes['stats_'+key].scatter(x,y, c=z,\
+                #                 cmap=self.statsviewcmap,\
+                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
+
+                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
+                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
+                # # highlight data for curent station
+                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+
+                if ikey == len(statskeys_out)-1:
+                    self.axes['stats_'+key].set_xlabel('external')
+                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
+                axes['stats_'+key].set_ylabel('model')
+
+
+                if (only is not None) and ('stats_lightupdate' in only):
+                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
+                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
+                else:
+                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
+                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
+                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
+                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
+                self.abline(1,0,axis=self.axes['stats_'+key])
+
+        if (only is None) or ('stats_colorbar' in only):
+            label ='stats_colorbar'
+            axes[label].clear()
+            import matplotlib as mpl
+            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
+            self.axes[label].fields[label] = \
+             mpl.colorbar.ColorbarBase(self.axes[label],\
+                        orientation='horizontal',\
+                        label="percentile of "+self.frames['worldmap']['inputkey'],
+                        alpha=1.,
+                                cmap=self.statsviewcmap,\
+                                       norm=norm
+                         )
+
+        #print('r1')
+        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
+            #print('r2')
+            label = 'worldmap_stations'
+            axes[label].clear()
+            
+            stations = self.frames['worldmap']['stations'].table
+            globaldata = self.globaldata
+            
+            key = label
+
+            #print('r3')
+            if (stations is not None):
+                xlist = []
+                ylist = []
+                #print('r4')
+                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
+            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    xlist.append(x)
+                    ylist.append(y)
+                #picker is needed to make it clickable (pick_event)
+                axes[label].data[label] = axes[label].scatter(xlist,ylist,
+                                                              c='r', s=15,
+                                                              picker = 15,
+                                                              label=key,
+                                                              edgecolor='k',
+                                                              linewidth=0.8)
+
+            # cb.set_label('Wilting point [kg kg-3]')
+                #print('r5')
+
+                
+            #     xseries = []
+            #     yseries = []
+            #     for iSTN,STN in stations.iterrows():
+            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
+            #         xseries.append(x)                    
+            #         yseries.append(y)
+            #         
+            #         
+            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
+                    
+                if ('current_station' in frames['worldmap']):
+                    #print('r5')
+                    STN = frames['stats']['current_station']
+                    STNID = frames['stats']['STNID']
+                    #print('r5')
+
+                    x,y = len(axes['worldmap'].lon)* \
+                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
+                          len(axes['worldmap'].lat)* \
+                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    #print('r6')
+                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
+                    #                          == \
+                    #                          self.frames['worldmap']['STNID'])\
+                    #                         & \
+                    #                         (self.seltablestats['DT'] \
+                    #                          == self.axes['statsview0].focus['DT']) \
+                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
+                    #print('r7')
+                    text = 'STNID: '+ format(STNID,'10.0f') + \
+                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
+                            ', LON: '+format(STN['longitude'],'3.3f')+ \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+
+                            #+', VAL: '+format(VAL,'.3e')
+
+                    axes[label].scatter(x,y, c='r', s=30,\
+                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
+                    #print('r8')
+            
+                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
+                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
+                    #colorstation = max((min((1.,colorstation)),0.))
+                    colorstation =0.2
+                    from matplotlib import cm
+                    axes[label].annotate(text,
+                                         xy=(x,y),
+                                         xytext=(0.05,0.05),
+                                         textcoords='axes fraction', 
+                                         bbox=dict(boxstyle="round",
+                                         fc = cm.viridis(colorstation)),
+                                         arrowprops=dict(arrowstyle="->",
+                                                         linewidth=1.1),
+                                         color='white' if colorstation < 0.5 else 'black')
+                    #print('r9')
+
+                    # #pos = sc.get_offsets()[ind["ind"][0]]
+                    # 
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label].data[label+'statannotate'].set_text(text)
+                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
+                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
+            #print('r9')
+            axes[label].axis('off')
+            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
+            axes[label].set_ylim((len(axes['worldmap'].lat),0))
+            #print('r10')
+
+        if (only is None) or ('profiles' in only): 
+            #print('r11')
+
+            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
+            # # self.update_station(goto_first_sounding=False)
+            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
+            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
+            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
+            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
+
+            label = 'air_ap:theta'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # +\
+                # ' -> '+ \
+                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+            
+            
+            
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+            #print('r12')
+
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
+            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
+            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            #print('r13')
+            # 
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r14')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+
+            #print('r15')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+                          
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r16')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r17')
+            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            print(hmax)
+            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            if valid_mod:
+
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="mod "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+
+            #print('r18')
+            axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('theta [K]')
+
+            label = 'air_ap:q'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+
+            #print('r19')
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            if valid_mod:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            else:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            # 
+            #print('r20')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r21')
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            #print('r23')
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r24')
+            if valid_mod:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="fit ")#+\
+                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #+'LT')
+            #print('r25')
+            #axes[label].legend()
+
+            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            #axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('q [kg/kg]')
+
+            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+
+            # #pl.subplots_adjust(right=0.6)
+
+            # label = 'q_pro'
+            # axes[label].clear()
+
+            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
+            # 
+            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
+            # 
+            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
+
+            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
+            # #pl.subplots_adjust(right=0.6)
+            # axes[label].set_xlabel('specific humidity [kg/kg]')
+ 
+
+            #print('r26')
+            time = self.frames['profiles']['record_yaml_mod'].out.time
+            for ilabel,label in enumerate(['h','theta','q']):
+                axes["out:"+label].clear()
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].set_ylabel(label)
+                if ilabel == 2:
+                    axes["out:"+label].set_xlabel('local sun time [h]')
+                
+            #print('r27')
+            label = 'SEB'
+            axes[label].clear()
+            
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
+            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
+            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
+                
+            #print('r28')
+            
+            axes[label].legend()
+            
+            #         for ax in self.fig_timeseries_axes:
+#             ax.clear()
+#         
+#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
+#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
+#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
+#         #print(self.morning_sounding.c4gl.out.Swin)
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
+#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
+#         self.fig_timeseries_axes[3].legend()
+#         self.fig.canvas.draw()
+            
+
+
+
+
+
+
+        #self.ready()
+        #print('r29')
+        fig.canvas.draw()
+        #fig.show()
+
+        self.axes = axes
+        self.tbox = tbox
+        self.fig = fig
+
+    def on_pick(self,event):
+        #print("HELLO")
+        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
+        #self.axes['theta_pro'].clear()
+        #self.axes['q_pro'].clear()
+        
+
+        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
+        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
+        keys_to_axes = {}
+        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
+
+        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
+        keys_to_axes['worldmap'] = 'worldmap'
+        
+        axes = self.axes
+        #nstatsview = self.nstatsview
+        #statsviewcmap = self.statsviewcmap
+        stations = self.frames['worldmap']['stations'].table
+
+
+        #print("p1")
+        current = event
+        artist = event.artist
+        
+        selkey = artist.get_label()
+        
+        #print(keys_to_axes)
+        
+        label = keys_to_axes[selkey]
+        #print("HELLO",selkey,label)
+
+        # # Get to know in which axes we are
+        # label = None
+        # for axeskey in axes.keys():
+        #     if event.inaxes == axes[axeskey]:
+        #         label = axeskey
+        #         
+
+        # cont, pos = None, None
+        
+        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
+        ind = event.ind
+        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
+        d = axes[label].collections[0]
+        #d.set_offset_position('data')
+        xy = d.get_offsets()
+        x, y =  xy[:,0],xy[:,1]
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+
+        #print("p2")
+        if len(ind) > 0:
+            #print("p3")
+            pos = x[ind[0]], y[ind[0]]
+
+            #if label[:-1] == 'statsview':
+            #    #seltablestatsstdrel = self.seltablestatsstdrel
+            #    #seltablestatspct = self.seltablestatspct
+
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    
+            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
+            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+            #    
+            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
+            #el
+            if (label == 'worldmap') or (label == 'worldmap_stations'):
+                self.hover_active = False
+                if (self.frames['worldmap']['STNID'] !=
+                    self.frames['profiles']['STNID']):
+                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
+                # so we just need to perform update_station
+                    self.update_station()
+            elif (label[:5] == 'stats'):
+
+                self.hover_active = False
+                if (self.frames['stats']['STNID'] !=
+                self.frames['profiles']['STNID']) or \
+                   (self.frames['stats']['current_record_chunk'] != 
+                    self.frames['profiles']['current_record_chunk']) or \
+                   (self.frames['stats']['current_record_index'] != 
+                    self.frames['profiles']['current_record_index']):
+
+
+
+                    for key in ['STNID','current_station','stations_iterator']: 
+                        self.frames['worldmap'][key] = self.frames['stats'][key] 
+
+                    for key in self.frames['stats'].keys():
+                        self.frames['profiles'][key] = self.frames['stats'][key]
+
+                    STNID = self.frames['profiles']['STNID']
+                    chunk = self.frames['profiles']['current_record_chunk']
+                    if 'current_station_file_ini' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_ini'].close()
+                    self.frames['profiles']['current_station_file_ini'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+                    if 'current_station_file_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_mod'].close()
+                    self.frames['profiles']['current_station_file_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_afternoon'].close()
+                    self.frames['profiles']['current_station_file_afternoon'] = \
+                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+                    # go to hovered record of current station
+                    self.frames['profiles']['records_iterator'] = \
+                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # ... and go to the record of the profile window (last one that
+                    # was picked by the user)
+                    found = False
+                    EOF = False
+                    while (not found) and (not EOF):
+                        try:
+                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
+                            #print("hello*")
+                            #print(self.frames['profiles']['current_record_index'])
+                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
+                               (index == self.frames['profiles']['current_record_index']) and \
+                               (STNID == self.frames['profiles']['STNID']):
+                                #print('found!')
+                                found = True
+                        except StopIteration:
+                            EOF = True
+                    if found:
+                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_chunk'] = chunk
+                        self.frames['stats']['current_record_index'] = index
+                    # # for the profiles we make a distinct record iterator, so that the
+                    # # stats iterator can move independently
+                    # self.frames['profiles']['records_iterator'] = \
+                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # (self.frames['profiles']['STNID'] , \
+                    # self.frames['profiles']['current_record_index']) , \
+                    # self.frames['profiles']['current_record_mod'] = \
+                    #                 self.frames['profiles']['records_iterator'].__next__()
+
+
+                    # for the profiles we make a distinct record iterator, so that the
+                    # stats iterator can move independently
+
+                    self.update_record()
+
+
+
+    def on_plot_hover(self,event):
+        axes = self.axes
+        #print('h1')
+
+        # Get to know in which axes we are
+        label = None
+        for axeskey in axes.keys():
+            if event.inaxes == axes[axeskey]:
+                label = axeskey
+                
+        #print('h2')
+
+        cont, pos = None, None
+        #print (label)
+        
+        if label is not None:
+            if  ('data' in axes[label].__dict__.keys()) and \
+                (label in axes[label].data.keys()) and \
+                (axes[label].data[label] is not None):
+                
+                #print('h3')
+                cont, ind =  axes[label].data[label].contains(event)
+                selkey = axes[label].data[label].get_label()
+                if len(ind["ind"]) > 0:
+                    #print('h4')
+                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
+                    #print('pos',pos,selkey)
+
+
+                    #if label[:-1] == 'statsview':
+                    #    seltablestatsstdrel = self.seltablestatsstdrel
+                    #    seltablestatspct = self.seltablestatspct
+
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
+                    #    self.hover_active = True
+                    #    
+                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
+                    #    
+                    #el
+                    #print(label[:5])
+                    if (label[:5] == 'stats') or (label == 'times'):
+                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        
+
+                        if label[:5] == 'stats':
+                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            (self.frames['stats']['STNID'] ,
+                             self.frames['stats']['current_record_chunk'], 
+                             self.frames['stats']['current_record_index']) = \
+                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        # elif label[:5] == 'stats':
+                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                        #     (self.frames['stats']['STNID'] ,
+                        #      self.frames['stats']['current_record_chunk'], 
+                        #      self.frames['stats']['current_record_index']) = \
+                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+
+
+                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+                        
+                        # # TO TEST: should be removed, since it's is also done just below
+                        # self.frames['stats']['stations_iterator'] = \
+                        #     self.frames['worldmap']['stations_iterator'] 
+                
+                
+                        # self.goto_datetime_worldmap(
+                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+                        #     'after')
+
+
+                        # scrolling to the right station
+                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                        EOF = False
+                        found = False
+                        while (not found and not EOF):
+                            if (STNID == self.frames['stats']['STNID']):
+                                   found = True 
+                            if not found:
+                                try:
+                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                                except (StopIteration):
+                                    EOF = True
+                        if found:
+                        #    self.frames['stats']['STNID'] = STNID
+                            self.frames['stats']['current_station'] =  station
+
+                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
+                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
+
+
+                        # generate index of the current station
+                        self.frames['stats']['records_current_station_index'] = \
+                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                             == self.frames['stats']['STNID'])
+
+
+                        tab_suffixes = \
+                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            self.frames['stats']['records_current_station'+tab_suffix] = \
+                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+
+                        # go to hovered record of current station
+                        self.frames['stats']['records_iterator'] = \
+                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+
+
+                        # ... and go to the record of the profile window (last one that
+                        # was picked by the user)
+                        found = False
+                        EOF = False
+                        while (not found) and (not EOF):
+                            try:
+                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                                #print("hello*")
+                                #print(self.frames['profiles']['current_record_index'])
+                                if (index == self.frames['stats']['current_record_index']) and \
+                                   (chunk == self.frames['stats']['current_record_chunk']) and \
+                                   (STNID == self.frames['stats']['STNID']):
+                                    #print('found!')
+                                    found = True
+                            except StopIteration:
+                                EOF = True
+                        if found:
+                            #print('h5')
+                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_chunk'] = chunk
+                            self.frames['stats']['current_record_index'] = index
+
+                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
+                        tab_suffixes = \
+                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            #print(tab_suffix)
+                            #print(self.frames['stats']['records_current_station'+tab_suffix])
+                            self.frames['stats']['current_record'+tab_suffix] =  \
+                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                      (self.frames['stats']['STNID'] , \
+                                       self.frames['stats']['current_record_chunk'] , \
+                                       self.frames['stats']['current_record_index'])]
+
+
+                        self.hover_active = True
+                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                        # print('h13')
+                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
+                        #     self.goto_datetime_worldmap(
+                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                        #         'after')
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap',
+                        #                                           'profiles'])
+                        # else:
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap_stations',
+                        #                                           'profiles'])
+
+
+
+                    elif label in ['worldmap_stations','worldmap']:
+                        #print('h5')
+
+                        if (self.axes['worldmap'].lat is not None) and \
+                           (self.axes['worldmap'].lon is not None):
+
+
+                            #self.loading()
+                            self.fig.canvas.draw()
+                            self.fig.show()
+
+
+                            # get position of 
+                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
+                                                                 self.axes['worldmap'].lat[0]) + \
+                                           self.axes['worldmap'].lat[0],4)
+                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
+                                                                 self.axes['worldmap'].lon[0]) + \
+                                           self.axes['worldmap'].lon[0],4)
+                        
+                            stations = self.frames['worldmap']['stations'].table
+                            #print('h7')
+                        
+                            #reset stations iterator:
+                            # if 'stations_iterator' in self.frames['worldmap'].keys():
+                            #     self.frames['worldmap']['stations_iterator'].close()
+                            #     del(self.frames['worldmap']['stations_iterator'])
+                            # if 'stations_iterator' in self.frames['stats'].keys():
+                            #     self.frames['stats']['stations_iterator'].close()
+                            #     del(self.frames['stats']['stations_iterator'])
+                            self.frames['worldmap']['stations_iterator'] =\
+                               stations_iterator(self.frames['worldmap']['stations'])
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                            EOF = False
+                            found = False
+                            while (not found and not EOF):
+                                #print('h8',station.latitude,latmap)
+                                #print('h8',station.longitude,lonmap)
+                                if (round(station.latitude,3) == round(latmap,3)) and \
+                                    (round(station.longitude,3) == round(lonmap,3)):
+                                       found = True 
+                                if not found:
+                                    try:
+                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                                    except (StopIteration):
+                                        EOF = True
+                            if found:
+                                self.frames['worldmap']['STNID'] = STNID
+                                self.frames['worldmap']['current_station'] = \
+                                        station
+                        
+                            self.frames['stats']['stations_iterator'] = \
+                                self.frames['worldmap']['stations_iterator'] 
+                            #print('h8')
+                            # inherit station position for the stats frame...
+                            for key in self.frames['worldmap'].keys():
+                                self.frames['stats'][key] = self.frames['worldmap'][key]
+                                
+                            ## fetch records of current station...
+                            #self.frames['stats']['records_current_station_mod'] =\
+                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                            # ... and their indices
+                            self.frames['stats']['records_current_station_index'] = \
+                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                                     == \
+                                     self.frames['stats']['current_station'].name)
+
+
+                            tab_suffixes = \
+                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['records_current_station'+tab_suffix] = \
+                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+                            # ... create a record iterator ...
+                            #self.frames['stats']['records_iterator'].close()
+                            del(self.frames['stats']['records_iterator'])
+                            self.frames['stats']['records_iterator'] = \
+                                self.frames['stats']['records_current_station_mod'].iterrows()
+
+
+
+                        
+                            #print('h9')
+                            # ... and go to to the first record of the current station
+                            (self.frames['stats']['STNID'] , \
+                             self.frames['stats']['current_record_chunk'] , \
+                             self.frames['stats']['current_record_index']) , \
+                            self.frames['stats']['current_record_mod'] = \
+                                self.frames['stats']['records_iterator'].__next__()
+                        
+
+
+
+                            #print('h10')
+                            # cash the current record
+                            tab_suffixes = \
+                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['current_record'+tab_suffix] =  \
+                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                          (self.frames['stats']['STNID'] , \
+                                           self.frames['stats']['current_record_chunk'] , \
+                                           self.frames['stats']['current_record_index'])]
+
+                            #print('h11')
+                            
+                            self.hover_active = True
+                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                            #print('h13')
+
+                        
+
+            #if (stations is not None):
+            #    for iSTN,STN in stations.iterrows():
+            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
+            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
+
+        # self.fig.show()
+ 
+        # we are hovering on nothing, so we are going back to the position of
+        # the profile sounding
+        if pos is None:
+            if self.hover_active == True:
+                #print('h1*')
+                
+                #self.loading()
+                # to do: reset stations iterators
+
+                # get station and record index from the current profile
+                for key in ['STNID', 'current_station']:
+                    self.frames['stats'][key] = self.frames['profiles'][key]
+
+                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
+                self.frames['stats']['current_station'] = \
+                        self.frames['profiles']['current_station']
+                #print('h3a*')
+                self.frames['stats']['records_current_station_mod'] = \
+                        self.frames['profiles']['records_current_station_mod']
+                #print('h3b*')
+
+                # the next lines recreate the records iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+
+                # reset stations iterator...
+                #self.frames['stats']['records_iterator'].close()
+                del(self.frames['stats']['records_iterator'])
+                self.frames['stats']['records_iterator'] = \
+                    self.frames['stats']['records_current_station_mod'].iterrows()
+                #print('h4*')
+
+                # ... and go to the record of the profile window (last one that
+                # was picked by the user)
+                found = False
+                EOF = False
+                while (not found) and (not EOF):
+                    try:
+                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                        #print("hello*")
+                        #print(self.frames['profiles']['current_record_index'])
+                        #print(self.frames['profiles']['STNID'])
+                        #print(STNID,index)
+                        if (index == self.frames['profiles']['current_record_index']) and \
+                            (chunk == self.frames['profiles']['current_record_chunk']) and \
+                            (STNID == self.frames['profiles']['STNID']):
+                            #print('found!')
+                            found = True
+                    except StopIteration:
+                        EOF = True
+                if found:
+                    #print('h5*')
+                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_chunk'] = chunk
+                    self.frames['stats']['current_record_index'] = index
+
+                #print('h6*')
+
+
+
+                # # fetch records of current station...
+                # self.frames['stats']['records_current_station_mod'] =\
+                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                # ... and their indices
+                self.frames['stats']['records_current_station_index'] = \
+                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                         == \
+                         self.frames['stats']['current_station'].name)
+
+
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['records_current_station'+tab_suffix] = \
+                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+                
+
+                # cash the records of the current stations
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['current_record'+tab_suffix] =  \
+                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                              (self.frames['stats']['STNID'] , \
+                               self.frames['stats']['current_record_chunk'] , \
+                               self.frames['stats']['current_record_index'])]
+
+
+                # the next lines recreate the stations iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+                #print('h7*')
+
+                # reset the stations iterators
+                for framekey in ['stats','worldmap']:
+                    ##print(framekey)
+                    if 'stations_iterator' in self.frames[framekey]:
+                        #self.frames[framekey]['stations_iterator'].close()
+                        del(self.frames[framekey]['stations_iterator'])
+
+                self.frames['worldmap']['current_station'] = \
+                        self.frames['profiles']['current_station']
+
+                #recreate the stations iterator for the worldmap...
+                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+
+                # ... and go the position of the profile
+                #print('h8*')
+                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                EOF = False
+                found = False
+                while (not found and not EOF):
+                    if STNID == self.frames['profiles']['STNID'] :
+                        found = True 
+                    if not found:
+                        try:
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                        except (StopIteration):
+                            EOF = True
+                if found:
+                    self.frames['worldmap']['current_station'] = station
+                    self.frames['worldmap']['STNID'] = STNID
+                #print('h9*')
+                self.frames['stats']['stations_iterator'] = \
+                    self.frames['worldmap']['stations_iterator'] 
+
+                # the stats window now inherits the current station from the
+                # worldmap
+                for key in ['STNID','current_station','stations_iterator']: 
+                    self.frames['stats'][key] = self.frames['worldmap'][key] 
+                #print('h10*')
+
+                # # we now only need inherit station position and go to first record
+                # for key in self.frames['worldmap'].keys():
+                #     self.frames['stats'][key] = self.frames['worldmap'][key]
+
+                # self.frames['stats']['records_current_station'] =\
+                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
+
+                # #print(self.frames['stats']['records_current_station'])
+                # self.frames['stats']['records_iterator'] = \
+                #                 self.frames['stats']['records_current_station'].iterrows()
+                # (self.frames['stats']['STNID'] , \
+                # self.frames['stats']['current_record_index']) , \
+                # self.frames['stats']['current_record_mod'] = \
+                #                 self.frames['stats']['records_iterator'].__next__()
+                
+
+
+
+
+
+
+                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
+                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
+                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
+                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+                self.hover_active = False
+                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
+    # def loading(self):
+    #     self.tbox['loading'].set_text('Loading...')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+    #     sleep(0.1)
+    # def ready(self):
+    #     self.tbox['loading'].set_text('Ready')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+
+
+
diff --git a/build/lib/lib/model.py b/build/lib/lib/model.py
new file mode 100644
index 0000000..8760411
--- /dev/null
+++ b/build/lib/lib/model.py
@@ -0,0 +1,2214 @@
+# 
+# CLASS
+# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
+# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
+# Copyright (c) 2011-2015 Chiel van Heerwaarden
+# Copyright (c) 2011-2015 Bart van Stratum
+# Copyright (c) 2011-2015 Kees van den Dries
+# 
+# This file is part of CLASS
+# 
+# CLASS is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published bygamma
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# CLASS is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with CLASS.  If not, see .
+#
+
+import copy as cp
+import numpy as np
+import sys
+import warnings
+import pandas as pd
+from ribtol_hw import zeta_hs2 , funcsche
+import logging
+#from SkewT.thermodynamics import Density
+#import ribtol
+
+grav = 9.81
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+
+def qsat(T,p):
+    return 0.622 * esat(T) / p
+
+
+def ribtol(Rib, zsl, z0m, z0h): 
+    Rib = np.float64(Rib)
+    zsl = np.float64(zsl)
+    z0m = np.float64(z0m)
+    z0h = np.float64(z0h)
+    #print(Rib,zsl,z0m,z0h)
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    #print(Rib,zsl,z0m,z0h)
+    while (abs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+        #print(L,fx/fxdif)
+        if(abs(L) > 1e12):
+            break
+
+    return L
+  
+def psim(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psim = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+  
+def psih(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * np.log( (1. + x*x) / 2.)
+        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+ 
+class model:
+    def __init__(self, model_input = None,debug_level=None):
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        self.logger = logging.getLogger('model')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        """ initialize the different components of the model """ 
+
+        if model_input is not None:
+            # class4gl style input
+            if 'pars' in model_input.__dict__.keys():
+
+                # we make a reference to the full input first, so we can dump it
+                # afterwards
+                self.input_c4gl = model_input
+
+                # we copy the regular parameters first. We keep the classical input
+                # format as self.input so that we don't have to change the entire
+                # model code.
+                self.input = cp.deepcopy(model_input.pars)
+
+                # we copy other sections we are interested in, such as profile
+                # data, and store it also under input
+
+                # I know we mess up a bit the structure of the class4gl_input, but
+                # we will make it clean again at the time of dumping data
+
+                # So here, we copy the profile data into self.input
+                # 1. Air circulation data 
+                if 'sw_ac' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ac']:
+                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
+                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
+
+                    # correct pressure of levels according to surface pressure
+                    # error (so that interpolation is done in a consistent way)
+
+                    p_e = self.input.Ps - self.input.sp
+                    for irow in self.input.air_ac.index[::-1]:
+                       self.input.air_ac.p.iloc[irow] =\
+                        self.input.air_ac.p.iloc[irow] + p_e
+                       p_e = p_e -\
+                       (self.input.air_ac.p.iloc[irow]+p_e)/\
+                        self.input.air_ac.p.iloc[irow] *\
+                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+
+
+
+                # 2. Air circulation data 
+                if 'sw_ap' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ap']:
+                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
+
+            # standard class input
+            else:
+                self.input = cp.deepcopy(model_input)
+
+    def load_yaml_dict(self,yaml_dict):
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                for keydata,value in data.items():
+                    self.__dict__[keydata] = value
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            #elif key == 'sources':
+            #    self.__dict__[key] = data
+            elif key == 'out':
+                # lets convert it to a list of dictionaries
+                dictouttemp = pd.DataFrame(data).to_dict('list')
+            else: 
+                 warnings.warn("Key '"+key+"' is be implemented.")
+            #     self.__dict__[key] = data
+
+
+        self.tsteps = len(dictouttemp['h'])
+        self.out = model_output(self.tsteps)
+        for keydictouttemp in dictouttemp.keys():
+            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+
+
+  
+    def run(self):
+        # initialize model variables
+        self.init()
+  
+        # time integrate model 
+        #for self.t in range(self.tsteps):
+        while self.t < self.tsteps:
+          
+            # time integrate components
+            self.timestep()
+  
+        # delete unnecessary variables from memory
+        self.exitmodel()
+    
+    def init(self):
+        # assign variables from input data
+        # initialize constants
+        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
+        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        self.rho        = 1.2                   # density of air [kg m-3]
+        self.k          = 0.4                   # Von Karman constant [-]
+        self.g          = 9.81                  # gravity acceleration [m s-2]
+        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+        self.bolz       = 5.67e-8               # Bolzman constant [-]
+        self.rhow       = 1000.                 # density of water [kg m-3]
+        self.S0         = 1368.                 # solar constant [W m-2]
+
+        # A-Gs constants and settings
+        # Plant type:       -C3-     -C4-
+        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
+        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
+        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
+        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
+        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
+        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
+        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
+        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
+        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
+        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
+        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
+
+        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
+        self.mair       =  28.9;                # molecular weight air [g mol -1]
+        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
+
+        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
+        self.wmax       =  0.55;                # upper reference value soil water [-]
+        self.wmin       =  0.005;               # lower reference value soil water [-]
+        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
+        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
+
+        # Read switches
+        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
+        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
+        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
+        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
+        self.sw_sl      = self.input.sw_sl      # surface layer switch
+        self.sw_rad     = self.input.sw_rad     # radiation switch
+        self.sw_ls      = self.input.sw_ls      # land surface switch
+        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
+        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
+
+        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
+        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
+        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+  
+        # initialize mixed-layer
+        self.h          = self.input.h          # initial ABL height [m]
+        self.Ps         = self.input.Ps         # surface pressure [Pa]
+        self.sp         = self.input.sp         # This is also surface pressure
+                                                #but derived from the global data [Pa]
+        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
+        self.ws         = None                  # large-scale vertical velocity [m s-1]
+        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
+        self.we         = -1.                   # entrainment velocity [m s-1]
+       
+         # Temperature 
+        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
+        
+        
+        self.substep    = False
+        self.substeps   = 0
+
+
+
+        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
+        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
+        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
+        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
+        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
+ 
+        self.wstar      = 0.                    # convective velocity scale [m s-1]
+ 
+        # 2m diagnostic variables 
+        self.T2m        = None                  # 2m temperature [K]
+        self.q2m        = None                  # 2m specific humidity [kg kg-1]
+        self.e2m        = None                  # 2m vapor pressure [Pa]
+        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
+        self.u2m        = None                  # 2m u-wind [m s-1]
+        self.v2m        = None                  # 2m v-wind [m s-1]
+ 
+        # Surface variables 
+        self.thetasurf  = self.input.theta      # surface potential temperature [K]
+        self.thetavsurf = None                  # surface virtual potential temperature [K]
+        self.qsurf      = None                  # surface specific humidity [g kg-1]
+
+        # Mixed-layer top variables
+        self.P_h        = None                  # Mixed-layer top pressure [pa]
+        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
+        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
+        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
+        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
+        self.dz_h       = None                  # Transition layer thickness [-]
+        self.lcl        = None                  # Lifting condensation level [m]
+
+        # Virtual temperatures and fluxes
+        self.thetav     = None                  # initial mixed-layer potential temperature [K]
+        self.dthetav    = None                  # initial virtual temperature jump at h [K]
+        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
+        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
+       
+        
+        
+        
+        
+        
+        # Moisture 
+        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
+
+        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
+        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
+        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
+  
+        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
+        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
+        self.e          = None                  # mixed-layer vapor pressure [Pa]
+        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
+        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
+      
+        
+        
+        # CO2
+        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
+        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
+        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
+        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
+        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
+        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
+        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
+        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
+        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
+        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
+       
+        # Wind 
+        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
+        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
+        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = self.input.advu       # advection of u-wind [m s-2]
+        
+        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
+        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = self.input.advv       # advection of v-wind [m s-2]
+         
+  # BEGIN -- HW 20170606
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        if self.sw_ac:
+        # this is the data frame with the grided profile on the L60 grid
+        # (subsidence, and advection) 
+            self.air_ac      = self.input.air_ac  # full level air circulation
+                                                  # forcing
+            # self.air_ach     = self.input.air_ach # half level air circulation
+            #                                       # forcing
+            
+
+        if self.sw_ap:
+        # this is the data frame with the fitted profile (including HAGL,
+        # THTA,WSPD, SNDU,WNDV PRES ...)
+            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
+
+            # just for legacy reasons...
+            if 'z' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
+            if 'p' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
+
+            indexh = np.where(self.air_ap.z.values == self.h)
+            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
+                raise ValueError("Error input profile consistency: mixed- \
+                                 layer height needs to be equal to the second \
+                                 and third \
+                                 level of the vertical profile input!")
+            # initialize q from its profile when available
+            p_old = self.Ps
+            p_new = self.air_ap.p[indexh[0][0]]
+            
+            if ((p_old is not None) & (p_old != p_new)):
+                print("Warning: Ps input was provided ("+str(p_old)+\
+                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+                    +str(p_new)+"Pa).")
+                                    
+            self.Ps = p_new
+            # these variables/namings are more convenient to work with in the code
+            # we will update the original variables afterwards
+            #self.air_ap['q'] = self.air_ap.QABS/1000.
+
+            self.air_ap = \
+                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
+            # we require the temperature fields, since we need to consider
+            # advection
+            # if self.sw_ac:
+            #     #self.air_ap['theta'] = self.air_ap['t'] *
+
+            #     # we consider self.sp in case of air-circulation input (for
+            #     # consistence)
+            #     self.air_ap['t'] = \
+            #                 self.air_ap.theta *  \
+            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
+            # else:
+            # we consider self.Ps in case of balloon input only 
+            self.air_ap = self.air_ap.assign(t = lambda x: \
+                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
+
+            #self.air_ap['theta'] = self.air_ap.THTA
+            if 'u' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
+            if 'v' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
+
+            for var in ['theta','q','u','v']:
+
+                
+                if self.air_ap[var][1] != self.air_ap[var][0]:
+                    raise ValueError("Error input profile consistency: two \
+                                     lowest profile levels for "+var+" should \
+                                     be equal.")
+                
+                # initialize the value from its profile when available
+                value_old = self.__dict__[var]
+                value_new = self.air_ap[var][indexh[0][0]]
+                
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided \
+                                     ("+str(value_old)+ "kg kg-1), \
+                                     but it is now overwritten by the first \
+                                     level (index 0) of air_ap]var\ which is \
+                                     different (" +str(value_new)+"K).")
+                                        
+                self.__dict__[var] = value_new
+
+                # make a profile of the stratification 
+                # please note that the stratification between z_pro[i] and
+                # z_pro[i+1] is given by air_ap.GTHT[i]
+
+                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
+                # np.gradient(self.z_pro)
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
+
+
+                self.__dict__['gamma'+var] = \
+                    self.air_ap['gamma'+var][np.where(self.h >= \
+                                                     self.air_ap.z)[0][-1]]
+
+
+
+        # the variable p_pro is just for diagnosis of lifted index
+            
+            
+
+            # input Ph is wrong, so we correct it according to hydrostatic equation
+            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+
+            #if self.sw_ac:
+                # note that we use sp as surface pressure, which is determined
+                # from era-interim instead of the observations. This is to
+                # avoid possible failure of the interpolation routine
+                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
+                #                          + \
+                #                          list(self.air_ap.p[3:]))
+
+            # else:
+                # in the other case, it is updated at the time of calculting
+                # the statistics 
+
+# END -- HW 20170606      
+        #print(self.air_ap)
+
+        if self.sw_ac and not self.sw_ap:
+            raise ValueError("air circulation switch only possible when air \
+                             profiles are given")
+        
+        if self.sw_ac:
+
+            # # # we comment this out, because subsidence is calculated
+            # according to advection
+            # #interpolate subsidence towards the air_ap height coordinate
+            # self.air_ap['w'] = np.interp(self.air_ap.p,\
+            #                               self.air_ac.p,\
+            #                               self.air_ac.w) 
+            # #subsidence at the mixed-layer top
+            # self.w = self.air_ap.w[1]
+        
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+                # in case we didn't find any points, we just take the lowest one.
+                # actually, this can happen if ERA-INTERIM pressure levels are
+                # inconsistent with 
+                if in_ml.sum() == 0:
+                    warnings.warn(" no circulation points in the mixed layer \
+                                  found. We just take the bottom one.")
+                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+
+                for var in ['t','q','u','v']:
+    
+                   # calculation of the advection variables for the mixed layer
+                   # we weight by the hydrostatic thickness of each layer and
+                   # divide by the total thickness
+                   self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+
+                   # calculation of the advection variables for the profile above
+                   # (lowest 3 values are not used by class)
+                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
+                   self.air_ap['adv'+var] = \
+                           np.interp(self.air_ap.p,\
+                                     self.air_ac.p,\
+                                     self.air_ac['adv'+var+'_x']) \
+                           + \
+                           np.interp(self.air_ap.p, \
+                                       self.air_ac.p, \
+                                       self.air_ac['adv'+var+'_y'])
+
+                # as an approximation, we consider that advection of theta in the
+                # mixed layer is equal to advection of t. This is a sufficient
+                # approximation since theta and t are very similar at the surface
+                # pressure.
+                self.__dict__['advtheta'] = self.__dict__['advt']
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # self.wrho = np.interp(self.P_h,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) 
+            # self.ws   = self.air_ap.w.iloc[1]
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
+                self.air_ap = self.air_ap.assign(R = 0.)
+                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                     self.Rv*self.air_ap.q)
+                self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+                
+                self.air_ap = self.air_ap.assign(w = 0.)
+                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+                #print('hello w ini')
+
+                # Note: in case of sw_ac is False, we update it from prescribed
+                # divergence
+                self.ws   = self.air_ap.w[1]
+
+                # self.ws   = self.wrho/self.rho
+                # self.ws   = self.wrho/(self.P_h/ \
+                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
+                #                         self.theta) # this should be T!!!
+
+                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+                #                         + \
+                #                         self.air_ac['divU_y'][in_ml])* \
+                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                #             self.air_ac['delpdgrav'][in_ml].sum() \
+        
+
+        # Tendencies 
+        self.htend      = None                  # tendency of CBL [m s-1]
+        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
+        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
+        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
+        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
+        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
+        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
+        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
+        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
+        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
+        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
+        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
+  
+        # initialize surface layer
+        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
+        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
+        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
+        self.z0m        = self.input.z0m        # roughness length for momentum [m]
+        self.z0h        = self.input.z0h        # roughness length for scalars [m]
+        self.Cm         = 1e12                  # drag coefficient for momentum [-]
+        self.Cs         = 1e12                  # drag coefficient for scalars [-]
+        self.L          = None                  # Obukhov length [m]
+        self.Rib        = None                  # bulk Richardson number [-]
+        self.ra         = None                  # aerodynamic resistance [s m-1]
+  
+        # initialize radiation
+        self.lat        = self.input.lat        # latitude [deg]
+        #self.fc         = self.input.fc         # coriolis parameter [s-1]
+        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
+        self.lon        = self.input.lon        # longitude [deg]
+        self.doy        = self.input.doy        # day of the year [-]
+        self.tstart     = self.input.tstart     # time of the day [-]
+        self.cc         = self.input.cc         # cloud cover fraction [-]
+        self.Swin       = None                  # incoming short wave radiation [W m-2]
+        self.Swout      = None                  # outgoing short wave radiation [W m-2]
+        self.Lwin       = None                  # incoming long wave radiation [W m-2]
+        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
+        self.Q          = self.input.Q          # net radiation [W m-2]
+        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
+  
+        # initialize land surface
+        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
+        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
+        self.T2         = self.input.T2         # temperature deeper soil layer [K]
+                           
+        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
+        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
+        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
+        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
+                           
+        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
+        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
+        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
+                           
+        self.C1sat      = self.input.C1sat      
+        self.C2ref      = self.input.C2ref      
+
+        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
+        
+        self.LAI        = self.input.LAI        # leaf area index [-]
+        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
+        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = self.input.alpha      # surface albedo [-]
+  
+        self.rs         = 1.e6                  # resistance transpiration [s m-1]
+        self.rssoil     = 1.e6                  # resistance soil [s m-1]
+                           
+        self.Ts         = self.input.Ts         # surface temperature [K]
+                           
+        self.cveg       = self.input.cveg       # vegetation fraction [-]
+        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
+        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
+        self.cliq       = None                  # wet fraction [-]
+                          
+        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
+  
+        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
+        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
+        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
+  
+        self.H          = None                  # sensible heat flux [W m-2]
+        self.LE         = None                  # evapotranspiration [W m-2]
+        self.LEliq      = None                  # open water evaporation [W m-2]
+        self.LEveg      = None                  # transpiration [W m-2]
+        self.LEsoil     = None                  # soil evaporation [W m-2]
+        self.LEpot      = None                  # potential evaporation [W m-2]
+        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
+        self.G          = None                  # ground heat flux [W m-2]
+
+        # initialize A-Gs surface scheme
+        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
+
+        # initialize cumulus parameterization
+        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
+        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
+        self.ac         = 0.                    # Cloud core fraction [-]
+        self.M          = 0.                    # Cloud core mass flux [m s-1] 
+        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
+  
+        # initialize time variables
+        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
+        self.dt     = self.input.dt
+        self.dtcur      = self.dt
+        self.firsttime = True
+        self.t      = 0
+ 
+        # Some sanity checks for valid input
+        if (self.c_beta is None): 
+            self.c_beta = 0                     # Zero curvature; linear response
+        assert(self.c_beta >= 0 or self.c_beta <= 1)
+
+        # initialize output
+        self.out = model_output(self.tsteps)
+ 
+        self.statistics()
+  
+        # calculate initial diagnostic variables
+        if(self.sw_rad):
+            self.run_radiation()
+ 
+        if(self.sw_sl):
+            for i in range(10): 
+                self.run_surface_layer()
+  
+        if(self.sw_ls):
+            self.run_land_surface()
+
+        if(self.sw_cu):
+            self.run_mixed_layer()
+            self.run_cumulus()
+        
+        if(self.sw_ml):
+            self.run_mixed_layer()
+
+    def timestep(self):
+
+        self.dtmax = +np.inf
+        self.logger.debug('before stats') 
+        self.statistics()
+
+        # run radiation model
+        self.logger.debug('before rad') 
+        if(self.sw_rad):
+            self.run_radiation()
+  
+        # run surface layer model
+        if(self.sw_sl):
+            self.logger.debug('before surface layer') 
+            self.run_surface_layer()
+        
+        # run land surface model
+        if(self.sw_ls):
+            self.logger.debug('before land surface') 
+            self.run_land_surface()
+ 
+        # run cumulus parameterization
+        if(self.sw_cu):
+            self.logger.debug('before cumulus') 
+            self.run_cumulus()
+   
+        self.logger.debug('before mixed layer') 
+        # run mixed-layer model
+        if(self.sw_ml):
+            self.run_mixed_layer()
+        self.logger.debug('after mixed layer') 
+ 
+        #get first profile data point above mixed layer
+        if self.sw_ap:
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                # here we correct for the fact that the upper profile also
+                # shifts in the vertical.
+
+                diffhtend = self.htend - self.air_ap.w[zidx_first]
+                if diffhtend > 0:
+                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            else:
+                if self.htend > 0:
+                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            #print(self.h,zidx_first,self.ws,self.air_ap.z)
+
+        
+        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
+        self.logger.debug('before store') 
+        self.substep =  (self.dtcur > self.dtmax)
+        if self.substep:
+            dtnext = self.dtcur - self.dtmax
+            self.dtcur = self.dtmax
+
+        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
+
+        # HW: this will be done multiple times in case of a substep is needed
+        # store output before time integration
+        if self.firsttime:
+            self.store()
+  
+        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
+        # time integrate land surface model
+        if(self.sw_ls):
+            self.integrate_land_surface()
+        self.logger.debug('before integrate mixed layer') 
+        # time integrate mixed-layer model
+        if(self.sw_ml):
+            self.integrate_mixed_layer() 
+        self.logger.debug('after integrate mixed layer') 
+        if self.substep:
+            self.dtcur = dtnext
+            self.firsttime = False
+            self.substeps += 1
+        else:
+            self.dtcur = self.dt
+            self.t += 1 
+            self.firsttime = True
+            self.substeps = 0
+        self.logger.debug('going to next step')
+        
+        
+  
+    def statistics(self):
+        # Calculate virtual temperatures 
+        self.thetav   = self.theta  + 0.61 * self.theta * self.q
+        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
+        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
+        # Mixed-layer top properties
+        self.P_h    = self.Ps - self.rho * self.g * self.h
+        # else:
+            # in the other case, it is updated at the time that the profile is
+            # updated (and at the initialization
+
+        self.T_h    = self.theta - self.g/self.cp * self.h
+
+        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
+        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
+
+        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
+
+        # Find lifting condensation level iteratively
+        if(self.t == 0):
+            self.lcl = self.h
+            RHlcl = 0.5
+        else:
+            RHlcl = 0.9998 
+
+        itmax = 30
+        it = 0
+        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
+            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
+            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
+        else:
+            self.q2_h   = 0.
+            self.CO22_h = 0.
+
+        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
+        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
+        self.M      = self.ac * self.wstar
+        self.wqM    = self.M * self.q2_h**0.5
+
+        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
+        if(self.dCO2 < 0):
+            self.wCO2M  = self.M * self.CO22_h**0.5
+        else:
+            self.wCO2M  = 0.
+
+    def run_mixed_layer(self):
+        if(not self.sw_sl):
+            # decompose ustar along the wind components
+            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
+            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
+
+
+
+        # calculate large-scale vertical velocity (subsidence)
+        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
+            self.ws = -self.divU * self.h
+        # else:
+        #     in case the air circulation switch is turned on, subsidence is
+        #     calculated from the circulate profile at the initialization and
+        #     in the integrate_mixed_layer routine
+              
+        # calculate compensation to fix the free troposphere in case of subsidence 
+        if(self.sw_fixft):
+            w_th_ft  = self.gammatheta * self.ws
+            w_q_ft   = self.gammaq     * self.ws
+            w_CO2_ft = self.gammaCO2   * self.ws 
+        else:
+            w_th_ft  = 0.
+            w_q_ft   = 0.
+            w_CO2_ft = 0. 
+      
+        # calculate mixed-layer growth due to cloud top radiative divergence
+        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
+       
+        # calculate convective velocity scale w* 
+        if(self.wthetav > 0.):
+            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
+        else:
+            self.wstar  = 1e-6;
+      
+        # Virtual heat entrainment flux 
+        self.wthetave    = -self.beta * self.wthetav 
+        
+        # compute mixed-layer tendencies
+        if(self.sw_shearwe):
+            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
+        else:
+            self.we    = -self.wthetave / self.dthetav
+        # Don't allow boundary layer shrinking if wtheta < 0 
+        if(self.we < 0):
+            self.we = 0.
+
+        # Calculate entrainment fluxes
+        self.wthetae     = -self.we * self.dtheta
+        self.wqe         = -self.we * self.dq
+        self.wCO2e       = -self.we * self.dCO2
+        
+        htend_pre       = self.we + self.ws + self.wf - self.M
+        
+        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+        
+ 
+        #print('thetatend_pre',thetatend_pre)
+        
+        #preliminary boundary-layer top chenage
+        #htend_pre = self.we + self.ws + self.wf - self.M
+        #preliminary change in temperature jump
+        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
+                          thetatend_pre + w_th_ft
+        
+        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
+        l_entrainment = True
+
+        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
+            l_entrainment = False
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! temperature jump is at the lower limit \
+                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+        elif dtheta_pre < 0.1:
+            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
+            l_entrainment = True
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          " Warning! Potential temperature jump at mixed- \
+                          layer height would become too low limiting timestep \
+                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+            self.dtmax = min(self.dtmax,dtmax_new)
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "next subtimestep, entrainment will be disabled")
+            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
+
+
+
+        # when entrainment is disabled, we just use the simplified formulation
+        # as in Wouters et al., 2013 (section 2.2.1)
+
+        self.dthetatend = l_entrainment*dthetatend_pre + \
+                        (1.-l_entrainment)*0.
+        self.thetatend = l_entrainment*thetatend_pre + \
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = l_entrainment*htend_pre + \
+                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
+        #stop
+
+
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+
+
+        # self.qtend = l_entrainment*qtend_pre + \
+        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
+        # self.CO2tend = l_entrainment*CO2tend_pre + \
+        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+
+
+        #     # part of the timestep for which the temperature mixed-layer jump
+        #     # was changing, and for which entrainment took place. For the other
+        #     # part, we don't assume entrainment anymore, and we use the
+        #     # simplified formulation  of Wouters et al., 2013
+
+        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
+        #   
+        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
+        #                      self.dthetatend + w_th_ft) + \
+        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
+        #     self.htend = fac*self.htend + \
+        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
+        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
+        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+
+        # else:
+        #     #self.htend = htend_pre
+        #     self.dthetatend = dthetatend_pre
+        #     self.thetatend = thetatend_pre
+        
+        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+     
+        # assume u + du = ug, so ug - u = du
+        if(self.sw_wind):
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+  
+            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
+        
+        # tendency of the transition layer thickness
+        if(self.ac > 0 or self.lcl - self.h < 300):
+            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
+        else:
+            self.dztend = 0.
+
+   
+    def integrate_mixed_layer(self):
+        # set values previous time step
+        h0      = self.h
+        
+        theta0  = self.theta
+        dtheta0 = self.dtheta
+        q0      = self.q
+        dq0     = self.dq
+        CO20    = self.CO2
+        dCO20   = self.dCO2
+        
+        u0      = self.u
+        du0     = self.du
+        v0      = self.v
+        dv0     = self.dv
+
+        dz0     = self.dz_h
+  
+        # integrate mixed-layer equations
+        
+            
+
+# END -- HW 20170606        
+        self.h        = h0      + self.dtcur * self.htend
+        # print(self.h,self.htend)
+        # stop
+        self.theta    = theta0  + self.dtcur * self.thetatend
+        #print(dtheta0,self.dtcur,self.dthetatend)
+        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+        self.q        = q0      + self.dtcur * self.qtend
+        self.dq       = dq0     + self.dtcur * self.dqtend
+        self.CO2      = CO20    + self.dtcur * self.CO2tend
+        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
+        self.dz_h     = dz0     + self.dtcur * self.dztend
+            
+        # Limit dz to minimal value
+        dz0 = 50
+        if(self.dz_h < dz0):
+            self.dz_h = dz0 
+  
+        if(self.sw_wind):
+            self.u        = u0      + self.dtcur * self.utend
+            self.du       = du0     + self.dtcur * self.dutend
+            self.v        = v0      + self.dtcur * self.vtend
+            self.dv       = dv0     + self.dtcur * self.dvtend
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+            for var in ['t','q','u','v']:
+                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
+
+            # take into account advection for the whole profile
+                
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
+
+            var = 'z'
+            #print(self.air_ap[var])
+                #     print(self.air_ap['adv'+var])
+
+
+
+
+            #moving the profile vertically according to the vertical wind
+                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
+
+
+            # air_apvarold = pd.Series(np.array(self.air_ap.z))
+            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
+            # stop
+
+
+                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
+                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
+
+            #As t is updated, we also need to recalculate theta (and R)
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+
+            # air_aptheta_old = pd.Series(self.air_ap['theta'])
+            self.air_ap['theta'] = \
+                        self.air_ap.t * \
+                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
+                                         self.dtcur * self.air_ap.w[zidx_first:]
+
+#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
+#            print(self.t, self.dtcur,self.dt,self.htend)
+
+            # # the pressure levels of the profiles are recalculated according to
+            # # there new height (after subsidence)
+            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
+            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
+            #         * self.dtcur *  self.air_ap.w[zidx_first:]
+
+            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
+                    self.dtcur * self.air_ap.wp[zidx_first:]
+
+            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
+        # note that theta and q itself are updatet by class itself
+
+    
+        if self.sw_ap:
+            # Just for model consistency preservation purposes, we set the
+            # theta variables of the mixed-layer to nan values, since the
+            # mixed-layer values should overwritte by the mixed-layer
+            # calculations of class.
+            self.air_ap['theta'][0:3] = np.nan 
+            self.air_ap['p'][0:3] = np.nan 
+            self.air_ap['q'][0:3] = np.nan 
+            self.air_ap['u'][0:3] = np.nan 
+            self.air_ap['v'][0:3] = np.nan 
+            self.air_ap['t'][0:3] = np.nan 
+            self.air_ap['z'][0:3] = np.nan 
+
+            # Update the vertical profiles: 
+            #   - new mixed layer properties( h, theta, q ...)
+            #   - any data points below the new ixed-layer height are removed
+
+            # Three data points at the bottom that describe the mixed-layer
+            # properties
+            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
+                                           # columns as air_ap
+            # air_ap_head['z'].iloc[0] = 2.
+            # air_ap_head['z'].iloc[1] = self.__dict__['h']
+            # air_ap_head['z'].iloc[2] = self.__dict__['h']
+            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
+                        [2.,self.__dict__['h'],self.__dict__['h']]
+            for var in ['theta','q','u','v']:
+
+                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
+                        [self.__dict__[var], \
+                         self.__dict__[var], \
+                         self.__dict__[var] + self.__dict__['d'+var]]
+                
+            #print(self.air_ap)
+
+            # This is the remaining profile considering the remaining
+            # datapoints above the mixed layer height
+            air_ap_tail = self.air_ap.iloc[3:]
+            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
+
+            # print('h',self.h)
+            # # only select samples monotonically increasing with height
+            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            # air_ap_tail = pd.DataFrame()
+            # theta_low = self.theta
+            # z_low =     self.h
+            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            # for ibottom in range(1,len(air_ap_tail_orig)):
+            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
+            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+            # make theta increase strong enough to avoid numerical
+            # instability
+            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            air_ap_tail = pd.DataFrame()
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            theta_low = self.theta
+            z_low =     self.h
+            ibottom = 0
+            itop = 0
+            # print(air_ap_tail_orig)
+            # stop
+
+            # HW: this is the lower limit that we use for gammatheta, which is
+            # there to avoid model crashes. Besides on this limit, the upper
+            # air profile is modified in a way that is still conserves total
+            # quantities of moisture and temperature. The limit is set by trial
+            # and error. The numerics behind the crash should be investigated
+            # so that a cleaner solution can be provided.
+            gammatheta_lower_limit = 0.002
+            while ((itop in range(0,1)) or (itop != ibottom)):
+                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+                if (
+                    #(z_mean > (z_low+0.2)) and \
+                    #(theta_mean > (theta_low+0.02) ) and \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                  (itop >= (len(air_ap_tail_orig)-1)) \
+                   :
+
+                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                    ibottom = itop+1
+                    theta_low = air_ap_tail.theta.iloc[-1]
+                    z_low =     air_ap_tail.z.iloc[-1]
+    
+
+                itop +=1
+                # elif  (itop > len(air_ap_tail_orig)-10):
+                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+                #print(itop,ibottom)
+
+            if itop > 1:
+                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! Temperature profile was too steep. \
+                                  Modifying profile: "+ \
+                                  str(itop - 1)+ " measurements were dropped \
+                                  and replaced with its average \
+                                  Modifying profile. \
+                                  mean with next profile point(s).") 
+
+
+            self.air_ap = pd.concat((air_ap_head,\
+                                     air_ap_tail,\
+                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
+                                                                      axis=1)
+
+            if  self.sw_ac:
+                qvalues = \
+                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
+
+                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
+                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
+                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+                self.P_h    = self.Ps - self.rho * self.g * self.h
+                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
+                        [self.Ps,  self.P_h, self.P_h-0.1]
+
+                self.air_ap.t = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
+
+
+        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
+
+
+
+
+        # else:
+            # in the other case, it is updated at the time the statistics are
+            # calculated 
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if in_ml.sum() == 0:
+                warnings.warn(" no circulation points in the mixed layer \
+                              found. We just take the bottom one.")
+                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+            for var in ['t','q','u','v']:
+
+                # calculation of the advection variables for the mixed-layer
+                # these will be used for the next timestep
+                # Warning: w is excluded for now.
+
+                self.__dict__['adv'+var] = \
+                        ((self.air_ac['adv'+var+'_x'][in_ml] \
+                         + \
+                         self.air_ac['adv'+var+'_y'][in_ml])* \
+                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                        self.air_ac['delpdgrav'][in_ml].sum()
+
+                # calculation of the advection variables for the profile above
+                # the mixed layer (also for the next timestep)
+                self.air_ap['adv'+var] = \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p,\
+                                              self.air_ac['adv'+var+'_x']) \
+                                    + \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p, \
+                                              self.air_ac['adv'+var+'_y'])
+                # if var == 't':
+                #     print(self.air_ap['adv'+var])
+                #     stop
+
+            # as an approximation, we consider that advection of theta in the
+            # mixed layer is equal to advection of t. This is a sufficient
+            # approximation since theta and t are very similar at the surface
+            # pressure.
+
+            self.__dict__['advtheta'] = self.__dict__['advt']
+
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            # update the vertical wind profile
+            self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                          self.air_ac.p, \
+                                          self.air_ac['wp'])
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+            
+            air_apwold = self.air_ap['w']
+            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+            #print('hello w upd')
+
+            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # # self.wrho = np.interp(self.P_h,\
+            # #                      self.air_ach.p,\
+            # #                      self.air_ach['wrho']) \
+
+
+
+            # Also update the vertical wind at the mixed-layer height
+            # (subsidence)
+            self.ws   = self.air_ap.w[1]
+        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
+
+            ## Finally, we update he 
+            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+            #                        + \
+            #                        self.air_ac['divU_y'][in_ml])* \
+            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+            #            self.air_ac['delpdgrav'][in_ml].sum() 
+            
+
+        if self.sw_ap:
+            for var in ['theta','q','u','v']:
+
+                # update of the slope (gamma) for the different variables, for
+                # the next timestep!
+
+                # there is an warning message that tells about dividing through
+                # zero, which we ignore
+
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                    # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap['gamma'+var] = gammavar
+
+                # Based on the above, update the gamma value at the mixed-layer
+                # top
+                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
+                                                                     self.air_ap.z)[0][-1]]
+
+            
+    def run_radiation(self):
+        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
+        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
+        sinlea = max(sinlea, 0.0001)
+        
+        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
+  
+        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
+  
+        self.Swin  = self.S0 * Tr * sinlea
+        self.Swout = self.alpha * self.S0 * Tr * sinlea
+        
+        
+        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
+        self.Lwout = self.bolz * self.Ts ** 4.
+          
+        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
+        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
+  
+    def run_surface_layer(self):
+        # HW: I had to raise the minimum wind speed to make the simulation with
+        # the non-iterative solution stable (this solution was a wild guess, so I don't
+        # know the exact problem of the instability in case of very low wind
+        # speeds yet)
+        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        # version of 20180730 where there are still some runs crashing. Maybe
+        # an upper limit should be set on the monin-obukhov length instead of
+        # a lower limmit on the wind speed?
+        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        
+        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
+        qsatsurf       = qsat(self.thetasurf, self.Ps)
+        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
+        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
+
+        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
+  
+        zsl       = 0.1 * self.h
+        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
+        
+
+
+        if self.sw_lit:
+            self.Rib  = min(self.Rib, 0.2)
+            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
+            self.zeta  = zsl/self.L
+            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
+            
+        
+            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
+            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
+            
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+        
+     
+            # diagnostic meteorological variables
+            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
+            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
+            
+            # diagnostic meteorological variables
+        else:
+            
+            ## circumventing any iteration with Wouters et al., 2012
+            self.zslz0m = np.max((zsl/self.z0m,10.))
+            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
+            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
+            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
+            self.L = zsl/self.zeta
+            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
+        
+            self.Cm = self.k**2.0/funm/funm
+            self.Cs = self.k**2.0/funm/funh
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+            
+            # extrapolation from mixed layer (instead of from surface) to 2meter
+            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
+            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
+            self.u2m    =                - self.uw     / self.ustar / self.k * funm
+            self.v2m    =                - self.vw     / self.ustar / self.k * funm
+        
+        
+        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
+        self.e2m    = self.q2m * self.Ps / 0.622
+     
+    def ribtol(self, Rib, zsl, z0m, z0h): 
+        if(Rib > 0.):
+            L    = 1.
+            L0   = 2.
+        else:
+            L  = -1.
+            L0 = -2.
+        #print(Rib,zsl,z0m,z0h)
+        
+        while (abs(L - L0) > 0.001):
+            L0      = L
+            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
+            Lstart  = L - 0.001*L
+            Lend    = L + 0.001*L
+            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
+                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+            L       = L - fx / fxdif
+            #print(L)
+            if(abs(L) > 1e12):
+                break
+
+        return L
+      
+    def psim(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psim = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+        return psim
+      
+    def psih(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psih  = 2. * np.log( (1. + x*x) / 2.)
+            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+        return psih
+ 
+    def jarvis_stewart(self):
+        # calculate surface resistances using Jarvis-Stewart model
+        if(self.sw_rad):
+            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
+        else:
+            f1 = 1.
+  
+        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
+            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
+        else:
+            f2 = 1.e8
+ 
+        # Limit f2 in case w2 > wfc, where f2 < 1
+        f2 = max(f2, 1.);
+ 
+        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
+        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
+  
+        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
+
+    def factorial(self,k):
+        factorial = 1
+        for n in range(2,k+1):
+            factorial = factorial * float(n)
+        return factorial;
+
+    def E1(self,x):
+        E1sum = 0
+        for k in range(1,100):
+            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
+        return -0.57721566490153286060 - np.log(x) - E1sum
+ 
+    def ags(self):
+        # Select index for plant type
+        if(self.c3c4 == 'c3'):
+            c = 0
+        elif(self.c3c4 == 'c4'):
+            c = 1
+        else:
+            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
+
+        # calculate CO2 compensation concentration
+        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
+
+        # calculate mesophyll conductance
+        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
+                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
+        gm            = gm / 1000. # conversion from mm s-1 to m s-1
+  
+        # calculate CO2 concentration inside the leaf (ci)
+        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
+        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
+  
+        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
+        D0            = (self.f0[c] - fmin) / self.ad[c]
+  
+        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
+        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
+        ci            = cfrac * (co2abs - CO2comp) + CO2comp
+  
+        # calculate maximal gross primary production in high light conditions (Ag)
+        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
+  
+        # calculate effect of soil moisture stress on gross assimilation rate
+        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
+  
+        # calculate stress function
+        if (self.c_beta == 0):
+            fstr = betaw;
+        else:
+            # Following Combe et al (2016)
+            if (self.c_beta < 0.25):
+                P = 6.4 * self.c_beta
+            elif (self.c_beta < 0.50):
+                P = 7.6 * self.c_beta - 0.3
+            else:
+                P = 2**(3.66 * self.c_beta + 0.34) - 1
+            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
+  
+        # calculate gross assimilation rate (Am)
+        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
+        Rdark        = (1. / 9.) * Am
+        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
+  
+        # calculate  light use efficiency
+        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
+  
+        # calculate gross primary productivity
+        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
+  
+        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
+        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
+        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
+  
+        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
+        a1           = 1. / (1. - self.f0[c])
+        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
+  
+        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
+  
+        # calculate surface resistance for moisture and carbon dioxide
+        self.rs      = 1. / (1.6 * gcco2)
+        rsCO2        = 1. / gcco2
+  
+        # calculate net flux of CO2 into the plant (An)
+        An           = -(co2abs - ci) / (self.ra + rsCO2)
+  
+        # CO2 soil surface flux
+        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
+        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
+  
+        # CO2 flux
+        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
+        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
+        self.wCO2    = self.wCO2A + self.wCO2R
+ 
+    def run_land_surface(self):
+        # compute ra
+        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
+        #print('ueff',self.u,self.v,self.wstar)
+
+        if(self.sw_sl):
+          self.ra = (self.Cs * ueff)**-1.
+        else:
+          self.ra = ueff / max(1.e-3, self.ustar)**2.
+
+        #print('ra',self.ra,self.ustar,ueff)
+
+        # first calculate essential thermodynamic variables
+        self.esat    = esat(self.theta)
+        self.qsat    = qsat(self.theta, self.Ps)
+        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
+        self.dqsatdT = 0.622 * desatdT / self.Ps
+        self.e       = self.q * self.Ps / 0.622
+
+        if(self.ls_type == 'js'): 
+            self.jarvis_stewart() 
+        elif(self.ls_type == 'ags'):
+            self.ags()
+        else:
+            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
+
+        # recompute f2 using wg instead of w2
+        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
+          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
+        else:
+          f2        = 1.e8
+        self.rssoil = self.rssoilmin * f2 
+ 
+        Wlmx = self.LAI * self.Wmax
+        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
+        self.cliq = min(1., self.Wl / Wlmx) 
+     
+        # calculate skin temperature implictly
+        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
+            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
+            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
+            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
+
+        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        #print('Ts',self.rs)
+
+        esatsurf      = esat(self.Ts)
+        self.qsatsurf = qsat(self.Ts, self.Ps)
+
+        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+  
+        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
+  
+        self.LE     = self.LEsoil + self.LEveg + self.LEliq
+        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
+        #print('H',self.ra,self.Ts,self.theta)
+        self.G      = self.Lambda * (self.Ts - self.Tsoil)
+        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
+        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
+        
+        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
+  
+        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
+   
+        d1          = 0.1
+        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
+        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
+        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
+        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
+  
+        # calculate kinematic heat fluxes
+        self.wtheta   = self.H  / (self.rho * self.cp)
+        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
+        self.wq       = self.LE / (self.rho * self.Lv)
+ 
+    def integrate_land_surface(self):
+        # integrate soil equations
+        Tsoil0        = self.Tsoil
+        wg0           = self.wg
+        Wl0           = self.Wl
+  
+        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
+        self.wg       = wg0     + self.dtcur * self.wgtend
+        self.Wl       = Wl0     + self.dtcur * self.Wltend
+  
+    # store model output
+    def store(self):
+        t                      = self.t
+        
+        self.out.time[t]          = t * self.dt / 3600. + self.tstart
+
+        # in case we are at the end of the simulation, we store the vertical
+        # profiles to the output
+        
+        # if t == (len(self.out.time) - 1):
+        #     self.out.air_ac = self.air_ac
+        #     self.out.air_ap = self.air_ap
+
+        
+        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
+        #  for key in self.out.__dict__.keys():
+        #      if key in self.__dict__:
+        #          self.out.__dict__[key][t]  = self.__dict__[key]
+        
+        self.out.h[t]          = self.h
+        
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
+        
+        self.out.gammatheta[t] = self.gammatheta
+        self.out.gammau[t]     = self.gammau
+        self.out.gammav[t]     = self.gammav
+        self.out.gammaq[t]     = self.gammaq
+        self.out.theta[t]      = self.theta
+        self.out.thetav[t]     = self.thetav
+        self.out.dtheta[t]     = self.dtheta
+        self.out.dthetav[t]    = self.dthetav
+        self.out.wtheta[t]     = self.wtheta
+        self.out.wthetav[t]    = self.wthetav
+        self.out.wthetae[t]    = self.wthetae
+        self.out.wthetave[t]   = self.wthetave
+        
+        self.out.q[t]          = self.q
+        self.out.dq[t]         = self.dq
+        self.out.wq[t]         = self.wq
+        self.out.wqe[t]        = self.wqe
+        self.out.wqM[t]        = self.wqM
+      
+        self.out.qsat[t]       = self.qsat
+        self.out.e[t]          = self.e
+        self.out.esat[t]       = self.esat
+      
+        fac = (self.rho*self.mco2)/self.mair
+        self.out.CO2[t]        = self.CO2
+        self.out.dCO2[t]       = self.dCO2
+        self.out.wCO2[t]       = self.wCO2  * fac
+        self.out.wCO2e[t]      = self.wCO2e * fac
+        self.out.wCO2R[t]      = self.wCO2R * fac
+        self.out.wCO2A[t]      = self.wCO2A * fac
+
+        self.out.u[t]          = self.u
+        self.out.du[t]         = self.du
+        self.out.uw[t]         = self.uw
+        
+        self.out.v[t]          = self.v
+        self.out.dv[t]         = self.dv
+        self.out.vw[t]         = self.vw
+        
+        self.out.T2m[t]        = self.T2m
+        self.out.q2m[t]        = self.q2m
+        self.out.u2m[t]        = self.u2m
+        self.out.v2m[t]        = self.v2m
+        self.out.e2m[t]        = self.e2m
+        self.out.esat2m[t]     = self.esat2m
+
+
+        self.out.Tsoil[t]      = self.Tsoil
+        self.out.T2[t]         = self.T2
+        self.out.Ts[t]         = self.Ts
+        self.out.wg[t]         = self.wg
+        
+        self.out.thetasurf[t]  = self.thetasurf
+        self.out.thetavsurf[t] = self.thetavsurf
+        self.out.qsurf[t]      = self.qsurf
+        self.out.ustar[t]      = self.ustar
+        self.out.Cm[t]         = self.Cm
+        self.out.Cs[t]         = self.Cs
+        self.out.L[t]          = self.L
+        self.out.Rib[t]        = self.Rib
+  
+        self.out.Swin[t]       = self.Swin
+        self.out.Swout[t]      = self.Swout
+        self.out.Lwin[t]       = self.Lwin
+        self.out.Lwout[t]      = self.Lwout
+        self.out.Q[t]          = self.Q
+  
+        self.out.ra[t]         = self.ra
+        self.out.rs[t]         = self.rs
+        self.out.H[t]          = self.H
+        self.out.LE[t]         = self.LE
+        self.out.LEliq[t]      = self.LEliq
+        self.out.LEveg[t]      = self.LEveg
+        self.out.LEsoil[t]     = self.LEsoil
+        self.out.LEpot[t]      = self.LEpot
+        self.out.LEref[t]      = self.LEref
+        self.out.G[t]          = self.G
+
+        self.out.zlcl[t]       = self.lcl
+        self.out.RH_h[t]       = self.RH_h
+
+        self.out.ac[t]         = self.ac
+        self.out.M[t]          = self.M
+        self.out.dz[t]         = self.dz_h
+        self.out.substeps[t]   = self.substeps
+  
+    # delete class variables to facilitate analysis in ipython
+    def exitmodel(self):
+        del(self.Lv)
+        del(self.cp)
+        del(self.rho)
+        del(self.k)
+        del(self.g)
+        del(self.Rd)
+        del(self.Rv)
+        del(self.bolz)
+        del(self.S0)
+        del(self.rhow)
+  
+        del(self.t)
+        del(self.dt)
+        del(self.tsteps)
+         
+        del(self.h)          
+        del(self.Ps)        
+        del(self.fc)        
+        del(self.ws)
+        del(self.we)
+        
+        del(self.theta)
+        del(self.dtheta)
+        del(self.gammatheta)
+        del(self.advtheta)
+        del(self.beta)
+        del(self.wtheta)
+    
+        del(self.T2m)
+        del(self.q2m)
+        del(self.e2m)
+        del(self.esat2m)
+        del(self.u2m)
+        del(self.v2m)
+        
+        del(self.thetasurf)
+        del(self.qsatsurf)
+        del(self.thetav)
+        del(self.dthetav)
+        del(self.thetavsurf)
+        del(self.qsurf)
+        del(self.wthetav)
+        
+        del(self.q)
+        del(self.qsat)
+        del(self.dqsatdT)
+        del(self.e)
+        del(self.esat)
+        del(self.dq)
+        del(self.gammaq)
+        del(self.advq)
+        del(self.wq)
+        
+        del(self.u)
+        del(self.du)
+        del(self.gammau)
+        del(self.advu)
+        
+        del(self.v)
+        del(self.dv)
+        del(self.gammav)
+        del(self.advv)
+  
+        del(self.htend)
+        del(self.thetatend)
+        del(self.dthetatend)
+        del(self.qtend)
+        del(self.dqtend)
+        del(self.utend)
+        del(self.dutend)
+        del(self.vtend)
+        del(self.dvtend)
+     
+        del(self.Tsoiltend) 
+        del(self.wgtend)  
+        del(self.Wltend) 
+  
+        del(self.ustar)
+        del(self.uw)
+        del(self.vw)
+        del(self.z0m)
+        del(self.z0h)        
+        del(self.Cm)         
+        del(self.Cs)
+        del(self.L)
+        del(self.Rib)
+        del(self.ra)
+  
+        del(self.lat)
+        del(self.lon)
+        del(self.doy)
+        del(self.tstart)
+   
+        del(self.Swin)
+        del(self.Swout)
+        del(self.Lwin)
+        del(self.Lwout)
+        del(self.cc)
+  
+        del(self.wg)
+        del(self.w2)
+        del(self.cveg)
+        del(self.cliq)
+        del(self.Tsoil)
+        del(self.T2)
+        del(self.a)
+        del(self.b)
+        del(self.p)
+        del(self.CGsat)
+  
+        del(self.wsat)
+        del(self.wfc)
+        del(self.wwilt)
+  
+        del(self.C1sat)
+        del(self.C2ref)
+  
+        del(self.LAI)
+        del(self.rs)
+        del(self.rssoil)
+        del(self.rsmin)
+        del(self.rssoilmin)
+        del(self.alpha)
+        del(self.gD)
+  
+        del(self.Ts)
+  
+        del(self.Wmax)
+        del(self.Wl)
+  
+        del(self.Lambda)
+        
+        del(self.Q)
+        del(self.H)
+        del(self.LE)
+        del(self.LEliq)
+        del(self.LEveg)
+        del(self.LEsoil)
+        del(self.LEpot)
+        del(self.LEref)
+        del(self.G)
+  
+        del(self.sw_ls)
+        del(self.sw_rad)
+        del(self.sw_sl)
+        del(self.sw_wind)
+        del(self.sw_shearwe)
+
+# class for storing mixed-layer model output data
+class model_output:
+    def __init__(self, tsteps):
+        self.time          = np.zeros(tsteps)    # time [s]
+
+        # mixed-layer variables
+        self.h          = np.zeros(tsteps)    # ABL height [m]
+        
+        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammau     = np.zeros(tsteps)
+        self.gammav     = np.zeros(tsteps)
+        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
+        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
+        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
+        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
+        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
+        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
+        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
+        
+        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
+        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
+        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
+        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
+
+        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
+        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
+        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
+
+        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
+        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
+        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
+        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
+        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
+        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
+        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
+        
+        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
+        
+        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
+
+        # diagnostic meteorological variables
+        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
+        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
+        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
+        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
+        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
+        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
+
+        # ground variables
+        self.Tsoil       = np.zeros(tsteps)
+        self.T2          = np.zeros(tsteps)
+        self.Ts          = np.zeros(tsteps)
+        self.wg          = np.zeros(tsteps)
+
+        # surface-layer variables
+        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
+        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
+        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
+        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
+        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
+        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
+        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
+        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
+        self.L          = np.zeros(tsteps)    # Obukhov length [m]
+        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
+
+        # radiation variables
+        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
+        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
+        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
+        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
+        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
+
+        # land surface variables
+        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
+        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
+        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
+        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
+        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
+        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
+        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
+        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
+        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
+        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
+
+        # Mixed-layer top variables
+        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
+        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
+
+        # cumulus variables
+        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
+        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
+        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
+        
+        
+        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
+
+# class for storing mixed-layer model input data
+class model_input:
+    def __init__(self):
+
+        # # comment not valid
+        # we comment out the initialization, because there is a problem when
+        # inheriting values from one the another class4gl_iput. We also expect
+        # that the user specifies all the required parmameters (if not, an error
+        # is raised). 
+
+        # general model variables
+        self.runtime    = None  # duration of model run [s]
+        self.dt         = None  # time step [s]
+
+        # mixed-layer variables
+        self.sw_ml      = None  # mixed-layer model switch
+        self.sw_shearwe = None  # Shear growth ABL switch
+        self.sw_fixft   = None  # Fix the free-troposphere switch
+        self.h          = None  # initial ABL height [m]
+        self.Ps         = None  # surface pressure [Pa]
+        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
+        self.fc         = None  # Coriolis parameter [s-1]
+        
+        self.theta      = None  # initial mixed-layer potential temperature [K]
+        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
+
+        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
+
+        self.dtheta     = None  # initial temperature jump at h [K]
+        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = None  # advection of heat [K s-1]
+        self.beta       = None  # entrainment ratio for virtual heat [-]
+        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
+        
+        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
+        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
+
+        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = None  # advection of moisture [kg kg-1 s-1]
+        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
+
+        self.CO2        = None  # initial mixed-layer potential temperature [K]
+        self.dCO2       = None  # initial temperature jump at h [K]
+        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advCO2     = None  # advection of heat [K s-1]
+        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
+        
+        self.sw_wind    = None  # prognostic wind switch
+        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.du         = None  # initial u-wind jump at h [m s-1]
+        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = None  # advection of u-wind [m s-2]
+
+        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = None  # initial u-wind jump at h [m s-1]
+        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = None  # advection of v-wind [m s-2]
+
+        # surface layer variables
+        self.sw_sl      = None  # surface layer switch
+        self.ustar      = None  # surface friction velocity [m s-1]
+        self.z0m        = None  # roughness length for momentum [m]
+        self.z0h        = None  # roughness length for scalars [m]
+        self.Cm         = None  # drag coefficient for momentum [-]
+        self.Cs         = None  # drag coefficient for scalars [-]
+        self.L          = None  # Obukhov length [-]
+        self.Rib        = None  # bulk Richardson number [-]
+
+        # radiation parameters
+        self.sw_rad     = None  # radiation switch
+        self.lat        = None  # latitude [deg]
+        self.lon        = None  # longitude [deg]
+        self.doy        = None  # day of the year [-]
+        self.tstart     = None  # time of the day [h UTC]
+        self.cc         = None  # cloud cover fraction [-]
+        self.Q          = None  # net radiation [W m-2] 
+        self.dFz        = None  # cloud top radiative divergence [W m-2] 
+
+        # land surface parameters
+        self.sw_ls      = None  # land surface switch
+        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
+        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = None  # temperature top soil layer [K]
+        self.T2         = None  # temperature deeper soil layer [K]
+        
+        self.a          = None  # Clapp and Hornberger retention curve parameter a
+        self.b          = None  # Clapp and Hornberger retention curve parameter b
+        self.p          = None  # Clapp and Hornberger retention curve parameter p 
+        self.CGsat      = None  # saturated soil conductivity for heat
+        
+        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
+        self.wfc        = None  # volumetric water content field capacity [-]
+        self.wwilt      = None  # volumetric water content wilting point [-]
+        
+        self.C1sat      = None 
+        self.C2ref      = None
+
+        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
+        
+        self.LAI        = None  # leaf area index [-]
+        self.gD         = None  # correction factor transpiration for VPD [-]
+        self.rsmin      = None  # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = None  # surface albedo [-]
+        
+        self.Ts         = None  # initial surface temperature [K]
+        
+        self.cveg       = None  # vegetation fraction [-]
+        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
+        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
+        
+        self.Lambda     = None  # thermal diffusivity skin layer [-]
+
+        # A-Gs parameters
+        self.c3c4       = None  # Plant type ('c3' or 'c4')
+
+        # Cumulus parameters
+        self.sw_cu      = None  # Cumulus parameterization switch
+        self.dz_h       = None  # Transition layer thickness [m]
+        
+# BEGIN -- HW 20171027
+        # self.cala       = None      # soil heat conductivity [W/(K*m)]
+        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+# END -- HW 20171027
diff --git a/class4gl/__init__.py b/class4gl/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/class4gl/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
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diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
new file mode 100644
index 0000000..7baaa51
--- /dev/null
+++ b/class4gl/class4gl.py
@@ -0,0 +1,1611 @@
+# -*- coding: utf-8 -*-
+
+"""
+
+Created on Mon Jan 29 12:33:51 2018
+
+Module file for class4gl, which  extents the class-model to be able to take
+global air profiles as input. It exists of:
+
+CLASSES:
+    - an input object, namely class4gl_input. It includes:
+        - a function to read Wyoming sounding data from a yyoming stream object
+        - a function to read global data from a globaldata library object 
+    - the model object: class4gl
+    - ....    
+
+DEPENDENCIES:
+    - xarray
+    - numpy
+    - data_global
+    - Pysolar
+    - yaml
+
+@author: Hendrik Wouters
+
+"""
+
+
+
+""" Setup of envirnoment """
+
+# Standard modules of the stand class-boundary-layer model
+from model import model
+from model import model_output as class4gl_output
+from model import model_input
+from model import qsat
+#from data_soundings import wyoming 
+import Pysolar
+import yaml
+import logging
+import warnings
+import pytz
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+
+# Generic Python Packages
+import numpy as np
+import datetime as dt
+import pandas as pd
+import xarray as xr
+import io
+#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
+from data_global import data_global
+grav = 9.81
+
+# this is just a generic input object
+class generic_input(object):
+    def __init__(self):
+        self.init = True
+
+
+# all units from all variables in CLASS(4GL) should be defined here!
+units = {
+         'h':'m',
+         'theta':'K', 
+         'q':'kg/kg',
+         'cc': '-',
+         'cveg': '-',
+         'wg': 'm3 m-3',
+         'w2': 'm3 m-3',
+         #'wg': 'kg/kg',
+         'Tsoil': 'K',
+         'T2': 'K',
+         'z0m': 'm',
+         'alpha': '-',
+         'LAI': '-',
+         'dhdt':'m/h',
+         'dthetadt':'K/h',
+         'dqdt':'kg/kg/h',
+         'BR': '-',
+         'EF': '-',
+}
+
+class class4gl_input(object):
+# this was the way it was defined previously.
+#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+
+    def __init__(self,set_pars_defaults=True,debug_level=None):
+
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        print('hello')
+        self.logger = logging.getLogger('class4gl_input')
+        print(self.logger)
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # # create logger
+        # self.logger = logging.getLogger('class4gl_input')
+        # self.logger.setLevel(debug_level)
+
+        # # create console handler and set level to debug
+        # ch = logging.StreamHandler()
+        # ch.setLevel(debug_level)
+
+        # # create formatter
+        # formatter = logging.Formatter('%(asctime)s - \
+        #                                %(name)s - \
+        #                                %(levelname)s - \
+        #                                %(message)s')
+        # add formatter to ch
+        # ch.setFormatter(formatter)
+     
+        # # add ch to logger
+        # self.logger.addHandler(ch)
+
+        # """ end set up logger """
+
+
+
+        # these are the standard model input single-value parameters for class
+        self.pars = model_input()
+
+        # diagnostic parameters of the initial profile
+        self.diag = dict()
+
+        # In this variable, we keep track of the different parameters from where it originates from. 
+        self.sources = {}
+
+        if set_pars_defaults:
+            self.set_pars_defaults()
+
+    def set_pars_defaults(self):
+
+        """ 
+        Create empty model_input and set up case
+        """
+        defaults = dict( 
+        dt         = 60.    , # time step [s] 
+        runtime    = 6*3600 ,  # total run time [s]
+        
+        # mixed-layer input
+        sw_ml      = True   ,  # mixed-layer model switch
+        sw_shearwe = False  ,  # shear growth mixed-layer switch
+        sw_fixft   = False  ,  # Fix the free-troposphere switch
+        h          = 200.   ,  # initial ABL height [m]
+        Ps         = 101300.,  # surface pressure [Pa]
+        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
+        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
+        
+        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
+        dtheta     = 1.     ,  # initial temperature jump at h [K]
+        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        advtheta   = 0.     ,  # advection of heat [K s-1]
+        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
+        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
+        
+        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
+        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
+        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
+        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
+        
+        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
+        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
+        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
+        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
+        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
+        sw_wind    = True  ,  # prognostic wind switch
+        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
+        du         = 0.     ,  # initial u-wind jump at h [m s-1]
+        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
+        advu       = 0.     ,  # advection of u-wind [m s-2]
+        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
+        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
+        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
+        advv       = 0.     ,  # advection of v-wind [m s-2]
+        sw_sl      = True   , # surface layer switch
+        ustar      = 0.3    ,  # surface friction velocity [m s-1]
+        z0m        = 0.02   ,  # roughness length for momentum [m]
+        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
+        sw_rad     = True   , # radiation switch
+        lat        = 51.97  ,  # latitude [deg]
+        lon        = -4.93  ,  # longitude [deg]
+        doy        = 268.   ,  # day of the year [-]
+        tstart     = 6.8    ,  # time of the day [h UTC]
+        cc         = 0.0    ,  # cloud cover fraction [-]
+        Q          = 400.   ,  # net radiation [W m-2] 
+        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
+        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
+        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
+        cveg       = 0.85   ,  # vegetation fraction [-]
+        Tsoil      = 295.   ,  # temperature top soil layer [K]
+        Ts         = 295.   ,    # initial surface temperature [K]
+        T2         = 296.   ,  # temperature deeper soil layer [K]
+        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
+        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
+        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
+        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
+        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
+        wfc        = 0.323  ,  # volumetric water content field capacity [-]
+        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
+        C1sat      = 0.132  ,  
+        C2ref      = 1.8    ,
+        LAI        = 2.     ,  # leaf area index [-]
+        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
+        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
+        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
+        alpha      = 0.25   ,  # surface albedo [-]
+        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
+        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
+        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
+        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
+        sw_cu      = False  ,  # Cumulus parameterization switch
+        dz_h       = 150.   ,  # Transition layer thickness [m]
+        cala       = None   ,  # soil heat conductivity [W/(K*m)]
+        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
+        sw_ls      = True   ,
+        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
+        sw_lit     = False,
+        )
+        pars = model_input()
+        for key in defaults:
+            pars.__dict__[key] = defaults[key]
+        
+        self.update(source='defaults',pars=pars)
+        
+    def clear(self):
+        """ this procudure clears the class4gl_input """
+
+        for key in list(self.__dict__.keys()):
+            del(self.__dict__[key])
+        self.__init__()
+
+    def dump(self,file):
+        """ this procedure dumps the class4gl_input object into a yaml file
+            
+            Input: 
+                - self.__dict__ (internal): the dictionary from which we read 
+            Output:
+                - file: All the parameters in self.__init__() are written to
+                the yaml file, including pars, air_ap, sources etc.
+        """
+        file.write('---\n')
+        index = file.tell()
+        file.write('# CLASS4GL input; format version: 0.1\n')
+
+        # write out the position of the current record
+        yaml.dump({'index':index}, file, default_flow_style=False)
+
+        # we do not include the none values
+        for key,data in self.__dict__.items():
+            #if ((type(data) == model_input) or (type(class4gl_input):
+            if key == 'pars':
+
+                pars = {'pars' : self.__dict__['pars'].__dict__}
+                parsout = {}
+                for key in pars.keys():
+                    if pars[key] is not None:
+                        parsout[key] = pars[key]
+
+                yaml.dump(parsout, file, default_flow_style=False)
+            elif type(data) == dict:
+                if key == 'sources':
+                    # in case of sources, we want to have a
+                    # condensed list format as well, so we leave out
+                    # 'default_flow_style=False'
+                    yaml.dump({key : data}, file)
+                else: 
+                    yaml.dump({key : data}, file,
+                              default_flow_style=False)
+            elif type(data) == pd.DataFrame:
+                # in case of dataframes (for profiles), we want to have a
+                # condensed list format as well, so we leave out
+                # 'default_flow_style=False'
+                yaml.dump({key: data.to_dict(orient='list')},file)
+
+                # # these are trials to get it into a more human-readable
+                # fixed-width format, but it is too complex
+                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
+                #file.write(stream)
+                
+                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
+                #file.write(key+': !!str |\n')
+                #file.write(str(data)+'\n')
+       
+    def load_yaml_dict(self,yaml_dict,reset=True):
+        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
+            
+            Input: 
+                - yaml_dict: the dictionary from which we read 
+                - reset: reset data before reading        
+            Output:
+                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
+        """
+        
+        if reset:
+            for key in list(self.__dict__.keys()):
+                del(self.__dict__[key])
+            self.__init__()
+
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                self.__dict__[key] = model_input()
+                self.__dict__[key].__dict__ = data
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            elif key == 'sources':
+                self.__dict__[key] = data
+            elif key == 'diag':
+                self.__dict__[key] = data
+            else: 
+                warnings.warn("Key '"+key+"' may not be implemented.")
+                self.__dict__[key] = data
+
+    def update(self,source,**kwargs):
+        """ this procedure is to make updates of input parameters and tracking
+        of their source more convenient. It implements the assignment of
+        parameter source/sensitivity experiment IDs ('eg.,
+        'defaults', 'sounding balloon', any satellite information, climate
+        models, sensitivity tests etc.). These are all stored in a convenient
+        way with as class4gl_input.sources.  This way, the user can always consult with
+        from where parameters data originates from.  
+        
+        Input:
+            - source:    name of the underlying dataset
+            - **kwargs: a dictionary of data input, for which the key values
+            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
+            the values is a again a dictionary/dataframe of datakeys/columns
+            ('wg','PRES','datetime', ...) and datavalues (either single values,
+            profiles ...), eg., 
+
+                pars = {'wg': 0.007  , 'w2', 0.005}
+                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
+                                     300.,...]}
+            
+        Output:
+            - self.__dict__[datatype] : object to which the parameters are
+                                        assigned. They can be consulted with
+                                        self.pars, self.profiles, etc.
+                                        
+            - self.sources[source] : It supplements the overview overview of
+                                     data sources can be consulted with
+                                     self.sources. The structure is as follows:
+                                     as:
+                self.sources = { 
+                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
+                'GLEAM' :  ['pars:wg','pars:w2', ...],
+                 ...
+                }
+        
+        """
+
+        #print(source,kwargs)
+
+        for key,data in kwargs.items():
+
+            #print(key)
+            # if the key is not in class4gl_input object, then just add it. In
+            # that case, the update procedures below will just overwrite it 
+            if key not in self.__dict__:
+                self.__dict__[key] = data
+
+
+            
+
+            #... we do an additional check to see whether there is a type
+            # match. I not then raise a key error
+            if (type(data) != type(self.__dict__[key]) \
+                # we allow dict input for model_input pars
+                and not ((key == 'pars') and (type(data) == dict) and \
+                (type(self.__dict__[key]) == model_input))):
+
+                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
+
+
+            # This variable keeps track of the added data that is supplemented
+            # by the current source. We add this to class4gl_input.sources
+            datakeys = []
+
+            #... and we update the class4gl_input data, and this depends on the
+            # data type
+
+            if type(self.__dict__[key]) == pd.DataFrame:
+                # If the data type is a dataframe, then we update the columns
+                for column in list(data.columns):
+                    #print(column)
+                    self.__dict__[key][column] = data[column]
+                    datakeys.append(column)
+                    
+
+            elif type(self.__dict__[key]) == model_input:
+                # if the data type is a model_input, then we update its internal
+                # dictionary of parameters
+                if type(data) == model_input:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data.__dict__}
+                    datakeys = list(data.__dict__.keys())
+                elif type(data) == dict:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data}
+                    datakeys = list(data.keys())
+                else:
+                    raise TypeError('input key '+key+' is not of the same type\
+                                    as the one in the class4gl_object')
+
+            elif type(self.__dict__[key]) == dict:
+                # if the data type is a dictionary, we update the
+                # dictionary 
+                self.__dict__[key] = {self.__dict__[key] , data}
+                datakeys = list(data.keys())
+
+
+            # if source entry is not existing yet, we add it
+            if source not in self.sources.keys():
+                self.sources[source] = []
+
+
+            # self.logger.debug('updating section "'+\
+            #                  key+' ('+' '.join(datakeys)+')'\
+            #                  '" from source \
+            #                  "'+source+'"')
+
+            # Update the source dictionary: add the provided data keys to the
+            # specified source list
+            for datakey in datakeys:
+                # At first, remove the occurences of the keys in the other
+                # source lists
+                for sourcekey,sourcelist in self.sources.items():
+                    if key+':'+datakey in sourcelist:
+                        self.sources[sourcekey].remove(key+':'+datakey)
+                # Afterwards, add it to the current source list
+                self.sources[source].append(key+':'+datakey)
+
+
+        # # in case the datatype is a class4gl_input_pars, we update its keys
+        # # according to **kwargs dictionary
+        # if type(self.__dict__[datatype]) == class4gl_input_pars:
+        #     # add the data parameters to the datatype object dictionary of the
+        #     # datatype
+        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
+        #                                        **kwargs}
+        # # in case, the datatype reflects a dataframe, we update the columns according
+        # # to the *args list
+        # elif type(self.__dict__[datatype]) == pd.DataFrame:
+        #     for dataframe in args:
+        #         for column in list(dataframe.columns):
+        #             self.__dict__[datatype][column] = dataframe[column]
+        
+
+    def get_profile(self,IOBJ, *args, **argv):
+        # if type(IOBJ) == wyoming:
+        self.get_profile_wyoming(IOBJ,*args,**argv)
+        # else:
+        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
+        
+    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
+        """ 
+            Purpose: 
+                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
+
+            Input:
+                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
+                function will take the profile at the stream's current
+                position. 
+                2. air_ap_mode: which air profile do we take? 
+                    - b : best
+                    - l : according to lower limit for the mixed-layer height
+                            estimate
+                    - u : according to upper limit for the mixed-layer height
+                            estimate
+
+
+            Output:
+                1. all single-value parameters are stored in the
+                   class4gl_input.pars object
+                2. the souding profiles are stored in the in the
+                   class4gl_input.air_balloon dataframe
+                3. modified sounding profiles for which the mixed layer height
+                   is fitted
+                4. ...
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = 'wyoming'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = air_ap_mode
+
+        """ Temporary variables used for output """
+        # single value parameters derived from the sounding profile
+        dpars = dict()
+        # profile values
+        air_balloon = pd.DataFrame()
+        # fitted profile values
+        air_ap = pd.DataFrame()
+        
+        string = wy_strm.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = wy_strm.current.find_next('pre').find_next('pre').text
+        
+        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
+        dpars = {**dpars,
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+               }
+        
+        # we get weird output when it's a numpy Timestamp, so we convert it to
+        # pd.datetime type
+
+        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
+        dpars['STNID'] = dpars['Station number']
+
+        # altitude above ground level
+        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        # absolute humidity in g/kg
+        air_balloon['q']= (air_balloon.MIXR/1000.) \
+                              / \
+                             (air_balloon.MIXR/1000.+1.)
+        # convert wind speed from knots to m/s
+        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
+        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        
+        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+
+        
+
+        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+
+        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
+        air_balloon['p'] = air_balloon.PRES*100.
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        #is_valid = (air_balloon.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_balloon.index[is_valid].values
+        print(valid_indices)
+
+        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+
+        air_balloon['t'] = air_balloon['TEMP']+273.15
+        air_balloon['theta'] = (air_balloon.t) * \
+                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+
+
+        if np.isnan(dpars['h']):
+            dpars['Ps'] = np.nan
+
+
+
+
+        if ~np.isnan(dpars['h']):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u 
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+            
+
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        air_ap_head['HGHT'] = air_ap_head['z'] \
+                                + \
+                                np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = dpars['theta']
+        z_low =     dpars['h']
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+
+        
+        dpars['lat'] = dpars['Station latitude']
+        dpars['latitude'] = dpars['lat']
+        
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        dpars['longitude'] = dpars['Station longitude']
+        
+        dpars['ldatetime'] = dpars['datetime'] \
+                            + \
+                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+
+        # # we make a pars object that is similar to the destination object
+        # pars = model_input()
+        # for key,value in dpars.items():
+        #     pars.__dict__[key] = value
+
+
+        # we round the columns to a specified decimal, so that we get a clean
+        # output format for yaml
+        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
+                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
+                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+# 
+        for column,decimal in decimals.items():
+            air_balloon[column] = air_balloon[column].round(decimal)
+            air_ap[column] = air_ap[column].round(decimal)
+
+        self.update(source='wyoming',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+
+        
+    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
+    
+        """
+        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
+                 according to the position (lat lon) and the class datetime and timespan
+                 globaldata should be a globaldata multifile object
+        
+        Input: 
+            - globaldata: this is the library object
+            - only_keys: only extract specified keys
+            - exclude_keys: do not inherit specified keys
+        """
+        classdatetime      = np.datetime64(self.pars.datetime_daylight)
+        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
+                                           + \
+                                           dt.timedelta(seconds=self.pars.runtime)\
+                                          )
+
+
+        # # list of variables that we get from global ground data
+        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
+        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
+        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
+        #                 'texture', 'itex', 'isoil', 'BR',
+        #                 'b', 'cveg',
+        #                 'C1sat', 
+        #                 'C2ref', 'p', 'a',
+        #                 ] #globaldata.datasets.keys():
+
+        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
+        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
+
+
+        if type(globaldata) is not data_global:
+            raise TypeError("Wrong type of input library") 
+
+        # by default, we get all dataset keys
+        keys = list(globaldata.datasets.keys())
+
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
+        # # In case there is surface pressure, we also calculate the half-level
+        # # and full-level pressure fields
+        # if ('sp' in keys):
+        #     keys.append('pfull')
+        #     keys.append('phalf')
+
+        # If specified, we only take the keys that are in only_keys
+        if only_keys is not None:
+            for key in keys:
+                if key not in only_keys:
+                    keys.remove(key)
+                
+        # If specified, we take out keys that are in exclude keys
+        if exclude_keys is not None:
+            for key in keys:
+                if key in exclude_keys:
+                    keys.remove(key)
+
+        # we set everything to nan first in the pars section (non-profile parameters
+        # without lev argument), so that we can check afterwards whether the
+        # data is well-fetched or not.
+        for key in keys:
+            if not ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None) and \
+                ('lev' in globaldata.datasets[key].page[key].dims)):
+                self.update(source='globaldata',pars={key:np.nan})
+            # # we do not check profile input for now. We assume it is
+            # # available
+            #else:
+            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
+
+        self.logger.debug('getting keys "'+', '.join(keys)+'\
+                          from global data')
+
+        for key in keys:
+            # If we find it, then we obtain the variables
+            if ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None)):
+
+                # check first whether the dataset has a height coordinate (3d space)
+                if 'lev' in globaldata.datasets[key].page[key].dims:
+
+                    # first, we browse to the correct file that has the current time
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+                        globaldata.datasets[key].browse_page(time=classdatetime)
+
+                    
+                    if (globaldata.datasets[key].page is not None):
+                        # find longitude and latitude coordinates
+                        ilats = (np.abs(globaldata.datasets[key].page.lat -
+                                        self.pars.latitude) < 0.5)
+                        ilons = (np.abs(globaldata.datasets[key].page.lon -
+                                        self.pars.longitude) < 0.5)
+                        
+                        # if we have a time dimension, then we look up the required timesteps during the class simulation
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            itimes = ((globaldata.datasets[key].page.time >= \
+                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            # In case we didn't find any correct time, we take the
+                            # closest one.
+                            if np.sum(itimes) == 0.:
+
+
+                                classdatetimemean = \
+                                    np.datetime64(self.pars.datetime_daylight + \
+                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
+                                                ))
+
+                                dstimes = globaldata.datasets[key].page.time
+                                time = dstimes.sel(time=classdatetimemean,method='nearest')
+                                itimes = (globaldata.datasets[key].page.time ==
+                                          time)
+                                
+                        else:
+                            # we don't have a time coordinate so it doesn't matter
+                            # what itimes is
+                            itimes = 0
+
+                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+
+                        # over which dimensions we take a mean:
+                        dims = globaldata.datasets[key].page[key].dims
+                        namesmean = list(dims)
+                        namesmean.remove('lev')
+                        idxmean = [dims.index(namemean) for namemean in namesmean]
+                        
+                        value = \
+                        globaldata.datasets[key].page[key].isel(time=itimes,
+                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+                        # Ideally, source should be equal to the datakey of globaldata.library 
+                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+                        #  but therefore the globaldata class requires a revision to make this work
+                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+
+                else:
+                    # this procedure is for reading the ground fields (2d space). 
+                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
+
+    
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+    
+                       # first, we browse to the correct file
+                       #print(key)
+                       globaldata.datasets[key].browse_page(time=classdatetime)
+    
+                    if globaldata.datasets[key].page is not None:
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - self.pars.latitude))
+                        ilat = np.where((DIST) == np.min(DIST))[0][0]
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - self.pars.longitude))
+                        ilon = np.where((DIST) == np.min(DIST))[0][0]
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - (self.pars.latitude + 0.5)))
+                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmax = ilat
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - (self.pars.longitude  + 0.5)))
+                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmax = ilon
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lat.values\
+                                - (self.pars.latitude - 0.5)))
+                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmin = ilat
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lon.values\
+                                - (self.pars.longitude  - 0.5)))
+                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmin = ilon        
+                        
+                        if ilatmin < ilatmax:
+                            ilatrange = range(ilatmin,ilatmax+1)
+                        else:
+                            ilatrange = range(ilatmax,ilatmin+1)
+                            
+                        if ilonmin < ilonmax:
+                            ilonrange = range(ilonmin,ilonmax+1)
+                        else:
+                            ilonrange = range(ilonmax,ilonmin+1)     
+                            
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                idatetime += 1
+                            
+                            classdatetimeend = np.datetime64(\
+                                                             self.pars.datetime +\
+                                                             dt.timedelta(seconds=self.pars.runtime)\
+                                                            ) 
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+                            if key in ['wg', 'w2']:
+                                idatetime = idatetime - 1
+                                idatetimeend = idatetimeend - 1
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['Tsoil','T2']:
+                                idatetimeend = idatetime
+                            
+                            idts = range(idatetime,idatetimeend+1)
+                            
+                            count = 0
+                            self.__dict__[key] = 0.
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    for iidts in idts:
+                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
+                                        count += 1
+                            value = value/count
+                            self.update(source='globaldata',pars={key:value.item()})
+                                
+                        else:
+                                
+                            count = 0
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
+                                    count += 1
+                            value = value/count                        
+
+                            self.update(source='globaldata',pars={key:value.item()})
+
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            self.logger.debug('also update LAI based on LAIpixel and cveg') 
+            # I suppose LAI pixel is already determined in the previous
+            # procedure. Anyway...
+            key = 'LAIpixel'
+
+            if globaldata.datasets[key].page is not None:
+                # first, we browse to the correct file that has the current time
+                if 'time' in list(globaldata.datasets[key].page[key].dims):
+                    globaldata.datasets[key].browse_page(time=classdatetime)
+            
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - self.pars.latitude))
+                ilat = np.where((DIST) == np.min(DIST))[0][0]
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - self.pars.longitude))
+                ilon = np.where((DIST) == np.min(DIST))[0][0]
+                 
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude + 0.5)))
+                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmax = ilat
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values \
+                        - (self.pars.longitude  + 0.5)))
+                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmax = ilon
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude - 0.5)))
+                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmin = ilat
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - (self.pars.longitude  - 0.5)))
+                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmin = ilon        
+                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                
+                
+                if ilatmin < ilatmax:
+                    ilatrange = range(ilatmin,ilatmax+1)
+                else:
+                    ilatrange = range(ilatmax,ilatmin+1)
+                    
+                if ilonmin < ilonmax:
+                    ilonrange = range(ilonmin,ilonmax+1)
+                else:
+                    ilonrange = range(ilonmax,ilonmin+1)           
+                
+                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
+                LAIpixel = 0.
+                count = 0
+                for iilat in [ilat]: #ilatrange
+                    for iilon in [ilon]: #ilonrange
+                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
+                        
+                                        
+                        # if np.isnan(tarray[idatetime]):
+                        #     print("interpolating GIMMS LAIpixel nan value")
+                        #     
+                        #     mask = np.isnan(tarray)
+                        #     
+                        #     #replace each nan value with a interpolated value
+                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+                        #         
+                        #     else:
+                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
+                    
+                        #         tarray *= np.nan 
+                        
+                        count += 1
+                        #tarray_res += tarray
+                LAIpixel = LAIpixel/count
+                
+                count = 0
+                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
+  
+                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
+                #print('LAIpixel:',self.__dict__['LAIpixel'])
+                #print('cveg:',self.__dict__['cveg'])
+                
+                # finally, we rescale the LAI according to the vegetation
+                # fraction
+                value = 0. 
+                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
+                   value =self.pars.LAIpixel/self.pars.cveg
+                else:
+                    # in case of small vegetation fraction, we take just a standard 
+                    # LAI value. It doesn't have a big influence anyway for
+                    # small vegetation
+                    value = 2.
+                #print('LAI:',self.__dict__['LAI'])
+                self.update(source='globaldata',pars={'LAI':value}) 
+
+
+        # in case we have 'sp', we also calculate the 3d pressure fields at
+        # full level and half level
+        if ('sp' in keys) and ('sp' in self.pars.__dict__):
+            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
+
+            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # hydrostatic thickness of each model layer
+            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
+            # # dz = rhodz/(R * T / pfull)
+
+
+            # # subsidence multiplied by density. We calculate the subsidence of
+            # # the in class itself
+            # wrho = np.zeros_like(phalf)
+            # wrho[-1] = 0. 
+
+            # for ihlev in range(0,wrho.shape[0]-1):
+            #     # subsidence multiplied by density is the integral of
+            #     # divergences multiplied by the layer thicknessies
+            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
+            #                     self.air_ac['divU_y'][ihlev:]) * \
+            #                    delpdgrav[ihlev:]).sum()
+
+
+            
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'p':list(pfull)}))
+            self.update(source='globaldata',\
+                        air_ach=pd.DataFrame({'p':list(phalf)}))
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
+            # self.update(source='globaldata',\
+            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
+
+    def check_source(self,source,check_only_sections=None):
+        """ this procedure checks whether data of a specified source is valid.
+
+        INPUT:
+            source: the data source we want to check
+            check_only_sections: a string or list with sections to be checked
+        OUTPUT:
+            returns True or False
+        """
+
+        # we set source ok to false as soon as we find a invalid input
+        source_ok = True
+
+        # convert to a single-item list in case of a string
+        check_only_sections_def = (([check_only_sections]) if \
+                                   type(check_only_sections) is str else \
+                                    check_only_sections)
+                                  
+        if source not in self.sources.keys():
+            self.logger.info('Source '+source+' does not exist')
+            source_ok = False
+
+        for sectiondatakey in self.sources[source]:                             
+            section,datakey = sectiondatakey.split(':')                         
+            if ((check_only_sections_def is None) or \
+                (section in check_only_sections_def)):                          
+                checkdatakeys = []
+                if type(self.__dict__[section]) is pd.DataFrame:
+                    checkdata = self.__dict__[section]
+                elif type(self.__dict__[section]) is model_input:
+                    checkdata = self.__dict__[section].__dict__
+
+                if (datakey not in checkdata):                              
+                    # self.logger.info('Expected key '+datakey+\
+                    #                  ' is not in parameter input')                        
+                    source_ok = False                                           
+                elif (checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True):                    
+        
+                    # self.logger.info('Key value of "'+datakey+\
+                    #                  '" is invalid: ('+ \
+                    # str(self.__dict__[section].__dict__[datakey])+')')         
+                    source_ok = False
+
+        return source_ok
+
+    def check_source_globaldata(self):
+        """ this procedure checks whether all global parameter data is
+        available, according to the keys in the self.sources"""
+
+        source_globaldata_ok = True
+
+        #self.get_values_air_input()
+
+        # and now we can get the surface values
+        #class_settings = class4gl_input()
+        #class_settings.set_air_input(input_atm)
+        
+        # we only allow non-polar stations
+        if not (self.pars.lat <= 60.):
+            source_globaldata_ok = False
+            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+        
+        # check lat and lon
+        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
+            source_globaldata_ok = False
+            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+        else:
+            # we only check the ground parameter data (pars section). The 
+            # profile data (air_ap section) are supposed to be valid in any 
+            # case.
+            source_ok = self.check_source(source='globaldata',\
+                                          check_only_sections=['air_ac',\
+                                                               'air_ap',\
+                                                               'pars'])
+            if not source_ok:
+                source_globaldata_ok = False
+        
+            # Additional check: we exclude desert-like
+            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
+                source_globaldata_ok = False
+                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
+                source_globaldata_ok = False
+                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+            elif self.pars.cveg < 0.02:
+                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                source_globaldata_ok = False
+
+        return source_globaldata_ok
+
+
+class c4gli_iterator():
+    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
+    
+        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
+    """
+    def __init__(self,file):
+        # take file as IO stream
+        self.file = file
+        self.yaml_generator = yaml.load_all(file)
+        self.current_dict = {}
+        self.current_class4gl_input = class4gl_input()
+        separator = self.file.readline() # this is just dummy
+        self.header = file.readline()
+        if self.header != '# CLASS4GL record; format version: 0.1\n':
+            raise NotImplementedError("Wrong format version: '"+self.header+"'")
+    def __iter__(self):
+        return self
+    def __next__(self):
+        self.current_dict = self.yaml_generator.__next__()
+        self.current_class4gl_input.load_yaml_dict(self.current_dict)
+        return self.current_class4gl_input
+
+
+
+#get_cape and lift_parcel are adapted from the SkewT package
+    
+class gl_dia(object):
+    def get_lifted_index(self,timestep=-1):
+        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
+    
+#from SkewT
+#def get_lcl(startp,startt,startdp,nsteps=101):
+#    from numpy import interp
+#    #--------------------------------------------------------------------
+#    # Lift a parcel dry adiabatically from startp to LCL.
+#    # Init temp is startt in K, Init dew point is stwrtdp,
+#    # pressure levels are in Pa    
+#    #--------------------------------------------------------------------
+#
+#    assert startdp<=startt
+#
+#    if startdp==startt:
+#        return np.array([startp]),np.array([startt]),np.array([startdp]),
+#
+#    # Pres=linspace(startp,60000.,nsteps)
+#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
+#
+#    # Lift the dry parcel
+#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
+#    # Mixing ratio isopleth
+#    starte=VaporPressure(startdp)
+#    startw=MixRatio(starte,startp)
+#    e=Pres*startw/(.622+startw)
+#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
+#
+#    # Solve for the intersection of these lines (LCL).
+#    # interp requires the x argument (argument 2)
+#    # to be ascending in order!
+#    P_lcl=interp(0.,T_iso-T_dry,Pres)
+#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
+#
+#    # # presdry=linspace(startp,P_lcl)
+#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
+#
+#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
+#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
+#
+#    return P_lcl,T_lcl
+
+
+
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
+
+
+
+#from class
+def get_lcl(startp,startt,startqv):
+        # Find lifting condensation level iteratively
+    lcl = 20.
+    RHlcl = 0.5
+    
+    itmax = 30
+    it = 0
+    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHd
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self):
+       self.status = 'init'
+       self.found = False
+       self.DT = None
+       self.current = None
+       #self.mode = 'b'
+       self.profile_type = 'wyoming'  
+       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+         
+    def set_STNM(self,STNM):
+        self.__init__()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+        self.current = self.sounding_series.find('h2')
+        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+        
+        # go through other files and find first sounding when year is not specified
+        self.iFN=self.iFN+1
+        while keepsearching:
+            self.FN = self.FILES[self.iFN]
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            self.iFN=self.iFN+1
+            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+        self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        if self.found and get_atm:
+            self.get_values_air_input()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        #print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                if get_atm:
+                    self.get_values_air_input()
+                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        if self.found and get_atm:
+            self.get_values_air_input()
+       
+
+
+    def get_values_air_input(self,latitude=None,longitude=None):
+
+        # for iDT,DT in enumerate(DTS):
+        
+            #websource = urllib.request.urlopen(webpage)
+        #soup = BeautifulSoup(open(webpage), "html.parser")
+        
+       
+        #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        BLHV = np.max((BLHV,10.))
+        BLHVu = np.max((BLHVu,10.))
+        BLHVd = np.max((BLHVd,10.))
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+                
+                # get index of lowest valid observation. This seems to vary
+                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+                if len(idxvalid) > 0:
+                    #print('idxvalid',idxvalid)
+                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+                    else:
+                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+                    #print(col,meanabl)
+               
+                
+                # if col == 'PRES':
+                #     meanabl =  
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV',]:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+                        dtheta = np.max((0.1,dtheta_pre))
+                        #meanabl = meanabl - (dtheta - dtheta_pre)
+                        #print('dtheta_pre',dtheta_pre)
+                        #print('dtheta',dtheta)
+                        #print('meanabl',meanabl)
+                        #stop
+                        
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            
+            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+        
+            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+
+        try:
+            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+        except:
+            print("could not convert latitude coordinate")
+            PARAMS.insert(0,'latitude', np.nan)
+            PARAMS.insert(0,'lat', np.nan)
+        try:
+            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+            PARAMS.insert(0,'lon', 0.)
+        except:
+            print("could not convert longitude coordinate")
+            PARAMS.insert(0,'longitude', np.nan)
+            PARAMS.insert(0,'lon', 0.)
+
+        if latitude is not None:
+            print('overwriting latitude with specified value')
+            PARAMS['latitude'] = np.float(latitude)
+            PARAMS['lat'] = np.float(latitude)
+        if longitude is not None:
+            print('overwriting longitude with specified value')
+            PARAMS['longitude'] = np.float(longitude)
+        try:
+            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            # This is the nearest datetime when sun is up (for class)
+            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+            # apply the same time shift for UTC datetime
+            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+            
+        except:
+            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+
+        
+
+        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+        # as we are forcing lon equal to zero this is is expressed in local suntime
+        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
+        elif self.mode == 'b': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNb
+            BLCOLUMN = ONE_COLUMNb
+        elif self.mode == 'u': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNu
+            BLCOLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNd
+            BLCOLUMN = ONE_COLUMNd
+        else:
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb
+
+        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
+        # print(BLCOLUMN['HAGL'][lt6000])
+        # print(BLCOLUMN['HAGL'][lt2500])
+        # 
+        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+
+        #print(BLCOLUMN['HAGL'][lt2500])
+        PARAMS.insert(0,'OK',
+                      ((BLHVe < 200.) and 
+                       ( len(np.where(lt6000)[0]) > 5) and
+                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+                      )
+                     )
+
+        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+        
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = USE_ONECOLUMN
+        # if self.mode == 'o': #original 
+        #     self.ONE_COLUMN = ONE_COLUMN
+        # elif self.mode == 'b': # best BLH
+        #     self.ONE_COLUMN = ONE_COLUMNb
+        # elif self.mode == 'u':# upper BLH
+        #     self.ONE_COLUMN = ONE_COLUMNu
+        # elif self.mode == 'd': # lower BLH
+        #     self.ONE_COLUMN=ONE_COLUMNd
+        # else:
+        #     self.ONE_COLUMN = ONE_COLUMN
+
diff --git a/class4gl/data_global.py b/class4gl/data_global.py
new file mode 100644
index 0000000..9c3d9b5
--- /dev/null
+++ b/class4gl/data_global.py
@@ -0,0 +1,936 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: Hendrik Wouters
+
+Purpose: provides class routines for ground and atmosphere conditions used for
+the CLASS miced-layer model
+
+Usage:
+    from data_global import data_global
+    from class4gl import class4gl_input
+    from data_soundings import wyoming
+
+    # create a data_global object and load initial data pages
+    globaldata = data_global()
+    globaldata.load_datasets()
+    # create a class4gl_input object
+    c4gli = class4gl_input()
+    # Initialize it with profile data. We need to do this first. Actually this
+    # will set the coordinate parameters (datetime, latitude, longitude) in
+    # class4gl_input.pars.__dict__, which is required to read point data from
+    # the data_global object.
+
+    # open a Wyoming stream for a specific station
+    wy_strm = wyoming(STNM=91376)
+    # load the first profile
+    wy_strm.find_first()
+    # load the profile data into the class4gl_input object
+    c4gli.get_profile_wyoming(wy_strm)
+    
+    # and finally, read the global input data for this profile
+    c4gli.get_global_input(globaldata)
+
+
+"""
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+#import pynacolada as pcd
+import pandas as pd
+import xarray as xr
+import os
+import glob
+import sys
+import errno
+import warnings
+import logging
+
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+class book(object):
+    """ this is a class for a dataset spread over multiple files. It has a
+    similar purpose  open_mfdataset, but only 1 file (called current 'page')
+    one is loaded at a time. This saves precious memory.  """
+    def __init__(self,fn,concat_dim = None,debug_level=None):
+        self.logger = logging.getLogger('book')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # filenames are expanded as a list and sorted by filename
+        self.pages = glob.glob(fn); self.pages.sort()
+        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
+        if len(self.pages) == 0:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
+        self.ipage = -1; self.page = None
+        self.renames = {} # each time when opening a file, a renaming should be done.
+        self.set_page(0)
+
+        # we consider that the outer dimension is the one we concatenate
+        self.concat_dim = concat_dim
+        if self.concat_dim is None:
+            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
+
+    # this wraps the xarray sel-commmand
+    def sel(*args, **kwargs):
+        for dim in kwargs.keys():
+            if dim == self.concat_dim:
+                self.browse_page(**{dim: kwargs[dim]})
+        return page.sel(*args,**kwargs)
+
+
+    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
+    #def __getattr__(self,attr):
+    #    orig_attr = self.page.__getattribute__(attr)
+    #    if callable(orig_attr):
+    #        def hooked(*args, **kwargs):
+    #            for dim in kwargs.keys():
+    #                if dim == self.concat_dim:
+    #                    self.browse_page(**{dim: kwargs[dim]})
+    #
+    #            result = orig_attr(*args, **kwargs)
+    #            # prevent wrapped_class from becoming unwrapped
+    #            if result == self.page:
+    #                return self
+    #            self.post()
+    #            return result
+    #        return hooked
+    #    else:
+    #        return orig_attr
+
+    def set_renames(self,renames):
+        #first, we convert back to original names, and afterwards, we apply the update of the renames.
+        reverse_renames = dict((v,k) for k,v in self.renames.items())
+        self.renames = renames
+        if self.page is not None:
+            self.page = self.page.rename(reverse_renames)
+            self.page = self.page.rename(self.renames)
+
+    def set_page(self,ipage,page=None):
+        """ this sets the right page according to ipage:
+                - We do not switch the page if we are already at the right one
+                - we set the correct renamings (level -> lev, latitude -> lat,
+                etc.)
+                - The dataset is also squeezed.
+        """
+
+        if ((ipage != self.ipage) or (page is not None)):
+
+            if self.page is not None:
+                self.page.close()
+
+            self.ipage = ipage
+            if page is not None:
+                self.page = page
+            else:
+                if self.ipage == -1:
+                   self.page = None
+                else:
+                    #try:
+
+                    self.logger.info("Switching to page "+str(self.ipage)+': '\
+                                     +self.pages[self.ipage])
+                    self.page = xr.open_dataset(self.pages[self.ipage])
+
+
+            # do some final corrections to the dataset to make them uniform
+            if self.page is not None:
+               if 'latitude' in self.page.dims:
+#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
+               if 'level' in self.page.dims:
+                   self.page = self.page.rename({'level':'lev'})
+
+               self.page = self.page.rename(self.renames)
+               self.page = self.page.squeeze(drop=True)
+
+    def browse_page(self,rewind=2,**args):
+
+        # at the moment, this is only tested with files that are stacked according to the time dimension.
+        dims = args.keys()
+
+
+        if self.ipage == -1:
+            self.set_page(0)
+
+        found = False
+        iipage = 0
+        startipage = self.ipage - rewind
+        while (iipage < len(self.pages)) and not found:
+            ipage = (iipage+startipage) % len(self.pages)
+            for dim in args.keys():
+                this_file = True
+
+                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
+                if 'dims' not in self.__dict__:
+                    self.dims = {}
+                if dim not in self.dims.keys():
+                    self.dims[dim] = [None]*len(self.pages)
+
+                if self.dims[dim][ipage] is None:
+                    self.logger.info('Loading coordinates of dimension "'+dim+\
+                                     '" of page "' +str(ipage)+'".')
+                    self.set_page(ipage)
+                    # print(ipage)
+                    # print(dim)
+                    # print(dim,self.page[dim].values)
+                    self.dims[dim][ipage] = self.page[dim].values
+
+                # determine current time range of the current page
+                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
+                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
+
+                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
+                    this_file = False
+
+            if this_file:
+                found = True
+                self.set_page(ipage)
+            else:
+
+                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
+                #    iipage = len(self.pages) # we stop searching
+
+                iipage += 1
+
+        if not found:
+            self.logger.info("Page not found. Setting to page -1")
+            #iipage = len(self.pages) # we stop searching further
+            self.set_page(-1)
+
+        if self.ipage != -1:
+            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
+        else:
+            self.logger.debug("I'm now at page "+ str(self.ipage))
+
+
+class data_global(object):
+    def __init__(self,sources= {
+        # # old gleam
+        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
+        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
+        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
+        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
+        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
+        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
+        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
+        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
+        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
+        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
+        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
+        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
+        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
+        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
+        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
+        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
+        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
+        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
+        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
+        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
+        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
+        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
+        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
+        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
+        },debug_level=None):
+        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
+        self.sources = sources
+        self.datarefs = {}
+        self.datasets = {}
+        self.datetime = dt.datetime(1981,1,1)
+
+        self.logger = logging.getLogger('data_global')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+        self.debug_level = debug_level
+
+        warnings.warn('omitting pressure field p and advection')
+
+    def in_library(self,fn):
+        if fn not in self.library.keys():
+            return False
+        else:
+            print("Warning: "+fn+" is already in the library.")
+            return True
+
+    def add_to_library(self,fn):
+        if not self.in_library(fn):
+            print("opening: "+fn)
+            self.library[fn] = \
+                book(fn,concat_dim='time',debug_level=self.debug_level)
+
+            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
+            #if 'latitude' in self.library[fn].variables:
+            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+
+    # default procedure for loading datasets into the globaldata library
+    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
+        if type(varssource) is str:
+            varssource = [varssource]
+        if type(varsdest) is str:
+            varsdest = [varsdest]
+
+        self.add_to_library(input_fn)
+
+        if varssource is None:
+            varssource = []
+            for var in self.sources[input_fn].variables:
+                avoid = \
+                ['lat','lon','latitude','longitude','time','lev','level']
+                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
+                    varssource.append(var)
+
+        if varsdest is None:
+            varsdest = varssource
+
+        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        for ivar,vardest in enumerate(varsdest):
+            varsource = varssource[ivar]
+            print('setting '+vardest+' as '+varsource+' from '+input_fn)
+
+            if vardest in self.datarefs.keys():
+                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
+            #self.add_to_library(fn,varsource,vardest)
+            if vardest != varsource:
+                libkey = input_fn+'.'+varsource+'.'+vardest
+                if libkey not in self.library.keys():
+                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
+                    self.library[libkey] = book(input_fn,\
+                                                debug_level=self.debug_level)
+                    self.library[libkey].set_renames({varsource: vardest})
+
+                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+            else:
+                self.datarefs[vardest] = input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+
+            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
+            #     print('Warning: '+ vardest "not in " + input_fn)
+
+
+
+    def load_datasets(self,sources = None,recalc=0):
+
+        if sources is None:
+            sources = self.sources
+        for key in sources.keys():
+            #datakey,vardest,*args = key.split(':')
+            datakey,vardest = key.split(':')
+            #print(datakey)
+
+            fnvarsource = sources[key].split(':')
+            if len(fnvarsource) > 2:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource,fnargs = fnvarsource
+                fnargs = [fnargs]
+            elif len(fnvarsource) > 1:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource = fnvarsource
+                fnargs = []
+            else:
+                fn = sources[key]
+                varsource = vardest
+            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
+
+    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
+            # the default way of loading a 2d dataset
+            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
+                self.load_dataset_default(fn,varsource,vardest)
+            elif datakey == 'IGBPDIS':
+                if vardest == 'alpha':
+                    ltypes = ['W','B','H','TC']
+                    for ltype in ltypes:
+                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
+                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
+
+
+                    # landfr = {}
+                    # for ltype in ['W','B','H','TC']:
+                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
+
+
+
+                    keytemp = 'alpha'
+                    fnkeytemp = fn+':IGBPDIS:alpha'
+                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
+                        self.library[fnkeytemp]  = book(fnkeytemp,
+                                                        debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+                    else:
+                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
+                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
+                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
+                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
+                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
+                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
+                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
+
+                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+
+                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
+                        for ltype in ltypes:
+                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
+
+                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
+                        print('writing file to: '+fnkeytemp)
+                        os.system('rm '+fnkeytemp)
+                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
+                        self.library[fnkeytemp].close()
+
+
+                        self.library[fnkeytemp]  = \
+                            book(fnkeytemp,debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+
+
+                else:
+                    self.load_dataset_default(fn,varsource,vardest)
+
+
+            elif datakey == 'GLAS':
+                self.load_dataset_default(fn,varsource,vardest)
+                if vardest == 'z0m':
+                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
+                elif vardest == 'z0h':
+                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
+            elif datakey == 'DSMW':
+
+
+                # Procedure of the thermal properties:
+                # 1. determine soil texture from DSMW/10.
+                # 2. soil type with look-up table (according to DWD/EXTPAR)
+                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
+                #    with parameter look-up table from Noilhan and Planton (1989).
+                #    Note: The look-up table is inspired on DWD/COSMO
+
+                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
+
+
+
+                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
+                self.load_dataset_default(fn,'DSMW')
+                print('calculating texture')
+                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
+                TEMP  = {}
+                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
+                TEMP3 = {}
+                for SPKEY in SPKEYS:
+
+
+                    keytemp = SPKEY+'_values'
+                    fnoutkeytemp = fnout+':DSMW:'+keytemp
+                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                    else:
+                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
+                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
+                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+                        # for faster computation, we need to get it to memory out of Dask.
+                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
+                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
+
+                # yes, I know I only check the last file.
+                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
+                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
+                        print('idx',idx,SPKEY)
+                        SEL = (TEMP2 == idx)
+                    #     print(idx,len(TEMP3))
+                        for SPKEY in SPKEYS:
+                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
+
+                    for SPKEY in SPKEYS:
+                        keytemp = SPKEY+'_values'
+                        fnoutkeytemp = fnout+':DSMW:'+keytemp
+                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
+                        os.system('rm '+fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].close()
+
+
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                keytemp = 'texture'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+                else:
+                    self.library[fn+':DSMW:texture'] = xr.Dataset()
+                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
+                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
+                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
+                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+
+                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
+
+                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
+                    zundef[zundef < 0] = np.nan
+                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
+                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
+
+                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+
+
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+                print('calculating texture type')
+
+
+
+                keytemp = 'itex'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+                else:
+                    self.library[fnoutkeytemp] = xr.Dataset()
+                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+                    X = self.datasets['texture'].page['texture'].values*100
+                    X[pd.isnull(X)] = -9
+
+
+                    self.datasets[keytemp][keytemp].values = X
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
+                    self.datasets['itex'].close()
+
+
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+
+                keytemp = 'isoil'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                isoil_reprocessed = False
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+                else:
+                    isoil_reprocessed = True
+                    print('calculating soil type')
+                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    ITEX = self.datasets['itex'].page['itex'].values
+                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
+                    LOOKUP = [
+                              [-10 ,9],# ocean
+                              [0 ,7],# fine textured, clay (soil type 7)
+                              [20,6],# medium to fine textured, loamy clay (soil type 6)
+                              [40,5],# medium textured, loam (soil type 5)
+                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                              [80,3],# coarse textured, sand (soil type 3)
+                              [100,9],# coarse textured, sand (soil type 3)
+                            ]
+                    for iitex,iisoil in LOOKUP:
+                        ISOIL[ITEX > iitex] = iisoil
+                        print('iitex,iisoil',iitex,iisoil)
+
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    LOOKUP = [
+                              [9001, 1 ], # ice, glacier (soil type 1)
+                              [9002, 2 ], # rock, lithosols (soil type 2)
+                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                              [9,    9 ], # undefined (ocean)
+                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                              [9000, 9 ], # undefined (inland lake)
+                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                            ]
+                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
+
+                    CODE_VALUES[ITEX == 901200] = 9012
+                    for icode,iisoil in LOOKUP:
+                        ISOIL[CODE_VALUES == icode] = iisoil
+
+                    self.datasets['isoil']['isoil'].values = ISOIL
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+                    print('saved inbetween file to: '+fnoutkeytemp)
+
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                #adopted from data_soil.f90 (COSMO5.0)
+                SP_LOOKUP = {
+                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
+                  # (by index)                                           loam                    loam                                water      ice
+                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+                  # Important note: For peat, the unknown values below are set equal to that of loam
+                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
+                  #error in table 2 of NP89: values need to be multiplied by e-6
+                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
+
+                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
+                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
+                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
+                }
+
+
+                # isoil_reprocessed = False
+                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+
+                #     self.library[fn+':DSMW:isoil'] = \
+                #             book(fnoutkeytemp,debug_level=self.debug_level)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+                # else:
+                #     isoil_reprocessed = True
+                #     print('calculating soil type')
+                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+
+
+
+                # this should become cleaner in future but let's hard code it for now.
+                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
+                print('calculating soil parameter')
+                DATATEMPSPKEY = {}
+                if (recalc < 1) and (isoil_reprocessed == False): 
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        keytemp = SPKEY
+                        fnoutkeytemp=fnout+':DSMW:'+keytemp
+                        self.library[fn+':DSMW:'+SPKEY] =\
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
+                        self.datarefs[SPKEY] =fnoutkeytemp
+                else:
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+
+                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
+                    ISOIL = self.datasets['isoil'].page['isoil'].values
+                    print(np.where(ISOIL>0.))
+                    for i in range(11):
+                        SELECT = (ISOIL == i)
+                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
+
+                        os.system('rm '+fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].close()
+                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
+
+                        self.library[fn+':DSMW:'+SPKEY] = \
+                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+
+
+            else:
+                self.load_dataset_default(fn,varsource,vardest)
+
+
+
+
+
+
+#
+#                 # only print the last parameter value in the plot
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'cala'
+#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'crhoc'
+#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#     key = "CERES"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         CERES_start_date = dt.datetime(2000,3,1)
+#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+#
+#         var = 'cc'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#         print(class_settings.lat,class_settings.lon)
+#
+#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
+#
+#         input_nc.close()
+#
+
+
+#     key = "GIMMS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+#         print("Reading Leag Area Index from "+input_fn)
+#         var = 'LAI'
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+#
+#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+#
+#         if np.isnan(tarray[idatetime]):
+#             print("interpolating GIMMS cveg nan value")
+#
+#             mask = np.isnan(tarray)
+#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+#             else:
+#                 print("Warning. Could not interpolate GIMMS cveg nan value")
+#
+#         class_settings.__dict__[var] = tarray[idatetime]
+#
+#         input_nc.close()
+#
+#     key = "IGBPDIS_ALPHA"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         var = 'alpha'
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+#         print("Reading albedo from "+input_fn)
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#
+#         landfr = {}
+#         for ltype in ['W','B','H','TC']:
+#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+#
+#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+#
+#         alpha=0.
+#         for ltype in landfr.keys():
+#             alpha += landfr[ltype]*aweights[ltype]
+#
+#
+#         class_settings.__dict__[var] = alpha
+#         input_nc.close()
+#
+#
+#     key = "ERAINT_ST"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         print("Reading soil temperature from "+input_fn)
+#
+#         var = 'Tsoil'
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         var = 'T2'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+#
+#
+#         input_nc.close()
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #var = 'T2'
+#     #valold = class_settings.__dict__[var]
+#     #
+#     #class_settings.__dict__[var] = 305.
+#     #class_settings.__dict__['Tsoil'] = 302.
+#     #valnew = class_settings.__dict__[var]
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #
+#     #var = 'Lambda'
+#     #valold = class_settings.__dict__[var]
+#
+#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
+#     ## I need to ask Chiel.
+#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+#     #
+#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
+#     #class_settings.__dict__[var] = valnew
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     key = "GLAS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+#         print("Reading canopy height for determining roughness length from "+input_fn)
+#         var = 'z0m'
+#
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+#
+#         lowerlimit = 0.01
+#         if testval < lowerlimit:
+#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+#             class_settings.__dict__[var] = lowerlimit
+#         else:
+#             class_settings.__dict__[var] = testval
+#
+#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+#
+#
+#         input_nc.close()
+
+
+
+
+
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
new file mode 100644
index 0000000..3e483f3
--- /dev/null
+++ b/class4gl/interface_functions.py
@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+#from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+#'_afternoon.yaml'
+def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+    filename = yaml_file.name
+    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+    #yaml_file = open(filename)
+
+    #print('going to next observation',filename)
+    yaml_file.seek(index_start)
+
+    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+
+    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer.write(buf)
+    filebuffer.close()
+    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+
+    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    print(command)
+    os.system(command)
+    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    record_dict = json.load(jsonstream)
+    jsonstream.close()
+    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+
+
+    if mode =='mod':
+        modelout = class4gl()
+        modelout.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        return modelout
+    elif mode == 'ini':
+
+ 
+        # datetimes are incorrectly converted to strings. We need to convert them
+        # again to datetimes
+        for key,value in record_dict['pars'].items():
+            # we don't want the key with columns that have none values
+            if value is not None: 
+                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+               # elif (type(value) == str):
+                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
+            if (value == 0.9e19) or (value == '.9e19'):
+                record_dict['pars'][key] = np.nan
+        for key in record_dict.keys():
+            #print(key)
+            if key in ['air_ap','air_balloon',]:
+                #NNprint('check')
+                for datakey,datavalue in record_dict[key].items():
+                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+
+        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        c4gli = class4gl_input()
+        print(c4gli.logger,'hello')
+        c4gli.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+
+class stations(object):
+    def __init__(self,path,suffix='ini',refetch_stations=False):
+
+        self.path = path
+
+        self.file = self.path+'/stations_list.csv'
+        if (os.path.isfile(self.file)) and (not refetch_stations):
+            self.table = pd.read_csv(self.file)
+        else:
+            self.table = self.get_stations(suffix=suffix)
+            self.table.to_csv(self.file)
+        
+        self.table = self.table.set_index('STNID')
+        #print(self.table)
+
+    def get_stations(self,suffix):
+        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        if len(stations_list_files) == 0:
+            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        stations_list_files.sort()
+        print(stations_list_files)
+        if len(stations_list_files) == 0:
+            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
+        stations_list = []
+        for stations_list_file in stations_list_files:
+            thisfile = open(stations_list_file,'r')
+            yamlgen = yaml.load_all(thisfile)
+            try:
+                first_record  = yamlgen.__next__()
+            except:
+                first_record = None
+            if first_record is not None:
+                stations_list.append({})
+                for column in ['STNID','latitude','longitude']:
+                    #print(first_record['pars'].keys())
+                    stations_list[-1][column] = first_record['pars'][column]
+                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
+            yamlgen.close()
+            thisfile.close()
+    
+        print(stations_list)
+        return pd.DataFrame(stations_list)
+
+class stations_iterator(object):
+    def __init__(self,stations):
+        self.stations = stations
+        self.ix = -1 
+    def __iter__(self):
+        return self
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.stations.table)) 
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_row(self,row):
+        self.ix = row
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_STNID(self,STNID):
+        self.ix = np.where((self.stations.table.index == STNID))[0][0]
+        print(self.ix)
+        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+    def close():
+        del(self.ix)
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.records))
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+# #'_afternoon.yaml'
+# def get_record_yaml(yaml_file,index_start,index_end):
+#     filename = yaml_file.name
+#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+#     #yaml_file = open(filename)
+# 
+#     #print('going to next observation',filename)
+#     yaml_file.seek(index_start)
+# 
+#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+# 
+#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+#     filebuffer.write(buf)
+#     filebuffer.close()
+#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+#     
+#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+# 
+#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+#     print(command)
+#     os.system(command)
+#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
+#     record_dict = json.load(jsonstream)
+#     jsonstream.close()
+#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+#  
+#     # datetimes are incorrectly converted to strings. We need to convert them
+#     # again to datetimes
+#     for key,value in record_dict['pars'].items():
+#         # we don't want the key with columns that have none values
+#         if value is not None: 
+#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
+#            # elif (type(value) == str):
+#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+#                 
+#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
+# 
+#         if (value == 0.9e19) or (value == '.9e19'):
+#             record_dict['pars'][key] = np.nan
+#     for key in record_dict.keys():
+#         print(key)
+#         if key in ['air_ap','air_balloon',]:
+#             print('check')
+#             for datakey,datavalue in record_dict[key].items():
+#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+# 
+#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+# 
+#     c4gli = class4gl_input()
+#     c4gli.load_yaml_dict(record_dict)
+#     return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
+
+    records = pd.DataFrame()
+    for STNID,station in stations.iterrows():
+        dictfnchunks = []
+        if getchunk is 'all':
+
+            # we try the old single-chunk filename format first (usually for
+            # original profile pairs)
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(fn):
+                chunk = 0
+                dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+            # otherwise, we use the new multi-chunk filename format
+            else:
+                chunk = 0
+                end_of_chunks = False
+                while not end_of_chunks:
+                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(fn):
+                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    else:
+                        end_of_chunks = True
+                    chunk += 1
+
+            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
+            # yamlfilenames = glob.glob(globyamlfilenames)
+            # yamlfilenames.sort()
+        else:
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
+            
+        if len(dictfnchunks) > 0:
+            for dictfnchunk in dictfnchunks:
+                yamlfilename = dictfnchunk['fn']
+                chunk = dictfnchunk['chunk']
+                print(chunk)
+
+                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                generate_pkl = False
+                if not os.path.isfile(pklfilename): 
+                    print('pkl file does not exist. I generate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                elif not (os.path.getmtime(yamlfilename) <  \
+                    os.path.getmtime(pklfilename)):
+                    print('pkl file older than yaml file, so I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+
+                if refetch_records:
+                    print('refetch_records flag is True. I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                if not generate_pkl:
+                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                   # irecord = 0
+                else:
+                    with open(yamlfilename) as yaml_file:
+
+                        dictout = {}
+
+                        next_record_found = False
+                        end_of_file = False
+                        while (not next_record_found) and (not end_of_file):
+                            linebuffer = yaml_file.readline()
+                            next_record_found = (linebuffer == '---\n')
+                            end_of_file = (linebuffer == '')
+                        next_tell = yaml_file.tell()
+                        
+                        while not end_of_file:
+
+                            print(' next record:',next_tell)
+                            current_tell = next_tell
+                            next_record_found = False
+                            yaml_file.seek(current_tell)
+                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            linebuffer = ''
+                            while ( (not next_record_found) and (not end_of_file)):
+                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                linebuffer = yaml_file.readline()
+                                next_record_found = (linebuffer == '---\n')
+                                end_of_file = (linebuffer == '')
+                            filebuffer.close()
+                            
+                            next_tell = yaml_file.tell()
+                            index_start = current_tell
+                            index_end = next_tell
+
+                            
+                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            print(command)
+                            
+                            os.system(command)
+                            #jsonoutput = subprocess.check_output(command,shell=True) 
+                            #print(jsonoutput)
+                            #jsonstream = io.StringIO(jsonoutput)
+                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            record = json.load(jsonstream)
+                            dictouttemp = {}
+                            for key,value in record['pars'].items():
+                                # we don't want the key with columns that have none values
+                                if value is not None: 
+                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                   if (type(value) in regular_numeric_types):
+                                        dictouttemp[key] = value
+                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                            recordindex = record['index']
+                            dictouttemp['chunk'] = chunk
+                            dictouttemp['index_start'] = index_start
+                            dictouttemp['index_end'] = index_end
+                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            for key,value in dictouttemp.items():
+                                if key not in dictout.keys():
+                                    dictout[key] = {}
+                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                            print(' obs record registered')
+                            jsonstream.close()
+                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                    records_station = pd.DataFrame.from_dict(dictout)
+                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
+                    print('writing table file ('+pklfilename+') for station '\
+                          +str(STNID))
+                    records_station.to_pickle(pklfilename)
+                    # else:
+                    #     os.system('rm '+pklfilename)
+                    records = pd.concat([records,records_station])
+    return records
+
+def stdrel(mod,obs,columns):
+    stdrel = pd.DataFrame(columns = columns)
+    for column in columns:
+        stdrel[column] = \
+                (mod.groupby('STNID')[column].transform('mean') -
+                 obs.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') + \
+                (mod[column] -
+                 mod.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') 
+    return stdrel
+
+def pct(obs,columns):
+    pct = pd.DataFrame(columns=columns)
+    for column in columns:
+        #print(column)
+        pct[column] = ""
+        pct[column] = obs[column].rank(pct=True)
+    return pct
+
+def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (obs_afternoon.ldatetime - \
+                               obs_morning.ldatetime).dt.seconds*3600.
+    return stats
+
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
new file mode 100644
index 0000000..83148e5
--- /dev/null
+++ b/class4gl/interface_multi.py
@@ -0,0 +1,2061 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+# from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+cdictpres = {'blue': (\
+                   (0.,    0.,  0.),
+                   (0.25,  0.25, 0.25),
+                   (0.5,  .70, 0.70),
+                   (0.75, 1.0, 1.0),
+                   (1,     1.,  1.),
+                   ),
+       'green': (\
+                   (0. ,   0., 0.0),
+                   (0.25,  0.50, 0.50),
+                   (0.5,  .70, 0.70),
+                   (0.75,  0.50, 0.50),
+                   (1  ,    0,  0.),
+                   ),
+       'red':  (\
+                  (0 ,  1.0, 1.0),
+                  (0.25 ,  1.0, 1.0),
+                   (0.5,  .70, 0.70),
+                  (0.75 , 0.25, 0.25),
+                  (1,    0., 0.),
+                  )}
+
+statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+os.system('module load Ruby')
+
+class c4gl_interface_soundings(object):
+    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+        """ creates an interactive interface for analysing class4gl experiments
+
+        INPUT:
+            path_exp : path of the experiment output
+            path_obs : path of the observations 
+            globaldata: global data that is being shown on the map
+            refetch_stations: do we need to build the list of the stations again?
+        OUTPUT:
+            the procedure returns an interface object with interactive plots
+
+        """
+        
+        # set the ground
+        self.globaldata = globaldata
+
+ 
+        self.path_exp = path_exp
+        self.path_obs = path_obs
+        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
+
+        # # get the list of stations
+        # stationsfile = self.path_exp+'/stations_list.csv'
+        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
+        #     stations = pd.read_csv(stationsfile)
+        # else:
+        #     stations = get_stations(self.path_exp)
+        #     stations.to_csv(stationsfile)
+
+        # stations = stations.set_index('STNID')
+
+        self.frames = {}
+
+        self.frames['stats'] = {}
+        self.frames['worldmap'] = {}
+                
+        self.frames['profiles'] = {}
+        self.frames['profiles'] = {}
+        self.frames['profiles']['DT'] = None
+        self.frames['profiles']['STNID'] = None
+
+        #self.frames['worldmap']['stationsfile'] = stationsfile
+        self.frames['worldmap']['stations'] = stations(self.path_exp, \
+                                                       suffix='ini',\
+                                                       refetch_stations=refetch_stations)
+
+        # Initially, the stats frame inherets the values/iterators of
+        # worldmap
+        for key in self.frames['worldmap'].keys():
+            self.frames['stats'][key] = self.frames['worldmap'][key]
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_ini'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='ini',\
+                                           refetch_records=refetch_records
+                                           )
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_mod'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='mod',\
+                                           refetch_records=refetch_records
+                                           )
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_obs,\
+                                           subset='afternoon',\
+                                           refetch_records=refetch_records
+                                           )
+
+        self.frames['stats']['records_all_stations_mod'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['records_all_stations_ini']['dates'] = \
+            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+
+
+        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+
+        self.frames['stats']['records_all_stations_obs_afternoon'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['viewkeys'] = ['h','theta','q']
+        print('Calculating table statistics')
+        self.frames['stats']['records_all_stations_mod_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_mod'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+
+        self.frames['stats']['inputkeys'] = inputkeys
+        
+        # self.frames['stats']['inputkeys'] = \
+        #     [ key for key in \
+        #       self.globaldata.datasets.keys() \
+        #       if key in \
+        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
+
+
+        # get units from the class4gl units database
+        self.units = dict(units)
+        # for those that don't have a definition yet, we just ask a question
+        # mark
+        for var in self.frames['stats']['inputkeys']:
+            self.units[var] = '?'
+
+        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
+        self.frames['stats']['records_all_stations_ini_pct'] = \
+                  pct(self.frames['stats']['records_all_stations_ini'], \
+                      columns = self.frames['stats']['inputkeys'])
+
+        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
+        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+        #     mod['
+
+        # 
+        # 
+        # \
+        #        self.frames['stats']['records_all_stations_mod'], \
+
+
+
+        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        #               columns = [ 'd'+key+'dt' for key in \
+        #                           self.frames['stats']['viewkeys']], \
+        #              )
+
+        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
+        #               obs = self.frames['stats']['records_all_stations_ini'], \
+        #               columns = self.frames['stats']['viewkeys'], \
+        #              )
+        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+        
+        print('filtering pathological data')
+        # some observational sounding still seem problematic, which needs to be
+        # investigated. In the meantime, we filter them
+        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+        # we filter ALL data frames!!!
+        for key in self.frames['stats'].keys():
+            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+               (self.frames['stats'][key].index.names == indextype):
+                self.frames['stats'][key] = self.frames['stats'][key][valid]
+        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+
+
+        print("filtering stations from interface that have no records")
+        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
+            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                    == STNID).sum() == 0):
+                print("dropping", STNID)
+                self.frames['worldmap']['stations'].table = \
+                        self.frames['worldmap']['stations'].table.drop(STNID)
+                    
+        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+        
+        # TO TEST: should be removed, since it's is also done just below
+        self.frames['stats']['stations_iterator'] = \
+            self.frames['worldmap']['stations_iterator'] 
+
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
+        self.next_station()
+
+        # self.goto_datetime_worldmap(
+        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+        #     'after')
+    def sel_station(self,STNID=None,rownumber=None):
+
+        if (STNID is not None) and (rownumber is not None):
+            raise ValueError('Please provide either STNID or rownumber, not both.')
+
+        if (STNID is None) and (rownumber is None):
+            raise ValueError('Please provide either STNID or rownumber.')
+            
+        if STNID is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
+            print(
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+            )
+            self.update_station()
+        elif rownumber is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
+            self.update_station()
+
+
+
+    def next_station(self,event=None,jump=1):
+        with suppress(StopIteration):
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+                = self.frames['worldmap']['stations_iterator'].__next__(jump)
+            # self.frames['worldmap']['stations_iterator'].close()
+            # del(self.frames['worldmap']['stations_iterator'])
+            # self.frames['worldmap']['stations_iterator'] = \
+            #                 selfself.frames['worldmap']['stations'].iterrows()
+            # self.frames['worldmap']['STNID'],\
+            # self.frames['worldmap']['current_station'] \
+            #     = self.frames['worldmap']['stations_iterator'].__next__()
+
+        self.update_station()
+
+    def prev_station(self,event=None):
+        self.next_station(jump = -1,event=event)
+    def update_station(self):
+        for key in ['STNID','current_station','stations_iterator']: 
+            self.frames['stats'][key] = self.frames['worldmap'][key] 
+
+
+
+        # generate index of the current station
+        self.frames['stats']['records_current_station_index'] = \
+            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+             == \
+             self.frames['stats']['current_station'].name)
+
+        # create the value table of the records of the current station
+        tab_suffixes = \
+                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        for tab_suffix in tab_suffixes:
+            self.frames['stats']['records_current_station'+tab_suffix] = \
+                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+        # go to first record of current station
+        self.frames['stats']['records_iterator'] = \
+                        records_iterator(self.frames['stats']['records_current_station_mod'])
+        (self.frames['stats']['STNID'] , \
+        self.frames['stats']['current_record_chunk'] , \
+        self.frames['stats']['current_record_index']) , \
+        self.frames['stats']['current_record_mod'] = \
+                        self.frames['stats']['records_iterator'].__next__()
+
+        for key in self.frames['stats'].keys():
+            self.frames['profiles'][key] = self.frames['stats'][key]
+
+        STNID = self.frames['profiles']['STNID']
+        chunk = self.frames['profiles']['current_record_chunk']
+        if 'current_station_file_ini' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_ini'].close()
+        self.frames['profiles']['current_station_file_ini'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+        if 'current_station_file_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_mod'].close()
+        self.frames['profiles']['current_station_file_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_afternoon'].close()
+        self.frames['profiles']['current_station_file_afternoon'] = \
+            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+        self.frames['profiles']['records_iterator'] = \
+                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+        (self.frames['profiles']['STNID'] , \
+        self.frames['profiles']['current_record_chunk'] , \
+        self.frames['profiles']['current_record_index']) , \
+        self.frames['profiles']['current_record_mod'] = \
+                        self.frames['profiles']['records_iterator'].__next__()
+
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+
+        self.update_record()
+
+    def next_record(self,event=None,jump=1):
+        with suppress(StopIteration):
+            (self.frames['profiles']['STNID'] , \
+            self.frames['profiles']['current_record_chunk'] , \
+            self.frames['profiles']['current_record_index']) , \
+            self.frames['profiles']['current_record_mod'] = \
+                      self.frames['profiles']['records_iterator'].__next__(jump)
+        # except (StopIteration):
+        #     self.frames['profiles']['records_iterator'].close()
+        #     del( self.frames['profiles']['records_iterator'])
+        #     self.frames['profiles']['records_iterator'] = \
+        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #     (self.frames['profiles']['STNID'] , \
+        #     self.frames['profiles']['current_record_index']) , \
+        #     self.frames['profiles']['current_record_mod'] = \
+        #                     self.frames['profiles']['records_iterator'].__next__()
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        self.update_record()
+
+    def prev_record(self,event=None):
+        self.next_record(jump=-1,event=event)
+
+    def update_record(self):
+        self.frames['profiles']['current_record_ini'] =  \
+            self.frames['profiles']['records_current_station_ini'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'],\
+                  self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon'] =  \
+            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'] , \
+                  self.frames['profiles']['current_record_index'])]
+
+        self.frames['profiles']['current_record_mod_stats'] = \
+                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                    self.frames['profiles']['STNID'], \
+                    self.frames['profiles']['current_record_chunk'], \
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_ini_pct'] = \
+                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        # frame
+        # note that the current station, record is the same as the stats frame for initialization
+
+        # select first 
+        #self.frames['profiles']['current_record_index'], \
+        #self.frames['profiles']['record_yaml_mod'] = \
+        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
+        #                   self.frames['stats']['current_record_index'])
+        self.frames['profiles']['record_yaml_mod'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_mod'], \
+               self.frames['profiles']['current_record_mod'].index_start,
+               self.frames['profiles']['current_record_mod'].index_end,
+               mode='mod')
+                                
+        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_ini'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_ini'], \
+               record_ini.index_start,
+               record_ini.index_end,
+                mode='ini')
+
+        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_obs_afternoon'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_afternoon'], \
+               record_afternoon.index_start,
+               record_afternoon.index_end,
+                mode='ini')
+
+
+        key = self.frames['worldmap']['inputkey']
+        # only redraw the map if the current world map has a time
+        # dimension
+        if 'time' in self.globaldata.datasets[key].page[key].dims:
+            self.goto_datetime_worldmap(
+                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                'after')
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap',
+                                                  'profiles'])
+        else:
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap_stations',
+                                                  'profiles'])
+
+    def abline(self,slope, intercept,axis):
+        """Plot a line from slope and intercept"""
+        #axis = plt.gca()
+        x_vals = np.array(axis.get_xlim())
+        y_vals = intercept + slope * x_vals
+        axis.plot(x_vals, y_vals, 'k--')
+
+    def plot(self):
+        import pylab as pl
+        from matplotlib.widgets import Button
+        import matplotlib.pyplot as plt
+        import matplotlib as mpl
+        '''
+        Definition of the axes for the sounding table stats
+        '''
+        
+        fig = pl.figure(figsize=(14,9))
+        axes = {} #axes
+        btns = {} #buttons
+
+        # frames, which sets attributes for a group of axes, buttens, 
+        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            label = 'stats_'+str(key)
+            axes[label] = fig.add_subplot(\
+                            len(self.frames['stats']['viewkeys']),\
+                            5,\
+                            5*ikey+1,label=label)
+            # Actually, the axes should be a part of the frame!
+            #self.frames['stats']['axes'] = axes[
+
+            # pointer to the axes' point data
+            axes[label].data = {}
+
+            # pointer to the axes' color fields
+            axes[label].fields = {}
+
+
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
+
+        label ='stats_colorbar'
+        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
+        axes[label].fields = {}
+
+        from matplotlib.colors import LinearSegmentedColormap
+        cdictpres = {'blue': (\
+                           (0.,    0.,  0.),
+                           (0.25,  0.25, 0.25),
+                           (0.5,  .70, 0.70),
+                           (0.75, 1.0, 1.0),
+                           (1,     1.,  1.),
+                           ),
+               'green': (\
+                           (0. ,   0., 0.0),
+                           (0.25,  0.50, 0.50),
+                           (0.5,  .70, 0.70),
+                           (0.75,  0.50, 0.50),
+                           (1  ,    0,  0.),
+                           ),
+               'red':  (\
+                          (0 ,  1.0, 1.0),
+                          (0.25 ,  1.0, 1.0),
+                           (0.5,  .70, 0.70),
+                          (0.75 , 0.25, 0.25),
+                          (1,    0., 0.),
+                          )}
+        
+        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+        label = 'times'
+               
+        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+
+
+        label = 'worldmap'
+               
+        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+        axes[label].lat = None
+        axes[label].lon = None
+
+        label = 'worldmap_colorbar'
+        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+        axes[label].fields = {}
+
+        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
+        label = 'worldmap_stations'
+        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label].data = {}
+
+        fig.canvas.mpl_connect('pick_event', self.on_pick)
+        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
+
+
+        """ buttons definitions """
+        
+        label = 'bprev_dataset'
+        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous dataset')
+        btns[label].on_clicked(self.prev_dataset)
+
+        label = 'bnext_dataset'
+        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next dataset')
+        btns[label].on_clicked(self.next_dataset)
+
+        label = 'bprev_datetime'
+        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous datetime')
+        btns[label].on_clicked(self.prev_datetime)
+
+        label = 'bnext_datetime'
+        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next datetime')
+        btns[label].on_clicked(self.next_datetime)
+
+
+        label = 'bprev_station'
+        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous station')
+        btns[label].on_clicked(self.prev_station)
+
+        label = 'bnext_station'
+        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next station')
+        btns[label].on_clicked(self.next_station)
+
+        label = 'bprev_record'
+        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous record')
+        btns[label].on_clicked(self.prev_record)
+
+        label = 'bnext_record'
+        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next record')
+        btns[label].on_clicked(self.next_record)
+
+
+        # self.nstatsview = nstatsview
+        # self.statsviewcmap = statsviewcmap
+        self.fig = fig
+        self.axes = axes
+        self.btns = btns
+        self.tbox = {}
+        # self.hover_active = False
+
+        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
+        #                                transform=plt.gcf().transFigure)
+
+        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
+                                          transform=plt.gcf().transFigure)
+
+        label = 'air_ap:theta'
+        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
+
+        label = 'air_ap:q'
+        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
+
+        label = 'out:h'
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+
+        label = 'out:theta'
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+
+        label = 'out:q'
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+
+        label = 'SEB'
+        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
+
+
+        self.hover_active = False
+        self.fig = fig
+        self.fig.show()
+        self.fig.canvas.draw()
+        self.refresh_plot_interface()
+
+
+    # def scan_stations(self):
+    #     blabla
+        
+
+
+    # def get_records(current_file):
+    #     records = pd.DataFrame()
+
+    #     # initial position
+    #     next_record_found = False
+    #     while(not next_record_found):
+    #         next_record_found = (current_file.readline() == '---\n')
+    #     next_tell = current_file.tell() 
+    #     end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #     while not end_of_file:
+    #         current_tell = next_tell
+    #         next_record_found = False
+    #         current_file.seek(current_tell)
+    #         while ( (not next_record_found) and (not end_of_file)):
+    #             current_line = current_file.readline()
+    #             next_record_found = (currentline == '---\n')
+    #             end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #         # we store the position of the next record
+    #         next_tell = current_file.tell() 
+    #         
+    #         # we get the current record. Unfortunately we need to reset the
+    #         # yaml record generator first.
+    #         current_yamlgen.close()
+    #         current_yamlgen = yaml.load_all(current_file)
+    #         current_file.seek(current_tell)
+    #         current_record_mod = current_yamlgen.__next__()
+    #     current_yamlgen.close()
+
+    #     return records
+
+       #      next_record_found = False
+       #      while(not record):
+       #          next_record_found = (self.current_file.readline() == '---\n')
+       #      self.current_tell0 = self.current_file.tell() 
+
+       #  
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell0 = self.current_file.tell() 
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell1 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell0)
+       #  self.r0 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell1)
+       #  next_record_found = False
+       #  while ( (not next_record_found) and (not end_of_file):
+       #      current_line = self.current_file.readline()
+       #      next_record_found = (currentline == '---\n')
+       #      end_of_file = (currentline == '') # an empty line means we are at the end
+
+       #  self.current_tell2 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell1)
+       #  self.r1 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell2)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell3 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell2)
+       #  self.r2 = self.current_yamlgen.__next__()
+
+       #  # go to position of next record in file
+       #  self.current_file.seek(self.current_tell3)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell4 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell3)
+       #  self.r3 = self.current_yamlgen.__next__()
+ 
+       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
+
+    def goto_datetime_worldmap(self,DT,shift=None):
+        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                self.frames['worldmap']['iDT'] += 1
+            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                self.frames['worldmap']['iDT'] -= 1 
+            # for gleam, we take the values of the previous day
+            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                self.frames['worldmap']['iDT'] -= 2 
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+        #else:
+        #    self.frames['worldmap'].pop('DT')
+
+    def next_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def prev_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def next_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+
+
+    def sel_dataset(self,inputkey):
+        self.frames['worldmap']['inputkey'] = inputkey
+        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
+        self.goto_datetime_worldmap(
+            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+            'after')# get nearest datetime of the current dataset to the profile
+        if "fig" in self.__dict__.keys():
+            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+       
+    # def prev_station(self,event=None):
+    #     self.istation = (self.istation - 1) % self.stations.shape[0]
+    #     self.update_station()
+
+
+
+
+    #def update_datetime(self):
+    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
+    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
+    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
+    #        print(self.worldmapfocus['DT'])
+    #        self.refresh_plot_interface(only='worldmap')
+
+    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
+
+        #print('r1')
+        for argkey in args.keys():
+            self.__dict__[arg] = args[argkey]
+
+        axes = self.axes
+        tbox = self.tbox
+        frames = self.frames
+        fig = self.fig
+ 
+        if (only is None) or ('worldmap' in only):
+            globaldata = self.globaldata
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+            else:
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+            keystotranspose = ['lat','lon']
+            for key in dict(datasetxr.dims).keys():
+                if key not in keystotranspose:
+                    keystotranspose.append(key)
+
+            datasetxr = datasetxr.transpose(*keystotranspose)
+            datasetxr = datasetxr.sortby('lat',ascending=False)
+
+            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+            lonleft = lonleft - 360.
+            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+            label = 'worldmap'
+            axes[label].clear()
+            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+
+        if (only is None) or ('worldmap' in only):
+            #if 'axmap' not in self.__dict__ :
+            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
+            #else:
+
+            #stations = self.stations
+
+
+            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
+            #     resolution = 'l', 
+            # area_thresh = 0.1,
+            #     llcrnrlon=-180., llcrnrlat=-90.0,
+            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
+            # 
+            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
+            # self.gmap.drawcountries(color='white',linewidth=0.3)
+            # #self.gmap.fillcontinents(color = 'gray')
+            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
+            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
+            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
+            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # #self.ax5.shadedrelief()
+
+           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
+
+
+            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+
+            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
+            if 'lev' in field.dims:
+                field = field.isel(lev=-1)
+
+            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+            axes[label].axis('off')
+
+            from matplotlib import cm
+            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+            
+            
+            title=frames['worldmap']['inputkey']
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            axes[label].set_title(title)
+
+            label ='worldmap_colorbar'
+            axes[label].clear()
+            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+
+
+            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+            # x,y = self.gmap(lons,lats)
+            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+
+        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+
+            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            store_xlim = {}
+            store_ylim = {}
+            for ikey, key in enumerate(statskeys_out):
+                if (only is not None) and ('stats_lightupdate' in only):
+                    store_xlim[key] = axes['stats_'+key].get_xlim()
+                    store_ylim[key] = axes['stats_'+key].get_ylim()
+                self.axes['stats_'+key].clear()    
+
+            label = 'times'
+            self.axes[label].clear()
+
+            key = 'dthetadt'
+            x = self.frames['stats']['records_all_stations_ini']['datetime']
+            #print(x)
+            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            #print(y)
+            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            #print(z)
+
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            self.axes[label].data[label] = self.axes[label].scatter(x.values,
+                                                                    y.values,
+                                                                    c=z.values,
+                                                                    cmap=self.statsviewcmap,
+                                                                    s=2,
+                                                                    vmin=0.,
+                                                                    vmax=1.,
+                                                                    alpha=alpha_cloud_pixels)
+
+            
+            x = self.frames['stats']['records_current_station_ini']['datetime']
+            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+
+            x = self.frames['profiles']['records_current_station_ini']['datetime']
+            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
+            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
+
+            for ikey, key in enumerate(statskeys_out):
+
+                # show data of all stations
+                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                qvalmax = x.quantile(0.999)
+                qvalmin = x.quantile(0.001)
+                print('applying extra filter over extreme values for plotting stats')
+                selx = (x >= qvalmin) & (x < qvalmax)
+                sely = (x >= qvalmin) & (x < qvalmax)
+                x = x[selx & sely]
+                y = y[selx & sely]
+                z = z[selx & sely]
+                self.axes['stats_'+key].data['stats_'+key] = \
+                       self.axes['stats_'+key].scatter(x,y, c=z,\
+                                cmap=self.statsviewcmap,\
+                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
+
+                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
+
+                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
+                y = self.frames['stats']['current_record_mod_stats'][key]
+                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
+                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
+                    axes['stats_'+key].annotate(text, \
+                                               xy=(x,y),\
+                                               xytext=(0.05,0.05),\
+                                               textcoords='axes fraction',\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               color='white',\
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                # self.axes['stats_'+key].data[key+'_current_record'] = \
+                #        self.axes['stats_'+key].scatter(x,y, c=z,\
+                #                 cmap=self.statsviewcmap,\
+                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
+
+                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
+                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
+                # # highlight data for curent station
+                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+
+                if ikey == len(statskeys_out)-1:
+                    self.axes['stats_'+key].set_xlabel('external')
+                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
+                axes['stats_'+key].set_ylabel('model')
+
+
+                if (only is not None) and ('stats_lightupdate' in only):
+                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
+                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
+                else:
+                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
+                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
+                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
+                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
+                self.abline(1,0,axis=self.axes['stats_'+key])
+
+        if (only is None) or ('stats_colorbar' in only):
+            label ='stats_colorbar'
+            axes[label].clear()
+            import matplotlib as mpl
+            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
+            self.axes[label].fields[label] = \
+             mpl.colorbar.ColorbarBase(self.axes[label],\
+                        orientation='horizontal',\
+                        label="percentile of "+self.frames['worldmap']['inputkey'],
+                        alpha=1.,
+                                cmap=self.statsviewcmap,\
+                                       norm=norm
+                         )
+
+        #print('r1')
+        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
+            #print('r2')
+            label = 'worldmap_stations'
+            axes[label].clear()
+            
+            stations = self.frames['worldmap']['stations'].table
+            globaldata = self.globaldata
+            
+            key = label
+
+            #print('r3')
+            if (stations is not None):
+                xlist = []
+                ylist = []
+                #print('r4')
+                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
+            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    xlist.append(x)
+                    ylist.append(y)
+                #picker is needed to make it clickable (pick_event)
+                axes[label].data[label] = axes[label].scatter(xlist,ylist,
+                                                              c='r', s=15,
+                                                              picker = 15,
+                                                              label=key,
+                                                              edgecolor='k',
+                                                              linewidth=0.8)
+
+            # cb.set_label('Wilting point [kg kg-3]')
+                #print('r5')
+
+                
+            #     xseries = []
+            #     yseries = []
+            #     for iSTN,STN in stations.iterrows():
+            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
+            #         xseries.append(x)                    
+            #         yseries.append(y)
+            #         
+            #         
+            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
+                    
+                if ('current_station' in frames['worldmap']):
+                    #print('r5')
+                    STN = frames['stats']['current_station']
+                    STNID = frames['stats']['STNID']
+                    #print('r5')
+
+                    x,y = len(axes['worldmap'].lon)* \
+                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
+                          len(axes['worldmap'].lat)* \
+                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    #print('r6')
+                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
+                    #                          == \
+                    #                          self.frames['worldmap']['STNID'])\
+                    #                         & \
+                    #                         (self.seltablestats['DT'] \
+                    #                          == self.axes['statsview0].focus['DT']) \
+                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
+                    #print('r7')
+                    text = 'STNID: '+ format(STNID,'10.0f') + \
+                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
+                            ', LON: '+format(STN['longitude'],'3.3f')+ \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+
+                            #+', VAL: '+format(VAL,'.3e')
+
+                    axes[label].scatter(x,y, c='r', s=30,\
+                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
+                    #print('r8')
+            
+                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
+                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
+                    #colorstation = max((min((1.,colorstation)),0.))
+                    colorstation =0.2
+                    from matplotlib import cm
+                    axes[label].annotate(text,
+                                         xy=(x,y),
+                                         xytext=(0.05,0.05),
+                                         textcoords='axes fraction', 
+                                         bbox=dict(boxstyle="round",
+                                         fc = cm.viridis(colorstation)),
+                                         arrowprops=dict(arrowstyle="->",
+                                                         linewidth=1.1),
+                                         color='white' if colorstation < 0.5 else 'black')
+                    #print('r9')
+
+                    # #pos = sc.get_offsets()[ind["ind"][0]]
+                    # 
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label].data[label+'statannotate'].set_text(text)
+                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
+                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
+            #print('r9')
+            axes[label].axis('off')
+            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
+            axes[label].set_ylim((len(axes['worldmap'].lat),0))
+            #print('r10')
+
+        if (only is None) or ('profiles' in only): 
+            #print('r11')
+
+            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
+            # # self.update_station(goto_first_sounding=False)
+            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
+            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
+            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
+            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
+
+            label = 'air_ap:theta'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # +\
+                # ' -> '+ \
+                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+            
+            
+            
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+            #print('r12')
+
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
+            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
+            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            #print('r13')
+            # 
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r14')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+
+            #print('r15')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+                          
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r16')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r17')
+            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            print(hmax)
+            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            if valid_mod:
+
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="mod "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+
+            #print('r18')
+            axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('theta [K]')
+
+            label = 'air_ap:q'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+
+            #print('r19')
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            if valid_mod:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            else:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            # 
+            #print('r20')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r21')
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            #print('r23')
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r24')
+            if valid_mod:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="fit ")#+\
+                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #+'LT')
+            #print('r25')
+            #axes[label].legend()
+
+            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            #axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('q [kg/kg]')
+
+            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+
+            # #pl.subplots_adjust(right=0.6)
+
+            # label = 'q_pro'
+            # axes[label].clear()
+
+            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
+            # 
+            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
+            # 
+            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
+
+            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
+            # #pl.subplots_adjust(right=0.6)
+            # axes[label].set_xlabel('specific humidity [kg/kg]')
+ 
+
+            #print('r26')
+            time = self.frames['profiles']['record_yaml_mod'].out.time
+            for ilabel,label in enumerate(['h','theta','q']):
+                axes["out:"+label].clear()
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].set_ylabel(label)
+                if ilabel == 2:
+                    axes["out:"+label].set_xlabel('local sun time [h]')
+                
+            #print('r27')
+            label = 'SEB'
+            axes[label].clear()
+            
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
+            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
+            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
+                
+            #print('r28')
+            
+            axes[label].legend()
+            
+            #         for ax in self.fig_timeseries_axes:
+#             ax.clear()
+#         
+#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
+#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
+#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
+#         #print(self.morning_sounding.c4gl.out.Swin)
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
+#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
+#         self.fig_timeseries_axes[3].legend()
+#         self.fig.canvas.draw()
+            
+
+
+
+
+
+
+        #self.ready()
+        #print('r29')
+        fig.canvas.draw()
+        #fig.show()
+
+        self.axes = axes
+        self.tbox = tbox
+        self.fig = fig
+
+    def on_pick(self,event):
+        #print("HELLO")
+        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
+        #self.axes['theta_pro'].clear()
+        #self.axes['q_pro'].clear()
+        
+
+        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
+        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
+        keys_to_axes = {}
+        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
+
+        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
+        keys_to_axes['worldmap'] = 'worldmap'
+        
+        axes = self.axes
+        #nstatsview = self.nstatsview
+        #statsviewcmap = self.statsviewcmap
+        stations = self.frames['worldmap']['stations'].table
+
+
+        #print("p1")
+        current = event
+        artist = event.artist
+        
+        selkey = artist.get_label()
+        
+        #print(keys_to_axes)
+        
+        label = keys_to_axes[selkey]
+        #print("HELLO",selkey,label)
+
+        # # Get to know in which axes we are
+        # label = None
+        # for axeskey in axes.keys():
+        #     if event.inaxes == axes[axeskey]:
+        #         label = axeskey
+        #         
+
+        # cont, pos = None, None
+        
+        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
+        ind = event.ind
+        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
+        d = axes[label].collections[0]
+        #d.set_offset_position('data')
+        xy = d.get_offsets()
+        x, y =  xy[:,0],xy[:,1]
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+
+        #print("p2")
+        if len(ind) > 0:
+            #print("p3")
+            pos = x[ind[0]], y[ind[0]]
+
+            #if label[:-1] == 'statsview':
+            #    #seltablestatsstdrel = self.seltablestatsstdrel
+            #    #seltablestatspct = self.seltablestatspct
+
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    
+            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
+            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+            #    
+            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
+            #el
+            if (label == 'worldmap') or (label == 'worldmap_stations'):
+                self.hover_active = False
+                if (self.frames['worldmap']['STNID'] !=
+                    self.frames['profiles']['STNID']):
+                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
+                # so we just need to perform update_station
+                    self.update_station()
+            elif (label[:5] == 'stats'):
+
+                self.hover_active = False
+                if (self.frames['stats']['STNID'] !=
+                self.frames['profiles']['STNID']) or \
+                   (self.frames['stats']['current_record_chunk'] != 
+                    self.frames['profiles']['current_record_chunk']) or \
+                   (self.frames['stats']['current_record_index'] != 
+                    self.frames['profiles']['current_record_index']):
+
+
+
+                    for key in ['STNID','current_station','stations_iterator']: 
+                        self.frames['worldmap'][key] = self.frames['stats'][key] 
+
+                    for key in self.frames['stats'].keys():
+                        self.frames['profiles'][key] = self.frames['stats'][key]
+
+                    STNID = self.frames['profiles']['STNID']
+                    chunk = self.frames['profiles']['current_record_chunk']
+                    if 'current_station_file_ini' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_ini'].close()
+                    self.frames['profiles']['current_station_file_ini'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+                    if 'current_station_file_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_mod'].close()
+                    self.frames['profiles']['current_station_file_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_afternoon'].close()
+                    self.frames['profiles']['current_station_file_afternoon'] = \
+                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+                    # go to hovered record of current station
+                    self.frames['profiles']['records_iterator'] = \
+                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # ... and go to the record of the profile window (last one that
+                    # was picked by the user)
+                    found = False
+                    EOF = False
+                    while (not found) and (not EOF):
+                        try:
+                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
+                            #print("hello*")
+                            #print(self.frames['profiles']['current_record_index'])
+                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
+                               (index == self.frames['profiles']['current_record_index']) and \
+                               (STNID == self.frames['profiles']['STNID']):
+                                #print('found!')
+                                found = True
+                        except StopIteration:
+                            EOF = True
+                    if found:
+                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_chunk'] = chunk
+                        self.frames['stats']['current_record_index'] = index
+                    # # for the profiles we make a distinct record iterator, so that the
+                    # # stats iterator can move independently
+                    # self.frames['profiles']['records_iterator'] = \
+                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # (self.frames['profiles']['STNID'] , \
+                    # self.frames['profiles']['current_record_index']) , \
+                    # self.frames['profiles']['current_record_mod'] = \
+                    #                 self.frames['profiles']['records_iterator'].__next__()
+
+
+                    # for the profiles we make a distinct record iterator, so that the
+                    # stats iterator can move independently
+
+                    self.update_record()
+
+
+
+    def on_plot_hover(self,event):
+        axes = self.axes
+        #print('h1')
+
+        # Get to know in which axes we are
+        label = None
+        for axeskey in axes.keys():
+            if event.inaxes == axes[axeskey]:
+                label = axeskey
+                
+        #print('h2')
+
+        cont, pos = None, None
+        #print (label)
+        
+        if label is not None:
+            if  ('data' in axes[label].__dict__.keys()) and \
+                (label in axes[label].data.keys()) and \
+                (axes[label].data[label] is not None):
+                
+                #print('h3')
+                cont, ind =  axes[label].data[label].contains(event)
+                selkey = axes[label].data[label].get_label()
+                if len(ind["ind"]) > 0:
+                    #print('h4')
+                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
+                    #print('pos',pos,selkey)
+
+
+                    #if label[:-1] == 'statsview':
+                    #    seltablestatsstdrel = self.seltablestatsstdrel
+                    #    seltablestatspct = self.seltablestatspct
+
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
+                    #    self.hover_active = True
+                    #    
+                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
+                    #    
+                    #el
+                    #print(label[:5])
+                    if (label[:5] == 'stats') or (label == 'times'):
+                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        
+
+                        if label[:5] == 'stats':
+                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            (self.frames['stats']['STNID'] ,
+                             self.frames['stats']['current_record_chunk'], 
+                             self.frames['stats']['current_record_index']) = \
+                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        # elif label[:5] == 'stats':
+                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                        #     (self.frames['stats']['STNID'] ,
+                        #      self.frames['stats']['current_record_chunk'], 
+                        #      self.frames['stats']['current_record_index']) = \
+                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+
+
+                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+                        
+                        # # TO TEST: should be removed, since it's is also done just below
+                        # self.frames['stats']['stations_iterator'] = \
+                        #     self.frames['worldmap']['stations_iterator'] 
+                
+                
+                        # self.goto_datetime_worldmap(
+                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+                        #     'after')
+
+
+                        # scrolling to the right station
+                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                        EOF = False
+                        found = False
+                        while (not found and not EOF):
+                            if (STNID == self.frames['stats']['STNID']):
+                                   found = True 
+                            if not found:
+                                try:
+                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                                except (StopIteration):
+                                    EOF = True
+                        if found:
+                        #    self.frames['stats']['STNID'] = STNID
+                            self.frames['stats']['current_station'] =  station
+
+                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
+                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
+
+
+                        # generate index of the current station
+                        self.frames['stats']['records_current_station_index'] = \
+                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                             == self.frames['stats']['STNID'])
+
+
+                        tab_suffixes = \
+                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            self.frames['stats']['records_current_station'+tab_suffix] = \
+                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+
+                        # go to hovered record of current station
+                        self.frames['stats']['records_iterator'] = \
+                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+
+
+                        # ... and go to the record of the profile window (last one that
+                        # was picked by the user)
+                        found = False
+                        EOF = False
+                        while (not found) and (not EOF):
+                            try:
+                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                                #print("hello*")
+                                #print(self.frames['profiles']['current_record_index'])
+                                if (index == self.frames['stats']['current_record_index']) and \
+                                   (chunk == self.frames['stats']['current_record_chunk']) and \
+                                   (STNID == self.frames['stats']['STNID']):
+                                    #print('found!')
+                                    found = True
+                            except StopIteration:
+                                EOF = True
+                        if found:
+                            #print('h5')
+                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_chunk'] = chunk
+                            self.frames['stats']['current_record_index'] = index
+
+                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
+                        tab_suffixes = \
+                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            #print(tab_suffix)
+                            #print(self.frames['stats']['records_current_station'+tab_suffix])
+                            self.frames['stats']['current_record'+tab_suffix] =  \
+                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                      (self.frames['stats']['STNID'] , \
+                                       self.frames['stats']['current_record_chunk'] , \
+                                       self.frames['stats']['current_record_index'])]
+
+
+                        self.hover_active = True
+                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                        # print('h13')
+                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
+                        #     self.goto_datetime_worldmap(
+                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                        #         'after')
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap',
+                        #                                           'profiles'])
+                        # else:
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap_stations',
+                        #                                           'profiles'])
+
+
+
+                    elif label in ['worldmap_stations','worldmap']:
+                        #print('h5')
+
+                        if (self.axes['worldmap'].lat is not None) and \
+                           (self.axes['worldmap'].lon is not None):
+
+
+                            #self.loading()
+                            self.fig.canvas.draw()
+                            self.fig.show()
+
+
+                            # get position of 
+                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
+                                                                 self.axes['worldmap'].lat[0]) + \
+                                           self.axes['worldmap'].lat[0],4)
+                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
+                                                                 self.axes['worldmap'].lon[0]) + \
+                                           self.axes['worldmap'].lon[0],4)
+                        
+                            stations = self.frames['worldmap']['stations'].table
+                            #print('h7')
+                        
+                            #reset stations iterator:
+                            # if 'stations_iterator' in self.frames['worldmap'].keys():
+                            #     self.frames['worldmap']['stations_iterator'].close()
+                            #     del(self.frames['worldmap']['stations_iterator'])
+                            # if 'stations_iterator' in self.frames['stats'].keys():
+                            #     self.frames['stats']['stations_iterator'].close()
+                            #     del(self.frames['stats']['stations_iterator'])
+                            self.frames['worldmap']['stations_iterator'] =\
+                               stations_iterator(self.frames['worldmap']['stations'])
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                            EOF = False
+                            found = False
+                            while (not found and not EOF):
+                                #print('h8',station.latitude,latmap)
+                                #print('h8',station.longitude,lonmap)
+                                if (round(station.latitude,3) == round(latmap,3)) and \
+                                    (round(station.longitude,3) == round(lonmap,3)):
+                                       found = True 
+                                if not found:
+                                    try:
+                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                                    except (StopIteration):
+                                        EOF = True
+                            if found:
+                                self.frames['worldmap']['STNID'] = STNID
+                                self.frames['worldmap']['current_station'] = \
+                                        station
+                        
+                            self.frames['stats']['stations_iterator'] = \
+                                self.frames['worldmap']['stations_iterator'] 
+                            #print('h8')
+                            # inherit station position for the stats frame...
+                            for key in self.frames['worldmap'].keys():
+                                self.frames['stats'][key] = self.frames['worldmap'][key]
+                                
+                            ## fetch records of current station...
+                            #self.frames['stats']['records_current_station_mod'] =\
+                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                            # ... and their indices
+                            self.frames['stats']['records_current_station_index'] = \
+                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                                     == \
+                                     self.frames['stats']['current_station'].name)
+
+
+                            tab_suffixes = \
+                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['records_current_station'+tab_suffix] = \
+                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+                            # ... create a record iterator ...
+                            #self.frames['stats']['records_iterator'].close()
+                            del(self.frames['stats']['records_iterator'])
+                            self.frames['stats']['records_iterator'] = \
+                                self.frames['stats']['records_current_station_mod'].iterrows()
+
+
+
+                        
+                            #print('h9')
+                            # ... and go to to the first record of the current station
+                            (self.frames['stats']['STNID'] , \
+                             self.frames['stats']['current_record_chunk'] , \
+                             self.frames['stats']['current_record_index']) , \
+                            self.frames['stats']['current_record_mod'] = \
+                                self.frames['stats']['records_iterator'].__next__()
+                        
+
+
+
+                            #print('h10')
+                            # cash the current record
+                            tab_suffixes = \
+                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['current_record'+tab_suffix] =  \
+                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                          (self.frames['stats']['STNID'] , \
+                                           self.frames['stats']['current_record_chunk'] , \
+                                           self.frames['stats']['current_record_index'])]
+
+                            #print('h11')
+                            
+                            self.hover_active = True
+                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                            #print('h13')
+
+                        
+
+            #if (stations is not None):
+            #    for iSTN,STN in stations.iterrows():
+            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
+            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
+
+        # self.fig.show()
+ 
+        # we are hovering on nothing, so we are going back to the position of
+        # the profile sounding
+        if pos is None:
+            if self.hover_active == True:
+                #print('h1*')
+                
+                #self.loading()
+                # to do: reset stations iterators
+
+                # get station and record index from the current profile
+                for key in ['STNID', 'current_station']:
+                    self.frames['stats'][key] = self.frames['profiles'][key]
+
+                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
+                self.frames['stats']['current_station'] = \
+                        self.frames['profiles']['current_station']
+                #print('h3a*')
+                self.frames['stats']['records_current_station_mod'] = \
+                        self.frames['profiles']['records_current_station_mod']
+                #print('h3b*')
+
+                # the next lines recreate the records iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+
+                # reset stations iterator...
+                #self.frames['stats']['records_iterator'].close()
+                del(self.frames['stats']['records_iterator'])
+                self.frames['stats']['records_iterator'] = \
+                    self.frames['stats']['records_current_station_mod'].iterrows()
+                #print('h4*')
+
+                # ... and go to the record of the profile window (last one that
+                # was picked by the user)
+                found = False
+                EOF = False
+                while (not found) and (not EOF):
+                    try:
+                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                        #print("hello*")
+                        #print(self.frames['profiles']['current_record_index'])
+                        #print(self.frames['profiles']['STNID'])
+                        #print(STNID,index)
+                        if (index == self.frames['profiles']['current_record_index']) and \
+                            (chunk == self.frames['profiles']['current_record_chunk']) and \
+                            (STNID == self.frames['profiles']['STNID']):
+                            #print('found!')
+                            found = True
+                    except StopIteration:
+                        EOF = True
+                if found:
+                    #print('h5*')
+                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_chunk'] = chunk
+                    self.frames['stats']['current_record_index'] = index
+
+                #print('h6*')
+
+
+
+                # # fetch records of current station...
+                # self.frames['stats']['records_current_station_mod'] =\
+                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                # ... and their indices
+                self.frames['stats']['records_current_station_index'] = \
+                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                         == \
+                         self.frames['stats']['current_station'].name)
+
+
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['records_current_station'+tab_suffix] = \
+                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+                
+
+                # cash the records of the current stations
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['current_record'+tab_suffix] =  \
+                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                              (self.frames['stats']['STNID'] , \
+                               self.frames['stats']['current_record_chunk'] , \
+                               self.frames['stats']['current_record_index'])]
+
+
+                # the next lines recreate the stations iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+                #print('h7*')
+
+                # reset the stations iterators
+                for framekey in ['stats','worldmap']:
+                    ##print(framekey)
+                    if 'stations_iterator' in self.frames[framekey]:
+                        #self.frames[framekey]['stations_iterator'].close()
+                        del(self.frames[framekey]['stations_iterator'])
+
+                self.frames['worldmap']['current_station'] = \
+                        self.frames['profiles']['current_station']
+
+                #recreate the stations iterator for the worldmap...
+                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+
+                # ... and go the position of the profile
+                #print('h8*')
+                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                EOF = False
+                found = False
+                while (not found and not EOF):
+                    if STNID == self.frames['profiles']['STNID'] :
+                        found = True 
+                    if not found:
+                        try:
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                        except (StopIteration):
+                            EOF = True
+                if found:
+                    self.frames['worldmap']['current_station'] = station
+                    self.frames['worldmap']['STNID'] = STNID
+                #print('h9*')
+                self.frames['stats']['stations_iterator'] = \
+                    self.frames['worldmap']['stations_iterator'] 
+
+                # the stats window now inherits the current station from the
+                # worldmap
+                for key in ['STNID','current_station','stations_iterator']: 
+                    self.frames['stats'][key] = self.frames['worldmap'][key] 
+                #print('h10*')
+
+                # # we now only need inherit station position and go to first record
+                # for key in self.frames['worldmap'].keys():
+                #     self.frames['stats'][key] = self.frames['worldmap'][key]
+
+                # self.frames['stats']['records_current_station'] =\
+                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
+
+                # #print(self.frames['stats']['records_current_station'])
+                # self.frames['stats']['records_iterator'] = \
+                #                 self.frames['stats']['records_current_station'].iterrows()
+                # (self.frames['stats']['STNID'] , \
+                # self.frames['stats']['current_record_index']) , \
+                # self.frames['stats']['current_record_mod'] = \
+                #                 self.frames['stats']['records_iterator'].__next__()
+                
+
+
+
+
+
+
+                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
+                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
+                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
+                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+                self.hover_active = False
+                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
+    # def loading(self):
+    #     self.tbox['loading'].set_text('Loading...')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+    #     sleep(0.1)
+    # def ready(self):
+    #     self.tbox['loading'].set_text('Ready')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+
+
+
diff --git a/class4gl/model.py b/class4gl/model.py
new file mode 100644
index 0000000..8760411
--- /dev/null
+++ b/class4gl/model.py
@@ -0,0 +1,2214 @@
+# 
+# CLASS
+# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
+# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
+# Copyright (c) 2011-2015 Chiel van Heerwaarden
+# Copyright (c) 2011-2015 Bart van Stratum
+# Copyright (c) 2011-2015 Kees van den Dries
+# 
+# This file is part of CLASS
+# 
+# CLASS is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published bygamma
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# CLASS is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with CLASS.  If not, see .
+#
+
+import copy as cp
+import numpy as np
+import sys
+import warnings
+import pandas as pd
+from ribtol_hw import zeta_hs2 , funcsche
+import logging
+#from SkewT.thermodynamics import Density
+#import ribtol
+
+grav = 9.81
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+
+def qsat(T,p):
+    return 0.622 * esat(T) / p
+
+
+def ribtol(Rib, zsl, z0m, z0h): 
+    Rib = np.float64(Rib)
+    zsl = np.float64(zsl)
+    z0m = np.float64(z0m)
+    z0h = np.float64(z0h)
+    #print(Rib,zsl,z0m,z0h)
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    #print(Rib,zsl,z0m,z0h)
+    while (abs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+        #print(L,fx/fxdif)
+        if(abs(L) > 1e12):
+            break
+
+    return L
+  
+def psim(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psim = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+  
+def psih(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * np.log( (1. + x*x) / 2.)
+        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+ 
+class model:
+    def __init__(self, model_input = None,debug_level=None):
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        self.logger = logging.getLogger('model')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        """ initialize the different components of the model """ 
+
+        if model_input is not None:
+            # class4gl style input
+            if 'pars' in model_input.__dict__.keys():
+
+                # we make a reference to the full input first, so we can dump it
+                # afterwards
+                self.input_c4gl = model_input
+
+                # we copy the regular parameters first. We keep the classical input
+                # format as self.input so that we don't have to change the entire
+                # model code.
+                self.input = cp.deepcopy(model_input.pars)
+
+                # we copy other sections we are interested in, such as profile
+                # data, and store it also under input
+
+                # I know we mess up a bit the structure of the class4gl_input, but
+                # we will make it clean again at the time of dumping data
+
+                # So here, we copy the profile data into self.input
+                # 1. Air circulation data 
+                if 'sw_ac' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ac']:
+                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
+                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
+
+                    # correct pressure of levels according to surface pressure
+                    # error (so that interpolation is done in a consistent way)
+
+                    p_e = self.input.Ps - self.input.sp
+                    for irow in self.input.air_ac.index[::-1]:
+                       self.input.air_ac.p.iloc[irow] =\
+                        self.input.air_ac.p.iloc[irow] + p_e
+                       p_e = p_e -\
+                       (self.input.air_ac.p.iloc[irow]+p_e)/\
+                        self.input.air_ac.p.iloc[irow] *\
+                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+
+
+
+                # 2. Air circulation data 
+                if 'sw_ap' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ap']:
+                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
+
+            # standard class input
+            else:
+                self.input = cp.deepcopy(model_input)
+
+    def load_yaml_dict(self,yaml_dict):
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                for keydata,value in data.items():
+                    self.__dict__[keydata] = value
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            #elif key == 'sources':
+            #    self.__dict__[key] = data
+            elif key == 'out':
+                # lets convert it to a list of dictionaries
+                dictouttemp = pd.DataFrame(data).to_dict('list')
+            else: 
+                 warnings.warn("Key '"+key+"' is be implemented.")
+            #     self.__dict__[key] = data
+
+
+        self.tsteps = len(dictouttemp['h'])
+        self.out = model_output(self.tsteps)
+        for keydictouttemp in dictouttemp.keys():
+            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+
+
+  
+    def run(self):
+        # initialize model variables
+        self.init()
+  
+        # time integrate model 
+        #for self.t in range(self.tsteps):
+        while self.t < self.tsteps:
+          
+            # time integrate components
+            self.timestep()
+  
+        # delete unnecessary variables from memory
+        self.exitmodel()
+    
+    def init(self):
+        # assign variables from input data
+        # initialize constants
+        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
+        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        self.rho        = 1.2                   # density of air [kg m-3]
+        self.k          = 0.4                   # Von Karman constant [-]
+        self.g          = 9.81                  # gravity acceleration [m s-2]
+        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+        self.bolz       = 5.67e-8               # Bolzman constant [-]
+        self.rhow       = 1000.                 # density of water [kg m-3]
+        self.S0         = 1368.                 # solar constant [W m-2]
+
+        # A-Gs constants and settings
+        # Plant type:       -C3-     -C4-
+        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
+        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
+        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
+        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
+        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
+        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
+        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
+        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
+        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
+        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
+        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
+
+        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
+        self.mair       =  28.9;                # molecular weight air [g mol -1]
+        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
+
+        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
+        self.wmax       =  0.55;                # upper reference value soil water [-]
+        self.wmin       =  0.005;               # lower reference value soil water [-]
+        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
+        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
+
+        # Read switches
+        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
+        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
+        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
+        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
+        self.sw_sl      = self.input.sw_sl      # surface layer switch
+        self.sw_rad     = self.input.sw_rad     # radiation switch
+        self.sw_ls      = self.input.sw_ls      # land surface switch
+        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
+        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
+
+        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
+        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
+        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+  
+        # initialize mixed-layer
+        self.h          = self.input.h          # initial ABL height [m]
+        self.Ps         = self.input.Ps         # surface pressure [Pa]
+        self.sp         = self.input.sp         # This is also surface pressure
+                                                #but derived from the global data [Pa]
+        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
+        self.ws         = None                  # large-scale vertical velocity [m s-1]
+        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
+        self.we         = -1.                   # entrainment velocity [m s-1]
+       
+         # Temperature 
+        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
+        
+        
+        self.substep    = False
+        self.substeps   = 0
+
+
+
+        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
+        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
+        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
+        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
+        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
+ 
+        self.wstar      = 0.                    # convective velocity scale [m s-1]
+ 
+        # 2m diagnostic variables 
+        self.T2m        = None                  # 2m temperature [K]
+        self.q2m        = None                  # 2m specific humidity [kg kg-1]
+        self.e2m        = None                  # 2m vapor pressure [Pa]
+        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
+        self.u2m        = None                  # 2m u-wind [m s-1]
+        self.v2m        = None                  # 2m v-wind [m s-1]
+ 
+        # Surface variables 
+        self.thetasurf  = self.input.theta      # surface potential temperature [K]
+        self.thetavsurf = None                  # surface virtual potential temperature [K]
+        self.qsurf      = None                  # surface specific humidity [g kg-1]
+
+        # Mixed-layer top variables
+        self.P_h        = None                  # Mixed-layer top pressure [pa]
+        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
+        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
+        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
+        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
+        self.dz_h       = None                  # Transition layer thickness [-]
+        self.lcl        = None                  # Lifting condensation level [m]
+
+        # Virtual temperatures and fluxes
+        self.thetav     = None                  # initial mixed-layer potential temperature [K]
+        self.dthetav    = None                  # initial virtual temperature jump at h [K]
+        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
+        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
+       
+        
+        
+        
+        
+        
+        # Moisture 
+        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
+
+        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
+        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
+        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
+  
+        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
+        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
+        self.e          = None                  # mixed-layer vapor pressure [Pa]
+        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
+        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
+      
+        
+        
+        # CO2
+        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
+        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
+        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
+        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
+        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
+        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
+        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
+        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
+        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
+        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
+       
+        # Wind 
+        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
+        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
+        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = self.input.advu       # advection of u-wind [m s-2]
+        
+        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
+        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = self.input.advv       # advection of v-wind [m s-2]
+         
+  # BEGIN -- HW 20170606
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        if self.sw_ac:
+        # this is the data frame with the grided profile on the L60 grid
+        # (subsidence, and advection) 
+            self.air_ac      = self.input.air_ac  # full level air circulation
+                                                  # forcing
+            # self.air_ach     = self.input.air_ach # half level air circulation
+            #                                       # forcing
+            
+
+        if self.sw_ap:
+        # this is the data frame with the fitted profile (including HAGL,
+        # THTA,WSPD, SNDU,WNDV PRES ...)
+            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
+
+            # just for legacy reasons...
+            if 'z' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
+            if 'p' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
+
+            indexh = np.where(self.air_ap.z.values == self.h)
+            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
+                raise ValueError("Error input profile consistency: mixed- \
+                                 layer height needs to be equal to the second \
+                                 and third \
+                                 level of the vertical profile input!")
+            # initialize q from its profile when available
+            p_old = self.Ps
+            p_new = self.air_ap.p[indexh[0][0]]
+            
+            if ((p_old is not None) & (p_old != p_new)):
+                print("Warning: Ps input was provided ("+str(p_old)+\
+                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+                    +str(p_new)+"Pa).")
+                                    
+            self.Ps = p_new
+            # these variables/namings are more convenient to work with in the code
+            # we will update the original variables afterwards
+            #self.air_ap['q'] = self.air_ap.QABS/1000.
+
+            self.air_ap = \
+                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
+            # we require the temperature fields, since we need to consider
+            # advection
+            # if self.sw_ac:
+            #     #self.air_ap['theta'] = self.air_ap['t'] *
+
+            #     # we consider self.sp in case of air-circulation input (for
+            #     # consistence)
+            #     self.air_ap['t'] = \
+            #                 self.air_ap.theta *  \
+            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
+            # else:
+            # we consider self.Ps in case of balloon input only 
+            self.air_ap = self.air_ap.assign(t = lambda x: \
+                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
+
+            #self.air_ap['theta'] = self.air_ap.THTA
+            if 'u' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
+            if 'v' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
+
+            for var in ['theta','q','u','v']:
+
+                
+                if self.air_ap[var][1] != self.air_ap[var][0]:
+                    raise ValueError("Error input profile consistency: two \
+                                     lowest profile levels for "+var+" should \
+                                     be equal.")
+                
+                # initialize the value from its profile when available
+                value_old = self.__dict__[var]
+                value_new = self.air_ap[var][indexh[0][0]]
+                
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided \
+                                     ("+str(value_old)+ "kg kg-1), \
+                                     but it is now overwritten by the first \
+                                     level (index 0) of air_ap]var\ which is \
+                                     different (" +str(value_new)+"K).")
+                                        
+                self.__dict__[var] = value_new
+
+                # make a profile of the stratification 
+                # please note that the stratification between z_pro[i] and
+                # z_pro[i+1] is given by air_ap.GTHT[i]
+
+                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
+                # np.gradient(self.z_pro)
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
+
+
+                self.__dict__['gamma'+var] = \
+                    self.air_ap['gamma'+var][np.where(self.h >= \
+                                                     self.air_ap.z)[0][-1]]
+
+
+
+        # the variable p_pro is just for diagnosis of lifted index
+            
+            
+
+            # input Ph is wrong, so we correct it according to hydrostatic equation
+            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+
+            #if self.sw_ac:
+                # note that we use sp as surface pressure, which is determined
+                # from era-interim instead of the observations. This is to
+                # avoid possible failure of the interpolation routine
+                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
+                #                          + \
+                #                          list(self.air_ap.p[3:]))
+
+            # else:
+                # in the other case, it is updated at the time of calculting
+                # the statistics 
+
+# END -- HW 20170606      
+        #print(self.air_ap)
+
+        if self.sw_ac and not self.sw_ap:
+            raise ValueError("air circulation switch only possible when air \
+                             profiles are given")
+        
+        if self.sw_ac:
+
+            # # # we comment this out, because subsidence is calculated
+            # according to advection
+            # #interpolate subsidence towards the air_ap height coordinate
+            # self.air_ap['w'] = np.interp(self.air_ap.p,\
+            #                               self.air_ac.p,\
+            #                               self.air_ac.w) 
+            # #subsidence at the mixed-layer top
+            # self.w = self.air_ap.w[1]
+        
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+                # in case we didn't find any points, we just take the lowest one.
+                # actually, this can happen if ERA-INTERIM pressure levels are
+                # inconsistent with 
+                if in_ml.sum() == 0:
+                    warnings.warn(" no circulation points in the mixed layer \
+                                  found. We just take the bottom one.")
+                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+
+                for var in ['t','q','u','v']:
+    
+                   # calculation of the advection variables for the mixed layer
+                   # we weight by the hydrostatic thickness of each layer and
+                   # divide by the total thickness
+                   self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+
+                   # calculation of the advection variables for the profile above
+                   # (lowest 3 values are not used by class)
+                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
+                   self.air_ap['adv'+var] = \
+                           np.interp(self.air_ap.p,\
+                                     self.air_ac.p,\
+                                     self.air_ac['adv'+var+'_x']) \
+                           + \
+                           np.interp(self.air_ap.p, \
+                                       self.air_ac.p, \
+                                       self.air_ac['adv'+var+'_y'])
+
+                # as an approximation, we consider that advection of theta in the
+                # mixed layer is equal to advection of t. This is a sufficient
+                # approximation since theta and t are very similar at the surface
+                # pressure.
+                self.__dict__['advtheta'] = self.__dict__['advt']
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # self.wrho = np.interp(self.P_h,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) 
+            # self.ws   = self.air_ap.w.iloc[1]
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
+                self.air_ap = self.air_ap.assign(R = 0.)
+                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                     self.Rv*self.air_ap.q)
+                self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+                
+                self.air_ap = self.air_ap.assign(w = 0.)
+                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+                #print('hello w ini')
+
+                # Note: in case of sw_ac is False, we update it from prescribed
+                # divergence
+                self.ws   = self.air_ap.w[1]
+
+                # self.ws   = self.wrho/self.rho
+                # self.ws   = self.wrho/(self.P_h/ \
+                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
+                #                         self.theta) # this should be T!!!
+
+                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+                #                         + \
+                #                         self.air_ac['divU_y'][in_ml])* \
+                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                #             self.air_ac['delpdgrav'][in_ml].sum() \
+        
+
+        # Tendencies 
+        self.htend      = None                  # tendency of CBL [m s-1]
+        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
+        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
+        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
+        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
+        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
+        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
+        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
+        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
+        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
+        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
+        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
+  
+        # initialize surface layer
+        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
+        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
+        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
+        self.z0m        = self.input.z0m        # roughness length for momentum [m]
+        self.z0h        = self.input.z0h        # roughness length for scalars [m]
+        self.Cm         = 1e12                  # drag coefficient for momentum [-]
+        self.Cs         = 1e12                  # drag coefficient for scalars [-]
+        self.L          = None                  # Obukhov length [m]
+        self.Rib        = None                  # bulk Richardson number [-]
+        self.ra         = None                  # aerodynamic resistance [s m-1]
+  
+        # initialize radiation
+        self.lat        = self.input.lat        # latitude [deg]
+        #self.fc         = self.input.fc         # coriolis parameter [s-1]
+        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
+        self.lon        = self.input.lon        # longitude [deg]
+        self.doy        = self.input.doy        # day of the year [-]
+        self.tstart     = self.input.tstart     # time of the day [-]
+        self.cc         = self.input.cc         # cloud cover fraction [-]
+        self.Swin       = None                  # incoming short wave radiation [W m-2]
+        self.Swout      = None                  # outgoing short wave radiation [W m-2]
+        self.Lwin       = None                  # incoming long wave radiation [W m-2]
+        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
+        self.Q          = self.input.Q          # net radiation [W m-2]
+        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
+  
+        # initialize land surface
+        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
+        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
+        self.T2         = self.input.T2         # temperature deeper soil layer [K]
+                           
+        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
+        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
+        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
+        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
+                           
+        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
+        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
+        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
+                           
+        self.C1sat      = self.input.C1sat      
+        self.C2ref      = self.input.C2ref      
+
+        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
+        
+        self.LAI        = self.input.LAI        # leaf area index [-]
+        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
+        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = self.input.alpha      # surface albedo [-]
+  
+        self.rs         = 1.e6                  # resistance transpiration [s m-1]
+        self.rssoil     = 1.e6                  # resistance soil [s m-1]
+                           
+        self.Ts         = self.input.Ts         # surface temperature [K]
+                           
+        self.cveg       = self.input.cveg       # vegetation fraction [-]
+        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
+        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
+        self.cliq       = None                  # wet fraction [-]
+                          
+        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
+  
+        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
+        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
+        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
+  
+        self.H          = None                  # sensible heat flux [W m-2]
+        self.LE         = None                  # evapotranspiration [W m-2]
+        self.LEliq      = None                  # open water evaporation [W m-2]
+        self.LEveg      = None                  # transpiration [W m-2]
+        self.LEsoil     = None                  # soil evaporation [W m-2]
+        self.LEpot      = None                  # potential evaporation [W m-2]
+        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
+        self.G          = None                  # ground heat flux [W m-2]
+
+        # initialize A-Gs surface scheme
+        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
+
+        # initialize cumulus parameterization
+        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
+        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
+        self.ac         = 0.                    # Cloud core fraction [-]
+        self.M          = 0.                    # Cloud core mass flux [m s-1] 
+        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
+  
+        # initialize time variables
+        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
+        self.dt     = self.input.dt
+        self.dtcur      = self.dt
+        self.firsttime = True
+        self.t      = 0
+ 
+        # Some sanity checks for valid input
+        if (self.c_beta is None): 
+            self.c_beta = 0                     # Zero curvature; linear response
+        assert(self.c_beta >= 0 or self.c_beta <= 1)
+
+        # initialize output
+        self.out = model_output(self.tsteps)
+ 
+        self.statistics()
+  
+        # calculate initial diagnostic variables
+        if(self.sw_rad):
+            self.run_radiation()
+ 
+        if(self.sw_sl):
+            for i in range(10): 
+                self.run_surface_layer()
+  
+        if(self.sw_ls):
+            self.run_land_surface()
+
+        if(self.sw_cu):
+            self.run_mixed_layer()
+            self.run_cumulus()
+        
+        if(self.sw_ml):
+            self.run_mixed_layer()
+
+    def timestep(self):
+
+        self.dtmax = +np.inf
+        self.logger.debug('before stats') 
+        self.statistics()
+
+        # run radiation model
+        self.logger.debug('before rad') 
+        if(self.sw_rad):
+            self.run_radiation()
+  
+        # run surface layer model
+        if(self.sw_sl):
+            self.logger.debug('before surface layer') 
+            self.run_surface_layer()
+        
+        # run land surface model
+        if(self.sw_ls):
+            self.logger.debug('before land surface') 
+            self.run_land_surface()
+ 
+        # run cumulus parameterization
+        if(self.sw_cu):
+            self.logger.debug('before cumulus') 
+            self.run_cumulus()
+   
+        self.logger.debug('before mixed layer') 
+        # run mixed-layer model
+        if(self.sw_ml):
+            self.run_mixed_layer()
+        self.logger.debug('after mixed layer') 
+ 
+        #get first profile data point above mixed layer
+        if self.sw_ap:
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                # here we correct for the fact that the upper profile also
+                # shifts in the vertical.
+
+                diffhtend = self.htend - self.air_ap.w[zidx_first]
+                if diffhtend > 0:
+                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            else:
+                if self.htend > 0:
+                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            #print(self.h,zidx_first,self.ws,self.air_ap.z)
+
+        
+        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
+        self.logger.debug('before store') 
+        self.substep =  (self.dtcur > self.dtmax)
+        if self.substep:
+            dtnext = self.dtcur - self.dtmax
+            self.dtcur = self.dtmax
+
+        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
+
+        # HW: this will be done multiple times in case of a substep is needed
+        # store output before time integration
+        if self.firsttime:
+            self.store()
+  
+        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
+        # time integrate land surface model
+        if(self.sw_ls):
+            self.integrate_land_surface()
+        self.logger.debug('before integrate mixed layer') 
+        # time integrate mixed-layer model
+        if(self.sw_ml):
+            self.integrate_mixed_layer() 
+        self.logger.debug('after integrate mixed layer') 
+        if self.substep:
+            self.dtcur = dtnext
+            self.firsttime = False
+            self.substeps += 1
+        else:
+            self.dtcur = self.dt
+            self.t += 1 
+            self.firsttime = True
+            self.substeps = 0
+        self.logger.debug('going to next step')
+        
+        
+  
+    def statistics(self):
+        # Calculate virtual temperatures 
+        self.thetav   = self.theta  + 0.61 * self.theta * self.q
+        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
+        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
+        # Mixed-layer top properties
+        self.P_h    = self.Ps - self.rho * self.g * self.h
+        # else:
+            # in the other case, it is updated at the time that the profile is
+            # updated (and at the initialization
+
+        self.T_h    = self.theta - self.g/self.cp * self.h
+
+        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
+        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
+
+        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
+
+        # Find lifting condensation level iteratively
+        if(self.t == 0):
+            self.lcl = self.h
+            RHlcl = 0.5
+        else:
+            RHlcl = 0.9998 
+
+        itmax = 30
+        it = 0
+        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
+            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
+            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
+        else:
+            self.q2_h   = 0.
+            self.CO22_h = 0.
+
+        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
+        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
+        self.M      = self.ac * self.wstar
+        self.wqM    = self.M * self.q2_h**0.5
+
+        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
+        if(self.dCO2 < 0):
+            self.wCO2M  = self.M * self.CO22_h**0.5
+        else:
+            self.wCO2M  = 0.
+
+    def run_mixed_layer(self):
+        if(not self.sw_sl):
+            # decompose ustar along the wind components
+            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
+            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
+
+
+
+        # calculate large-scale vertical velocity (subsidence)
+        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
+            self.ws = -self.divU * self.h
+        # else:
+        #     in case the air circulation switch is turned on, subsidence is
+        #     calculated from the circulate profile at the initialization and
+        #     in the integrate_mixed_layer routine
+              
+        # calculate compensation to fix the free troposphere in case of subsidence 
+        if(self.sw_fixft):
+            w_th_ft  = self.gammatheta * self.ws
+            w_q_ft   = self.gammaq     * self.ws
+            w_CO2_ft = self.gammaCO2   * self.ws 
+        else:
+            w_th_ft  = 0.
+            w_q_ft   = 0.
+            w_CO2_ft = 0. 
+      
+        # calculate mixed-layer growth due to cloud top radiative divergence
+        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
+       
+        # calculate convective velocity scale w* 
+        if(self.wthetav > 0.):
+            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
+        else:
+            self.wstar  = 1e-6;
+      
+        # Virtual heat entrainment flux 
+        self.wthetave    = -self.beta * self.wthetav 
+        
+        # compute mixed-layer tendencies
+        if(self.sw_shearwe):
+            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
+        else:
+            self.we    = -self.wthetave / self.dthetav
+        # Don't allow boundary layer shrinking if wtheta < 0 
+        if(self.we < 0):
+            self.we = 0.
+
+        # Calculate entrainment fluxes
+        self.wthetae     = -self.we * self.dtheta
+        self.wqe         = -self.we * self.dq
+        self.wCO2e       = -self.we * self.dCO2
+        
+        htend_pre       = self.we + self.ws + self.wf - self.M
+        
+        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+        
+ 
+        #print('thetatend_pre',thetatend_pre)
+        
+        #preliminary boundary-layer top chenage
+        #htend_pre = self.we + self.ws + self.wf - self.M
+        #preliminary change in temperature jump
+        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
+                          thetatend_pre + w_th_ft
+        
+        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
+        l_entrainment = True
+
+        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
+            l_entrainment = False
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! temperature jump is at the lower limit \
+                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+        elif dtheta_pre < 0.1:
+            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
+            l_entrainment = True
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          " Warning! Potential temperature jump at mixed- \
+                          layer height would become too low limiting timestep \
+                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+            self.dtmax = min(self.dtmax,dtmax_new)
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "next subtimestep, entrainment will be disabled")
+            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
+
+
+
+        # when entrainment is disabled, we just use the simplified formulation
+        # as in Wouters et al., 2013 (section 2.2.1)
+
+        self.dthetatend = l_entrainment*dthetatend_pre + \
+                        (1.-l_entrainment)*0.
+        self.thetatend = l_entrainment*thetatend_pre + \
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = l_entrainment*htend_pre + \
+                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
+        #stop
+
+
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+
+
+        # self.qtend = l_entrainment*qtend_pre + \
+        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
+        # self.CO2tend = l_entrainment*CO2tend_pre + \
+        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+
+
+        #     # part of the timestep for which the temperature mixed-layer jump
+        #     # was changing, and for which entrainment took place. For the other
+        #     # part, we don't assume entrainment anymore, and we use the
+        #     # simplified formulation  of Wouters et al., 2013
+
+        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
+        #   
+        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
+        #                      self.dthetatend + w_th_ft) + \
+        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
+        #     self.htend = fac*self.htend + \
+        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
+        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
+        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+
+        # else:
+        #     #self.htend = htend_pre
+        #     self.dthetatend = dthetatend_pre
+        #     self.thetatend = thetatend_pre
+        
+        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+     
+        # assume u + du = ug, so ug - u = du
+        if(self.sw_wind):
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+  
+            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
+        
+        # tendency of the transition layer thickness
+        if(self.ac > 0 or self.lcl - self.h < 300):
+            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
+        else:
+            self.dztend = 0.
+
+   
+    def integrate_mixed_layer(self):
+        # set values previous time step
+        h0      = self.h
+        
+        theta0  = self.theta
+        dtheta0 = self.dtheta
+        q0      = self.q
+        dq0     = self.dq
+        CO20    = self.CO2
+        dCO20   = self.dCO2
+        
+        u0      = self.u
+        du0     = self.du
+        v0      = self.v
+        dv0     = self.dv
+
+        dz0     = self.dz_h
+  
+        # integrate mixed-layer equations
+        
+            
+
+# END -- HW 20170606        
+        self.h        = h0      + self.dtcur * self.htend
+        # print(self.h,self.htend)
+        # stop
+        self.theta    = theta0  + self.dtcur * self.thetatend
+        #print(dtheta0,self.dtcur,self.dthetatend)
+        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+        self.q        = q0      + self.dtcur * self.qtend
+        self.dq       = dq0     + self.dtcur * self.dqtend
+        self.CO2      = CO20    + self.dtcur * self.CO2tend
+        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
+        self.dz_h     = dz0     + self.dtcur * self.dztend
+            
+        # Limit dz to minimal value
+        dz0 = 50
+        if(self.dz_h < dz0):
+            self.dz_h = dz0 
+  
+        if(self.sw_wind):
+            self.u        = u0      + self.dtcur * self.utend
+            self.du       = du0     + self.dtcur * self.dutend
+            self.v        = v0      + self.dtcur * self.vtend
+            self.dv       = dv0     + self.dtcur * self.dvtend
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+            for var in ['t','q','u','v']:
+                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
+
+            # take into account advection for the whole profile
+                
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
+
+            var = 'z'
+            #print(self.air_ap[var])
+                #     print(self.air_ap['adv'+var])
+
+
+
+
+            #moving the profile vertically according to the vertical wind
+                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
+
+
+            # air_apvarold = pd.Series(np.array(self.air_ap.z))
+            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
+            # stop
+
+
+                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
+                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
+
+            #As t is updated, we also need to recalculate theta (and R)
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+
+            # air_aptheta_old = pd.Series(self.air_ap['theta'])
+            self.air_ap['theta'] = \
+                        self.air_ap.t * \
+                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
+                                         self.dtcur * self.air_ap.w[zidx_first:]
+
+#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
+#            print(self.t, self.dtcur,self.dt,self.htend)
+
+            # # the pressure levels of the profiles are recalculated according to
+            # # there new height (after subsidence)
+            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
+            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
+            #         * self.dtcur *  self.air_ap.w[zidx_first:]
+
+            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
+                    self.dtcur * self.air_ap.wp[zidx_first:]
+
+            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
+        # note that theta and q itself are updatet by class itself
+
+    
+        if self.sw_ap:
+            # Just for model consistency preservation purposes, we set the
+            # theta variables of the mixed-layer to nan values, since the
+            # mixed-layer values should overwritte by the mixed-layer
+            # calculations of class.
+            self.air_ap['theta'][0:3] = np.nan 
+            self.air_ap['p'][0:3] = np.nan 
+            self.air_ap['q'][0:3] = np.nan 
+            self.air_ap['u'][0:3] = np.nan 
+            self.air_ap['v'][0:3] = np.nan 
+            self.air_ap['t'][0:3] = np.nan 
+            self.air_ap['z'][0:3] = np.nan 
+
+            # Update the vertical profiles: 
+            #   - new mixed layer properties( h, theta, q ...)
+            #   - any data points below the new ixed-layer height are removed
+
+            # Three data points at the bottom that describe the mixed-layer
+            # properties
+            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
+                                           # columns as air_ap
+            # air_ap_head['z'].iloc[0] = 2.
+            # air_ap_head['z'].iloc[1] = self.__dict__['h']
+            # air_ap_head['z'].iloc[2] = self.__dict__['h']
+            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
+                        [2.,self.__dict__['h'],self.__dict__['h']]
+            for var in ['theta','q','u','v']:
+
+                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
+                        [self.__dict__[var], \
+                         self.__dict__[var], \
+                         self.__dict__[var] + self.__dict__['d'+var]]
+                
+            #print(self.air_ap)
+
+            # This is the remaining profile considering the remaining
+            # datapoints above the mixed layer height
+            air_ap_tail = self.air_ap.iloc[3:]
+            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
+
+            # print('h',self.h)
+            # # only select samples monotonically increasing with height
+            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            # air_ap_tail = pd.DataFrame()
+            # theta_low = self.theta
+            # z_low =     self.h
+            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            # for ibottom in range(1,len(air_ap_tail_orig)):
+            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
+            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+            # make theta increase strong enough to avoid numerical
+            # instability
+            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            air_ap_tail = pd.DataFrame()
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            theta_low = self.theta
+            z_low =     self.h
+            ibottom = 0
+            itop = 0
+            # print(air_ap_tail_orig)
+            # stop
+
+            # HW: this is the lower limit that we use for gammatheta, which is
+            # there to avoid model crashes. Besides on this limit, the upper
+            # air profile is modified in a way that is still conserves total
+            # quantities of moisture and temperature. The limit is set by trial
+            # and error. The numerics behind the crash should be investigated
+            # so that a cleaner solution can be provided.
+            gammatheta_lower_limit = 0.002
+            while ((itop in range(0,1)) or (itop != ibottom)):
+                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+                if (
+                    #(z_mean > (z_low+0.2)) and \
+                    #(theta_mean > (theta_low+0.02) ) and \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                  (itop >= (len(air_ap_tail_orig)-1)) \
+                   :
+
+                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                    ibottom = itop+1
+                    theta_low = air_ap_tail.theta.iloc[-1]
+                    z_low =     air_ap_tail.z.iloc[-1]
+    
+
+                itop +=1
+                # elif  (itop > len(air_ap_tail_orig)-10):
+                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+                #print(itop,ibottom)
+
+            if itop > 1:
+                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! Temperature profile was too steep. \
+                                  Modifying profile: "+ \
+                                  str(itop - 1)+ " measurements were dropped \
+                                  and replaced with its average \
+                                  Modifying profile. \
+                                  mean with next profile point(s).") 
+
+
+            self.air_ap = pd.concat((air_ap_head,\
+                                     air_ap_tail,\
+                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
+                                                                      axis=1)
+
+            if  self.sw_ac:
+                qvalues = \
+                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
+
+                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
+                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
+                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+                self.P_h    = self.Ps - self.rho * self.g * self.h
+                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
+                        [self.Ps,  self.P_h, self.P_h-0.1]
+
+                self.air_ap.t = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
+
+
+        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
+
+
+
+
+        # else:
+            # in the other case, it is updated at the time the statistics are
+            # calculated 
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if in_ml.sum() == 0:
+                warnings.warn(" no circulation points in the mixed layer \
+                              found. We just take the bottom one.")
+                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+            for var in ['t','q','u','v']:
+
+                # calculation of the advection variables for the mixed-layer
+                # these will be used for the next timestep
+                # Warning: w is excluded for now.
+
+                self.__dict__['adv'+var] = \
+                        ((self.air_ac['adv'+var+'_x'][in_ml] \
+                         + \
+                         self.air_ac['adv'+var+'_y'][in_ml])* \
+                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                        self.air_ac['delpdgrav'][in_ml].sum()
+
+                # calculation of the advection variables for the profile above
+                # the mixed layer (also for the next timestep)
+                self.air_ap['adv'+var] = \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p,\
+                                              self.air_ac['adv'+var+'_x']) \
+                                    + \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p, \
+                                              self.air_ac['adv'+var+'_y'])
+                # if var == 't':
+                #     print(self.air_ap['adv'+var])
+                #     stop
+
+            # as an approximation, we consider that advection of theta in the
+            # mixed layer is equal to advection of t. This is a sufficient
+            # approximation since theta and t are very similar at the surface
+            # pressure.
+
+            self.__dict__['advtheta'] = self.__dict__['advt']
+
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            # update the vertical wind profile
+            self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                          self.air_ac.p, \
+                                          self.air_ac['wp'])
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+            
+            air_apwold = self.air_ap['w']
+            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+            #print('hello w upd')
+
+            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # # self.wrho = np.interp(self.P_h,\
+            # #                      self.air_ach.p,\
+            # #                      self.air_ach['wrho']) \
+
+
+
+            # Also update the vertical wind at the mixed-layer height
+            # (subsidence)
+            self.ws   = self.air_ap.w[1]
+        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
+
+            ## Finally, we update he 
+            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+            #                        + \
+            #                        self.air_ac['divU_y'][in_ml])* \
+            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+            #            self.air_ac['delpdgrav'][in_ml].sum() 
+            
+
+        if self.sw_ap:
+            for var in ['theta','q','u','v']:
+
+                # update of the slope (gamma) for the different variables, for
+                # the next timestep!
+
+                # there is an warning message that tells about dividing through
+                # zero, which we ignore
+
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                    # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap['gamma'+var] = gammavar
+
+                # Based on the above, update the gamma value at the mixed-layer
+                # top
+                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
+                                                                     self.air_ap.z)[0][-1]]
+
+            
+    def run_radiation(self):
+        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
+        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
+        sinlea = max(sinlea, 0.0001)
+        
+        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
+  
+        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
+  
+        self.Swin  = self.S0 * Tr * sinlea
+        self.Swout = self.alpha * self.S0 * Tr * sinlea
+        
+        
+        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
+        self.Lwout = self.bolz * self.Ts ** 4.
+          
+        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
+        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
+  
+    def run_surface_layer(self):
+        # HW: I had to raise the minimum wind speed to make the simulation with
+        # the non-iterative solution stable (this solution was a wild guess, so I don't
+        # know the exact problem of the instability in case of very low wind
+        # speeds yet)
+        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        # version of 20180730 where there are still some runs crashing. Maybe
+        # an upper limit should be set on the monin-obukhov length instead of
+        # a lower limmit on the wind speed?
+        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        
+        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
+        qsatsurf       = qsat(self.thetasurf, self.Ps)
+        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
+        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
+
+        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
+  
+        zsl       = 0.1 * self.h
+        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
+        
+
+
+        if self.sw_lit:
+            self.Rib  = min(self.Rib, 0.2)
+            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
+            self.zeta  = zsl/self.L
+            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
+            
+        
+            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
+            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
+            
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+        
+     
+            # diagnostic meteorological variables
+            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
+            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
+            
+            # diagnostic meteorological variables
+        else:
+            
+            ## circumventing any iteration with Wouters et al., 2012
+            self.zslz0m = np.max((zsl/self.z0m,10.))
+            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
+            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
+            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
+            self.L = zsl/self.zeta
+            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
+        
+            self.Cm = self.k**2.0/funm/funm
+            self.Cs = self.k**2.0/funm/funh
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+            
+            # extrapolation from mixed layer (instead of from surface) to 2meter
+            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
+            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
+            self.u2m    =                - self.uw     / self.ustar / self.k * funm
+            self.v2m    =                - self.vw     / self.ustar / self.k * funm
+        
+        
+        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
+        self.e2m    = self.q2m * self.Ps / 0.622
+     
+    def ribtol(self, Rib, zsl, z0m, z0h): 
+        if(Rib > 0.):
+            L    = 1.
+            L0   = 2.
+        else:
+            L  = -1.
+            L0 = -2.
+        #print(Rib,zsl,z0m,z0h)
+        
+        while (abs(L - L0) > 0.001):
+            L0      = L
+            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
+            Lstart  = L - 0.001*L
+            Lend    = L + 0.001*L
+            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
+                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+            L       = L - fx / fxdif
+            #print(L)
+            if(abs(L) > 1e12):
+                break
+
+        return L
+      
+    def psim(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psim = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+        return psim
+      
+    def psih(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psih  = 2. * np.log( (1. + x*x) / 2.)
+            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+        return psih
+ 
+    def jarvis_stewart(self):
+        # calculate surface resistances using Jarvis-Stewart model
+        if(self.sw_rad):
+            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
+        else:
+            f1 = 1.
+  
+        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
+            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
+        else:
+            f2 = 1.e8
+ 
+        # Limit f2 in case w2 > wfc, where f2 < 1
+        f2 = max(f2, 1.);
+ 
+        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
+        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
+  
+        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
+
+    def factorial(self,k):
+        factorial = 1
+        for n in range(2,k+1):
+            factorial = factorial * float(n)
+        return factorial;
+
+    def E1(self,x):
+        E1sum = 0
+        for k in range(1,100):
+            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
+        return -0.57721566490153286060 - np.log(x) - E1sum
+ 
+    def ags(self):
+        # Select index for plant type
+        if(self.c3c4 == 'c3'):
+            c = 0
+        elif(self.c3c4 == 'c4'):
+            c = 1
+        else:
+            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
+
+        # calculate CO2 compensation concentration
+        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
+
+        # calculate mesophyll conductance
+        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
+                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
+        gm            = gm / 1000. # conversion from mm s-1 to m s-1
+  
+        # calculate CO2 concentration inside the leaf (ci)
+        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
+        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
+  
+        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
+        D0            = (self.f0[c] - fmin) / self.ad[c]
+  
+        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
+        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
+        ci            = cfrac * (co2abs - CO2comp) + CO2comp
+  
+        # calculate maximal gross primary production in high light conditions (Ag)
+        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
+  
+        # calculate effect of soil moisture stress on gross assimilation rate
+        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
+  
+        # calculate stress function
+        if (self.c_beta == 0):
+            fstr = betaw;
+        else:
+            # Following Combe et al (2016)
+            if (self.c_beta < 0.25):
+                P = 6.4 * self.c_beta
+            elif (self.c_beta < 0.50):
+                P = 7.6 * self.c_beta - 0.3
+            else:
+                P = 2**(3.66 * self.c_beta + 0.34) - 1
+            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
+  
+        # calculate gross assimilation rate (Am)
+        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
+        Rdark        = (1. / 9.) * Am
+        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
+  
+        # calculate  light use efficiency
+        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
+  
+        # calculate gross primary productivity
+        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
+  
+        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
+        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
+        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
+  
+        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
+        a1           = 1. / (1. - self.f0[c])
+        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
+  
+        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
+  
+        # calculate surface resistance for moisture and carbon dioxide
+        self.rs      = 1. / (1.6 * gcco2)
+        rsCO2        = 1. / gcco2
+  
+        # calculate net flux of CO2 into the plant (An)
+        An           = -(co2abs - ci) / (self.ra + rsCO2)
+  
+        # CO2 soil surface flux
+        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
+        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
+  
+        # CO2 flux
+        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
+        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
+        self.wCO2    = self.wCO2A + self.wCO2R
+ 
+    def run_land_surface(self):
+        # compute ra
+        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
+        #print('ueff',self.u,self.v,self.wstar)
+
+        if(self.sw_sl):
+          self.ra = (self.Cs * ueff)**-1.
+        else:
+          self.ra = ueff / max(1.e-3, self.ustar)**2.
+
+        #print('ra',self.ra,self.ustar,ueff)
+
+        # first calculate essential thermodynamic variables
+        self.esat    = esat(self.theta)
+        self.qsat    = qsat(self.theta, self.Ps)
+        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
+        self.dqsatdT = 0.622 * desatdT / self.Ps
+        self.e       = self.q * self.Ps / 0.622
+
+        if(self.ls_type == 'js'): 
+            self.jarvis_stewart() 
+        elif(self.ls_type == 'ags'):
+            self.ags()
+        else:
+            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
+
+        # recompute f2 using wg instead of w2
+        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
+          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
+        else:
+          f2        = 1.e8
+        self.rssoil = self.rssoilmin * f2 
+ 
+        Wlmx = self.LAI * self.Wmax
+        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
+        self.cliq = min(1., self.Wl / Wlmx) 
+     
+        # calculate skin temperature implictly
+        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
+            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
+            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
+            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
+
+        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        #print('Ts',self.rs)
+
+        esatsurf      = esat(self.Ts)
+        self.qsatsurf = qsat(self.Ts, self.Ps)
+
+        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+  
+        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
+  
+        self.LE     = self.LEsoil + self.LEveg + self.LEliq
+        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
+        #print('H',self.ra,self.Ts,self.theta)
+        self.G      = self.Lambda * (self.Ts - self.Tsoil)
+        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
+        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
+        
+        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
+  
+        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
+   
+        d1          = 0.1
+        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
+        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
+        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
+        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
+  
+        # calculate kinematic heat fluxes
+        self.wtheta   = self.H  / (self.rho * self.cp)
+        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
+        self.wq       = self.LE / (self.rho * self.Lv)
+ 
+    def integrate_land_surface(self):
+        # integrate soil equations
+        Tsoil0        = self.Tsoil
+        wg0           = self.wg
+        Wl0           = self.Wl
+  
+        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
+        self.wg       = wg0     + self.dtcur * self.wgtend
+        self.Wl       = Wl0     + self.dtcur * self.Wltend
+  
+    # store model output
+    def store(self):
+        t                      = self.t
+        
+        self.out.time[t]          = t * self.dt / 3600. + self.tstart
+
+        # in case we are at the end of the simulation, we store the vertical
+        # profiles to the output
+        
+        # if t == (len(self.out.time) - 1):
+        #     self.out.air_ac = self.air_ac
+        #     self.out.air_ap = self.air_ap
+
+        
+        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
+        #  for key in self.out.__dict__.keys():
+        #      if key in self.__dict__:
+        #          self.out.__dict__[key][t]  = self.__dict__[key]
+        
+        self.out.h[t]          = self.h
+        
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
+        
+        self.out.gammatheta[t] = self.gammatheta
+        self.out.gammau[t]     = self.gammau
+        self.out.gammav[t]     = self.gammav
+        self.out.gammaq[t]     = self.gammaq
+        self.out.theta[t]      = self.theta
+        self.out.thetav[t]     = self.thetav
+        self.out.dtheta[t]     = self.dtheta
+        self.out.dthetav[t]    = self.dthetav
+        self.out.wtheta[t]     = self.wtheta
+        self.out.wthetav[t]    = self.wthetav
+        self.out.wthetae[t]    = self.wthetae
+        self.out.wthetave[t]   = self.wthetave
+        
+        self.out.q[t]          = self.q
+        self.out.dq[t]         = self.dq
+        self.out.wq[t]         = self.wq
+        self.out.wqe[t]        = self.wqe
+        self.out.wqM[t]        = self.wqM
+      
+        self.out.qsat[t]       = self.qsat
+        self.out.e[t]          = self.e
+        self.out.esat[t]       = self.esat
+      
+        fac = (self.rho*self.mco2)/self.mair
+        self.out.CO2[t]        = self.CO2
+        self.out.dCO2[t]       = self.dCO2
+        self.out.wCO2[t]       = self.wCO2  * fac
+        self.out.wCO2e[t]      = self.wCO2e * fac
+        self.out.wCO2R[t]      = self.wCO2R * fac
+        self.out.wCO2A[t]      = self.wCO2A * fac
+
+        self.out.u[t]          = self.u
+        self.out.du[t]         = self.du
+        self.out.uw[t]         = self.uw
+        
+        self.out.v[t]          = self.v
+        self.out.dv[t]         = self.dv
+        self.out.vw[t]         = self.vw
+        
+        self.out.T2m[t]        = self.T2m
+        self.out.q2m[t]        = self.q2m
+        self.out.u2m[t]        = self.u2m
+        self.out.v2m[t]        = self.v2m
+        self.out.e2m[t]        = self.e2m
+        self.out.esat2m[t]     = self.esat2m
+
+
+        self.out.Tsoil[t]      = self.Tsoil
+        self.out.T2[t]         = self.T2
+        self.out.Ts[t]         = self.Ts
+        self.out.wg[t]         = self.wg
+        
+        self.out.thetasurf[t]  = self.thetasurf
+        self.out.thetavsurf[t] = self.thetavsurf
+        self.out.qsurf[t]      = self.qsurf
+        self.out.ustar[t]      = self.ustar
+        self.out.Cm[t]         = self.Cm
+        self.out.Cs[t]         = self.Cs
+        self.out.L[t]          = self.L
+        self.out.Rib[t]        = self.Rib
+  
+        self.out.Swin[t]       = self.Swin
+        self.out.Swout[t]      = self.Swout
+        self.out.Lwin[t]       = self.Lwin
+        self.out.Lwout[t]      = self.Lwout
+        self.out.Q[t]          = self.Q
+  
+        self.out.ra[t]         = self.ra
+        self.out.rs[t]         = self.rs
+        self.out.H[t]          = self.H
+        self.out.LE[t]         = self.LE
+        self.out.LEliq[t]      = self.LEliq
+        self.out.LEveg[t]      = self.LEveg
+        self.out.LEsoil[t]     = self.LEsoil
+        self.out.LEpot[t]      = self.LEpot
+        self.out.LEref[t]      = self.LEref
+        self.out.G[t]          = self.G
+
+        self.out.zlcl[t]       = self.lcl
+        self.out.RH_h[t]       = self.RH_h
+
+        self.out.ac[t]         = self.ac
+        self.out.M[t]          = self.M
+        self.out.dz[t]         = self.dz_h
+        self.out.substeps[t]   = self.substeps
+  
+    # delete class variables to facilitate analysis in ipython
+    def exitmodel(self):
+        del(self.Lv)
+        del(self.cp)
+        del(self.rho)
+        del(self.k)
+        del(self.g)
+        del(self.Rd)
+        del(self.Rv)
+        del(self.bolz)
+        del(self.S0)
+        del(self.rhow)
+  
+        del(self.t)
+        del(self.dt)
+        del(self.tsteps)
+         
+        del(self.h)          
+        del(self.Ps)        
+        del(self.fc)        
+        del(self.ws)
+        del(self.we)
+        
+        del(self.theta)
+        del(self.dtheta)
+        del(self.gammatheta)
+        del(self.advtheta)
+        del(self.beta)
+        del(self.wtheta)
+    
+        del(self.T2m)
+        del(self.q2m)
+        del(self.e2m)
+        del(self.esat2m)
+        del(self.u2m)
+        del(self.v2m)
+        
+        del(self.thetasurf)
+        del(self.qsatsurf)
+        del(self.thetav)
+        del(self.dthetav)
+        del(self.thetavsurf)
+        del(self.qsurf)
+        del(self.wthetav)
+        
+        del(self.q)
+        del(self.qsat)
+        del(self.dqsatdT)
+        del(self.e)
+        del(self.esat)
+        del(self.dq)
+        del(self.gammaq)
+        del(self.advq)
+        del(self.wq)
+        
+        del(self.u)
+        del(self.du)
+        del(self.gammau)
+        del(self.advu)
+        
+        del(self.v)
+        del(self.dv)
+        del(self.gammav)
+        del(self.advv)
+  
+        del(self.htend)
+        del(self.thetatend)
+        del(self.dthetatend)
+        del(self.qtend)
+        del(self.dqtend)
+        del(self.utend)
+        del(self.dutend)
+        del(self.vtend)
+        del(self.dvtend)
+     
+        del(self.Tsoiltend) 
+        del(self.wgtend)  
+        del(self.Wltend) 
+  
+        del(self.ustar)
+        del(self.uw)
+        del(self.vw)
+        del(self.z0m)
+        del(self.z0h)        
+        del(self.Cm)         
+        del(self.Cs)
+        del(self.L)
+        del(self.Rib)
+        del(self.ra)
+  
+        del(self.lat)
+        del(self.lon)
+        del(self.doy)
+        del(self.tstart)
+   
+        del(self.Swin)
+        del(self.Swout)
+        del(self.Lwin)
+        del(self.Lwout)
+        del(self.cc)
+  
+        del(self.wg)
+        del(self.w2)
+        del(self.cveg)
+        del(self.cliq)
+        del(self.Tsoil)
+        del(self.T2)
+        del(self.a)
+        del(self.b)
+        del(self.p)
+        del(self.CGsat)
+  
+        del(self.wsat)
+        del(self.wfc)
+        del(self.wwilt)
+  
+        del(self.C1sat)
+        del(self.C2ref)
+  
+        del(self.LAI)
+        del(self.rs)
+        del(self.rssoil)
+        del(self.rsmin)
+        del(self.rssoilmin)
+        del(self.alpha)
+        del(self.gD)
+  
+        del(self.Ts)
+  
+        del(self.Wmax)
+        del(self.Wl)
+  
+        del(self.Lambda)
+        
+        del(self.Q)
+        del(self.H)
+        del(self.LE)
+        del(self.LEliq)
+        del(self.LEveg)
+        del(self.LEsoil)
+        del(self.LEpot)
+        del(self.LEref)
+        del(self.G)
+  
+        del(self.sw_ls)
+        del(self.sw_rad)
+        del(self.sw_sl)
+        del(self.sw_wind)
+        del(self.sw_shearwe)
+
+# class for storing mixed-layer model output data
+class model_output:
+    def __init__(self, tsteps):
+        self.time          = np.zeros(tsteps)    # time [s]
+
+        # mixed-layer variables
+        self.h          = np.zeros(tsteps)    # ABL height [m]
+        
+        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammau     = np.zeros(tsteps)
+        self.gammav     = np.zeros(tsteps)
+        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
+        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
+        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
+        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
+        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
+        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
+        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
+        
+        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
+        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
+        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
+        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
+
+        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
+        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
+        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
+
+        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
+        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
+        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
+        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
+        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
+        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
+        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
+        
+        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
+        
+        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
+
+        # diagnostic meteorological variables
+        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
+        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
+        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
+        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
+        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
+        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
+
+        # ground variables
+        self.Tsoil       = np.zeros(tsteps)
+        self.T2          = np.zeros(tsteps)
+        self.Ts          = np.zeros(tsteps)
+        self.wg          = np.zeros(tsteps)
+
+        # surface-layer variables
+        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
+        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
+        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
+        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
+        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
+        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
+        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
+        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
+        self.L          = np.zeros(tsteps)    # Obukhov length [m]
+        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
+
+        # radiation variables
+        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
+        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
+        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
+        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
+        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
+
+        # land surface variables
+        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
+        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
+        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
+        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
+        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
+        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
+        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
+        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
+        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
+        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
+
+        # Mixed-layer top variables
+        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
+        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
+
+        # cumulus variables
+        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
+        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
+        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
+        
+        
+        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
+
+# class for storing mixed-layer model input data
+class model_input:
+    def __init__(self):
+
+        # # comment not valid
+        # we comment out the initialization, because there is a problem when
+        # inheriting values from one the another class4gl_iput. We also expect
+        # that the user specifies all the required parmameters (if not, an error
+        # is raised). 
+
+        # general model variables
+        self.runtime    = None  # duration of model run [s]
+        self.dt         = None  # time step [s]
+
+        # mixed-layer variables
+        self.sw_ml      = None  # mixed-layer model switch
+        self.sw_shearwe = None  # Shear growth ABL switch
+        self.sw_fixft   = None  # Fix the free-troposphere switch
+        self.h          = None  # initial ABL height [m]
+        self.Ps         = None  # surface pressure [Pa]
+        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
+        self.fc         = None  # Coriolis parameter [s-1]
+        
+        self.theta      = None  # initial mixed-layer potential temperature [K]
+        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
+
+        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
+
+        self.dtheta     = None  # initial temperature jump at h [K]
+        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = None  # advection of heat [K s-1]
+        self.beta       = None  # entrainment ratio for virtual heat [-]
+        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
+        
+        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
+        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
+
+        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = None  # advection of moisture [kg kg-1 s-1]
+        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
+
+        self.CO2        = None  # initial mixed-layer potential temperature [K]
+        self.dCO2       = None  # initial temperature jump at h [K]
+        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advCO2     = None  # advection of heat [K s-1]
+        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
+        
+        self.sw_wind    = None  # prognostic wind switch
+        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.du         = None  # initial u-wind jump at h [m s-1]
+        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = None  # advection of u-wind [m s-2]
+
+        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = None  # initial u-wind jump at h [m s-1]
+        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = None  # advection of v-wind [m s-2]
+
+        # surface layer variables
+        self.sw_sl      = None  # surface layer switch
+        self.ustar      = None  # surface friction velocity [m s-1]
+        self.z0m        = None  # roughness length for momentum [m]
+        self.z0h        = None  # roughness length for scalars [m]
+        self.Cm         = None  # drag coefficient for momentum [-]
+        self.Cs         = None  # drag coefficient for scalars [-]
+        self.L          = None  # Obukhov length [-]
+        self.Rib        = None  # bulk Richardson number [-]
+
+        # radiation parameters
+        self.sw_rad     = None  # radiation switch
+        self.lat        = None  # latitude [deg]
+        self.lon        = None  # longitude [deg]
+        self.doy        = None  # day of the year [-]
+        self.tstart     = None  # time of the day [h UTC]
+        self.cc         = None  # cloud cover fraction [-]
+        self.Q          = None  # net radiation [W m-2] 
+        self.dFz        = None  # cloud top radiative divergence [W m-2] 
+
+        # land surface parameters
+        self.sw_ls      = None  # land surface switch
+        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
+        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = None  # temperature top soil layer [K]
+        self.T2         = None  # temperature deeper soil layer [K]
+        
+        self.a          = None  # Clapp and Hornberger retention curve parameter a
+        self.b          = None  # Clapp and Hornberger retention curve parameter b
+        self.p          = None  # Clapp and Hornberger retention curve parameter p 
+        self.CGsat      = None  # saturated soil conductivity for heat
+        
+        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
+        self.wfc        = None  # volumetric water content field capacity [-]
+        self.wwilt      = None  # volumetric water content wilting point [-]
+        
+        self.C1sat      = None 
+        self.C2ref      = None
+
+        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
+        
+        self.LAI        = None  # leaf area index [-]
+        self.gD         = None  # correction factor transpiration for VPD [-]
+        self.rsmin      = None  # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = None  # surface albedo [-]
+        
+        self.Ts         = None  # initial surface temperature [K]
+        
+        self.cveg       = None  # vegetation fraction [-]
+        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
+        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
+        
+        self.Lambda     = None  # thermal diffusivity skin layer [-]
+
+        # A-Gs parameters
+        self.c3c4       = None  # Plant type ('c3' or 'c4')
+
+        # Cumulus parameters
+        self.sw_cu      = None  # Cumulus parameterization switch
+        self.dz_h       = None  # Transition layer thickness [m]
+        
+# BEGIN -- HW 20171027
+        # self.cala       = None      # soil heat conductivity [W/(K*m)]
+        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+# END -- HW 20171027
diff --git a/class4gl/ribtol/Makefile b/class4gl/ribtol/Makefile
new file mode 100644
index 0000000..e23e3e1
--- /dev/null
+++ b/class4gl/ribtol/Makefile
@@ -0,0 +1,8 @@
+ribtol.so : ribtol.o
+	g++ -O3 -shared -Wl -z -def -o ribtol.so -lpython2.6 -lboost_python ribtol.o
+
+ribtol.o : ribtol.cpp
+	g++ -c -O3 -fPIC ribtol.cpp -I/usr/include/python2.6
+
+clean : 
+	rm -rf ribtol.o ribtol.so
diff --git a/class4gl/ribtol/MakefileMac b/class4gl/ribtol/MakefileMac
new file mode 100644
index 0000000..bf34ea8
--- /dev/null
+++ b/class4gl/ribtol/MakefileMac
@@ -0,0 +1,9 @@
+# Note: boost-python needs to be installed: brew install boost-python -with-python3 -without-python
+ribtol.so : ribtol.o
+	clang++ -O3 -shared -o ribtol.so -L/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib -lpython3.6m -L/usr/local/lib -lboost_python3-mt -lpython ribtol.o
+
+ribtol.o : ribtol.cpp
+	clang++ -c -O3 -fPIC ribtol.cpp -I/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/include/python3.6m -I/usr/local/include
+
+clean : 
+	rm -rf ribtol.o ribtol.so
diff --git a/class4gl/ribtol/__init__.py b/class4gl/ribtol/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/class4gl/ribtol/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/class4gl/ribtol/ribtol.cpp b/class4gl/ribtol/ribtol.cpp
new file mode 100644
index 0000000..148b0d3
--- /dev/null
+++ b/class4gl/ribtol/ribtol.cpp
@@ -0,0 +1,81 @@
+// fast conversion of bulk Richardson number to Obukhov length
+
+#include 
+#include 
+#include 
+using namespace std;
+
+inline double psim(double zeta)
+{
+  double psim;
+  double x;
+  if(zeta <= 0.)
+  {
+    //x     = (1. - 16. * zeta) ** (0.25)
+    //psim  = 3.14159265 / 2. - 2. * arctan(x) + log( (1.+x) ** 2. * (1. + x ** 2.) / 8.)
+    x    = pow(1. + pow(3.6 * abs(zeta),2./3.), -0.5);
+    psim = 3. * log( (1. + 1. / x) / 2.);
+  }
+  else
+  {
+    psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35;
+  }
+  return psim;
+}
+    
+inline double psih(double zeta)
+{
+  double psih;
+  double x;
+  if(zeta <= 0.)
+  {
+    // x     = (1. - 16. * zeta) ** (0.25)
+    // psih  = 2. * log( (1. + x ** 2.) / 2. )
+    x     = pow(1. + pow(7.9 * abs(zeta), (2./3.)), -0.5);
+    psih  = 3. * log( (1. + 1. / x) / 2.);
+  }
+  else
+  {
+    psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - pow(1. + (2./3.) * zeta, 1.5) - (10./3.) / 0.35 + 1.;
+  }
+  return psih;
+}
+
+
+double ribtol(double Rib, double zsl, double z0m, double z0h)
+{
+  double L, L0;
+  double Lstart, Lend;
+  double fx, fxdif;
+
+  if(Rib > 0.)
+  {
+    L    = 1.;
+    L0   = 2.;
+  }
+  else
+  {
+    L  = -1.;
+    L0 = -2.;
+  }
+    
+  while (abs(L - L0) > 0.001)
+  {
+    L0      = L;
+    fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / pow(log(zsl / z0m) - psim(zsl / L) + psim(z0m / L), 2.);
+    Lstart  = L - 0.001 * L;
+    Lend    = L + 0.001 * L;
+    fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / pow(log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart), 2.)) - (-zsl / Lend * (log(zsl / z0h) - psih(zsl / Lend) + psih(z0h / Lend)) / pow(log(zsl / z0m) - psim(zsl / Lend) + psim(z0m / Lend), 2.)) ) / (Lstart - Lend);
+    L       = L - fx / fxdif;
+  }
+  
+  return L;
+
+}
+
+BOOST_PYTHON_MODULE(ribtol)
+{
+    using namespace boost::python;
+    def("ribtol", ribtol);
+}
+
diff --git a/class4gl/ribtol/ribtol.pyx b/class4gl/ribtol/ribtol.pyx
new file mode 100644
index 0000000..e11a147
--- /dev/null
+++ b/class4gl/ribtol/ribtol.pyx
@@ -0,0 +1,48 @@
+#cython: boundscheck=False
+#cython: wraparound=False
+
+from libc.math cimport atan, log, exp, fabs
+
+cdef double psim(double zeta):
+    cdef double x, psim
+
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * atan(x) + log((1. + x)**2. * (1. + x**2.) / 8.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+      
+cdef double psih(double zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * log( (1. + x*x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+
+def ribtol(double Rib, double zsl, double z0m, double z0h): 
+    cdef double L, L0, fx, Lstart, Lend, fxdif
+
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    
+    while (fabs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+
+        if(fabs(L) > 1e15):
+            break
+
+    return L
diff --git a/class4gl/ribtol/ribtol_hw.py b/class4gl/ribtol/ribtol_hw.py
new file mode 100644
index 0000000..1946cc8
--- /dev/null
+++ b/class4gl/ribtol/ribtol_hw.py
@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan 12 10:46:20 2018
+
+@author: vsc42247
+"""
+
+
+
+# purpose of calc_cm_ch: calculate momentum and thermal turbulent diffusion coefficients of the surface layer with a non-iterative procedure (Wouters et al., 2012)
+
+# input:
+
+# zrib = bulk Richardson number = (g/T)* DT * z/(ua^2)
+#   with:
+#     g = 9.81 m/s2 the gravitational acceleration
+#     z = height (in meters) of the surface layer under consideration 
+#     T = (reference) temperature (in Kelvin) at height z 
+#     DT = (T - T_s) = temperature (in Kelvin) gradient between the surface and height z 
+#     u_a^2 = u^2 +  v^2 is the squared horizontal absolute wind speed 
+# zzz0m = ratio z/z0 between the height z and the momentum roughness length z0m
+# zkbm = ln(z0m/z0h), with z0m, z0h the momentum and thermal roughness length, respectively.
+
+# output: diffusion coefficients (CM and CH) which cna be used to determine surface-layer turbulent transport
+# u'w' = - CM ua^2.
+# w'T' = - CH ua DT 
+
+
+# Reference:
+# Wouters, H., De Ridder, K., and Lipzig, N. P. M.: Comprehensive
+# Parametrization of Surface-Layer Transfer Coefficients for Use
+# in Atmospheric Numerical Models, Bound.-Lay. Meteorol., 145,
+# 539–550, doi:10.1007/s10546-012-9744-3, 2012.
+
+import numpy as np
+
+def calc_cm_ch (zeta,zzz0m,zkbm):
+    krm = 0.4
+
+    #ZETA = zeta_hs2(zrib,zzz0m,zkbm)
+    FUNM,FUNH = funcsche(ZETA,zzz0m,zkbm)
+    CM = krm**2.0/FUNM/FUNM
+    CH = krm**2.0/FUNM/FUNH
+
+    # FUNMn,FUNHn = funcsche(0.,zzz0m,zkbm)
+    # CMn = krm**2.0/FUNMn/FUNMn
+    # CHn = krm**2.0/FUNMn/FUNHn
+
+    # print ZETA,FUNM,FUNH
+    # print 'CMCMN',CM/CMn
+    # print 'CHCHN',CH/CHn
+
+    return CM,CH
+
+
+def zeta_hs2(RiB,zzz0m,kBmin1):
+    #print(RiB,zzz0m,kBmin1)
+    mum=2.59
+    muh=0.95
+    nu=0.5
+    lam=1.5
+
+    betah = 5.0
+
+    zzz0h = zzz0m*np.exp(kBmin1)
+    zzzs = zzz0m*0.06 # to be changed!! r. 101 nog bekijken!!
+
+    L0M = np.log(zzz0m)
+    L0H = np.log(zzz0h)
+    facM = np.log(1.+lam/mum/zzzs)*np.exp(-mum*zzzs)/lam
+    facH = np.log(1.+lam/muh/zzzs)*np.exp(-muh*zzzs)/lam
+    L0Ms = L0M + facM 
+    L0Hs = L0H + facH
+
+    if RiB < 0.:
+        p = np.log(1.-RiB)
+        Q = -0.486 +0.219*p - 0.0331*p**2-4.93*np.exp(-L0H) - 3.65/L0H +\
+            0.38*p/L0H+ 14.8/L0H/L0H-0.946*p/L0H/L0H-10.0/L0H**3+ \
+            0.392*L0M/L0H-0.084*p*L0M/L0H+0.368*L0M/L0H/L0H
+        # print 'p: ',p
+        # print 'Q: ',Q
+        zeta = (1. + p*Q)* L0Ms**2/L0Hs * RiB
+    else:
+        betam = 4.76+7.03/zzz0m +0.24*zzz0m/zzz0h # to be changed
+        # betam = 5.0 + 1.59*10.**(-5.)*(np.exp(13.0-L0M)-1.0) \
+        #         +0.24*(np.exp(-kBmin1)-1.0) # to be changed!!
+        # print('betam',betam)
+        lL0M = np.log(L0M)
+        S0Ms = 1.-1./zzz0m + (1.+nu/mum/zzzs)*facM
+        S0Hs = 1.-1./zzz0h + (1.+nu/muh/zzzs)*facH
+        zetat = -0.316-0.515*np.exp(-L0H) + 25.8 *np.exp(-2.*L0H) + 4.36/L0H \
+                -6.39/L0H/L0H+0.834*lL0M - 0.0267*lL0M**2
+        # print('zetat',zetat)
+        RiBt = zetat *(L0Hs+ S0Hs*betah*zetat)/(L0Ms+S0Ms*betam*zetat)**2 
+        # print('RiBt',RiBt)
+
+        if (RiB > RiBt):
+            D = (L0Ms+S0Ms*betam*zetat)**3/\
+                (L0Ms*L0Hs+zetat*(2.*S0Hs * betah * L0Ms - S0Ms*betam*L0Hs))
+            zeta = zetat + D*(RiB-RiBt)
+        else:
+            r = RiB - S0Hs*betah/(S0Ms*betam)**2
+            B = S0Ms*betam*L0Hs- 2.*S0Hs*betah*L0Ms
+            C = 4.*(S0Ms*betam)**2 * L0Ms *(S0Hs*betah*L0Ms-S0Ms*betam*L0Hs)
+            zeta = - L0Ms / S0Ms/betam - B*C/(4.*(S0Ms*betam)**3 *(B**2+abs(C*r)))
+            if r != 0:
+                zeta = zeta + (B-np.sqrt(B**2+C*r) + B*C*r/(2.*(B**2+abs(C*r))))/(2.*(S0Ms*betam)**3*r)
+    # print('zeta',zeta)
+    return zeta
+
+def funcsche(zeta,zzz0,kBmin1):
+
+
+    mum=2.5
+    muh=0.9
+    nu=0.5
+    lam=1.5
+    
+    p2=3.141592/2.
+    
+    lnzzz0=np.log(zzz0)
+    zzzs=zzz0*0.06
+    zetamcorr=(1.+nu/(mum*zzzs))*zeta
+    zetam0=zeta/zzz0
+    zetahcorr=(1.+nu/(muh*zzzs))*zeta
+    zetah0=zeta/(zzz0*np.exp(kBmin1))
+    
+    if (zeta <= 0.):
+    
+        gamma=15.2
+        alfam=0.25
+        xx=(1.-gamma*zeta)**alfam
+        psim=2.*np.log((1.+xx)/2.)+np.log((1.+xx**2.)/2.)-2.*np.arctan(xx)+p2
+        xx0=(1.-gamma*zetam0)**alfam
+        psim0=2.*np.log((1.+xx0)/2.)+np.log((1.+xx0**2.)/2.)-2.*np.arctan(xx0)+p2
+        phimcorr=(1.-gamma*zetamcorr)**(-alfam)
+        
+        alfah=0.5
+        yy=(1.-gamma*zeta)**alfah
+        psih=2.*np.log((1.+yy)/2.)
+        yy0=(1.-gamma*zetah0)**alfah
+        psih0=2.*np.log((1.+yy0)/2.)
+        phihcorr=(1.-gamma*zetahcorr)**(-alfah)
+    else: 
+    
+        aa=6.1
+        bb=2.5
+        psim=-aa*np.log(zeta+(1.+zeta**bb)**(1./bb))
+        psim0=-aa*np.log(zetam0+(1.+zetam0**bb)**(1./bb))
+        phimcorr=1.+aa*(zetamcorr+zetamcorr**bb*(1.+zetamcorr**bb)**((1.-bb)/bb))/(zetamcorr+(1.+zetamcorr**bb)**(1./bb))
+        
+        cc=5.3
+        dd=1.1
+        psih=-cc*np.log(zeta+(1.+zeta**dd)**(1./dd))
+        psih0=-cc*np.log(zetah0+(1.+zetah0**dd)**(1./dd))
+        phihcorr=1.+cc*(zetahcorr+zetahcorr**dd*(1.+zetahcorr**dd)**((1.-dd)/dd))/(zetahcorr+(1.+zetahcorr**dd)**(1./dd))
+    
+    psistrm=phimcorr*(1./lam)*np.log(1.+lam/(mum*zzzs))*np.exp(-mum*zzzs)
+    psistrh=phihcorr*(1./lam)*np.log(1.+lam/(muh*zzzs))*np.exp(-muh*zzzs)
+    
+    funm=lnzzz0-psim+psim0 +psistrm
+    funh=lnzzz0+kBmin1-psih+psih0 +psistrh
+    return funm,funh
+
diff --git a/class4gl/ribtol/setup.py b/class4gl/ribtol/setup.py
new file mode 100644
index 0000000..bfb44db
--- /dev/null
+++ b/class4gl/ribtol/setup.py
@@ -0,0 +1,12 @@
+# build with "python setup.py build_ext --inplace"
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Build import cythonize
+import numpy as np
+import os
+
+os.environ["CC"] = "g++-7"
+
+setup(
+    ext_modules = cythonize((Extension("ribtol", sources=["ribtol.pyx"], include_dirs=[np.get_include()], ), ))
+)
diff --git a/dist/class4gl-0.1dev.tar.gz b/dist/class4gl-0.1dev.tar.gz
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diff --git a/dist/class4gl-0.1dev/PKG-INFO b/dist/class4gl-0.1dev/PKG-INFO
new file mode 100644
index 0000000..1cecd19
--- /dev/null
+++ b/dist/class4gl-0.1dev/PKG-INFO
@@ -0,0 +1,14 @@
+Metadata-Version: 1.0
+Name: class4gl
+Version: 0.1dev
+Summary: UNKNOWN
+Home-page: UNKNOWN
+Author: UNKNOWN
+Author-email: UNKNOWN
+License: GPLv3 licence
+Description: # class4gl
+        Chemistry Land-surface Atmosphere Soil Slab model (CLASS) | Python version
+        
+        This is the extension of class to be able to be used with global balloon soundings. 
+        
+Platform: UNKNOWN
diff --git a/dist/class4gl-0.1dev/bin/__init__.py b/dist/class4gl-0.1dev/bin/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/dist/class4gl-0.1dev/bin/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/dist/class4gl-0.1dev/lib/__init__.py b/dist/class4gl-0.1dev/lib/__init__.py
new file mode 100644
index 0000000..a21583b
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/__init__.py
@@ -0,0 +1,7 @@
+from . import model,class4gl,interface_multi,data_air,data_global
+
+__version__ = '0.1.0'
+
+__author__ = 'Hendrik Wouters '
+
+__all__ = []
diff --git a/dist/class4gl-0.1dev/lib/class4gl.py b/dist/class4gl-0.1dev/lib/class4gl.py
new file mode 100644
index 0000000..7baaa51
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/class4gl.py
@@ -0,0 +1,1611 @@
+# -*- coding: utf-8 -*-
+
+"""
+
+Created on Mon Jan 29 12:33:51 2018
+
+Module file for class4gl, which  extents the class-model to be able to take
+global air profiles as input. It exists of:
+
+CLASSES:
+    - an input object, namely class4gl_input. It includes:
+        - a function to read Wyoming sounding data from a yyoming stream object
+        - a function to read global data from a globaldata library object 
+    - the model object: class4gl
+    - ....    
+
+DEPENDENCIES:
+    - xarray
+    - numpy
+    - data_global
+    - Pysolar
+    - yaml
+
+@author: Hendrik Wouters
+
+"""
+
+
+
+""" Setup of envirnoment """
+
+# Standard modules of the stand class-boundary-layer model
+from model import model
+from model import model_output as class4gl_output
+from model import model_input
+from model import qsat
+#from data_soundings import wyoming 
+import Pysolar
+import yaml
+import logging
+import warnings
+import pytz
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+
+# Generic Python Packages
+import numpy as np
+import datetime as dt
+import pandas as pd
+import xarray as xr
+import io
+#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
+from data_global import data_global
+grav = 9.81
+
+# this is just a generic input object
+class generic_input(object):
+    def __init__(self):
+        self.init = True
+
+
+# all units from all variables in CLASS(4GL) should be defined here!
+units = {
+         'h':'m',
+         'theta':'K', 
+         'q':'kg/kg',
+         'cc': '-',
+         'cveg': '-',
+         'wg': 'm3 m-3',
+         'w2': 'm3 m-3',
+         #'wg': 'kg/kg',
+         'Tsoil': 'K',
+         'T2': 'K',
+         'z0m': 'm',
+         'alpha': '-',
+         'LAI': '-',
+         'dhdt':'m/h',
+         'dthetadt':'K/h',
+         'dqdt':'kg/kg/h',
+         'BR': '-',
+         'EF': '-',
+}
+
+class class4gl_input(object):
+# this was the way it was defined previously.
+#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+
+    def __init__(self,set_pars_defaults=True,debug_level=None):
+
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        print('hello')
+        self.logger = logging.getLogger('class4gl_input')
+        print(self.logger)
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # # create logger
+        # self.logger = logging.getLogger('class4gl_input')
+        # self.logger.setLevel(debug_level)
+
+        # # create console handler and set level to debug
+        # ch = logging.StreamHandler()
+        # ch.setLevel(debug_level)
+
+        # # create formatter
+        # formatter = logging.Formatter('%(asctime)s - \
+        #                                %(name)s - \
+        #                                %(levelname)s - \
+        #                                %(message)s')
+        # add formatter to ch
+        # ch.setFormatter(formatter)
+     
+        # # add ch to logger
+        # self.logger.addHandler(ch)
+
+        # """ end set up logger """
+
+
+
+        # these are the standard model input single-value parameters for class
+        self.pars = model_input()
+
+        # diagnostic parameters of the initial profile
+        self.diag = dict()
+
+        # In this variable, we keep track of the different parameters from where it originates from. 
+        self.sources = {}
+
+        if set_pars_defaults:
+            self.set_pars_defaults()
+
+    def set_pars_defaults(self):
+
+        """ 
+        Create empty model_input and set up case
+        """
+        defaults = dict( 
+        dt         = 60.    , # time step [s] 
+        runtime    = 6*3600 ,  # total run time [s]
+        
+        # mixed-layer input
+        sw_ml      = True   ,  # mixed-layer model switch
+        sw_shearwe = False  ,  # shear growth mixed-layer switch
+        sw_fixft   = False  ,  # Fix the free-troposphere switch
+        h          = 200.   ,  # initial ABL height [m]
+        Ps         = 101300.,  # surface pressure [Pa]
+        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
+        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
+        
+        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
+        dtheta     = 1.     ,  # initial temperature jump at h [K]
+        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        advtheta   = 0.     ,  # advection of heat [K s-1]
+        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
+        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
+        
+        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
+        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
+        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
+        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
+        
+        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
+        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
+        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
+        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
+        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
+        sw_wind    = True  ,  # prognostic wind switch
+        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
+        du         = 0.     ,  # initial u-wind jump at h [m s-1]
+        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
+        advu       = 0.     ,  # advection of u-wind [m s-2]
+        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
+        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
+        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
+        advv       = 0.     ,  # advection of v-wind [m s-2]
+        sw_sl      = True   , # surface layer switch
+        ustar      = 0.3    ,  # surface friction velocity [m s-1]
+        z0m        = 0.02   ,  # roughness length for momentum [m]
+        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
+        sw_rad     = True   , # radiation switch
+        lat        = 51.97  ,  # latitude [deg]
+        lon        = -4.93  ,  # longitude [deg]
+        doy        = 268.   ,  # day of the year [-]
+        tstart     = 6.8    ,  # time of the day [h UTC]
+        cc         = 0.0    ,  # cloud cover fraction [-]
+        Q          = 400.   ,  # net radiation [W m-2] 
+        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
+        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
+        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
+        cveg       = 0.85   ,  # vegetation fraction [-]
+        Tsoil      = 295.   ,  # temperature top soil layer [K]
+        Ts         = 295.   ,    # initial surface temperature [K]
+        T2         = 296.   ,  # temperature deeper soil layer [K]
+        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
+        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
+        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
+        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
+        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
+        wfc        = 0.323  ,  # volumetric water content field capacity [-]
+        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
+        C1sat      = 0.132  ,  
+        C2ref      = 1.8    ,
+        LAI        = 2.     ,  # leaf area index [-]
+        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
+        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
+        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
+        alpha      = 0.25   ,  # surface albedo [-]
+        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
+        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
+        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
+        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
+        sw_cu      = False  ,  # Cumulus parameterization switch
+        dz_h       = 150.   ,  # Transition layer thickness [m]
+        cala       = None   ,  # soil heat conductivity [W/(K*m)]
+        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
+        sw_ls      = True   ,
+        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
+        sw_lit     = False,
+        )
+        pars = model_input()
+        for key in defaults:
+            pars.__dict__[key] = defaults[key]
+        
+        self.update(source='defaults',pars=pars)
+        
+    def clear(self):
+        """ this procudure clears the class4gl_input """
+
+        for key in list(self.__dict__.keys()):
+            del(self.__dict__[key])
+        self.__init__()
+
+    def dump(self,file):
+        """ this procedure dumps the class4gl_input object into a yaml file
+            
+            Input: 
+                - self.__dict__ (internal): the dictionary from which we read 
+            Output:
+                - file: All the parameters in self.__init__() are written to
+                the yaml file, including pars, air_ap, sources etc.
+        """
+        file.write('---\n')
+        index = file.tell()
+        file.write('# CLASS4GL input; format version: 0.1\n')
+
+        # write out the position of the current record
+        yaml.dump({'index':index}, file, default_flow_style=False)
+
+        # we do not include the none values
+        for key,data in self.__dict__.items():
+            #if ((type(data) == model_input) or (type(class4gl_input):
+            if key == 'pars':
+
+                pars = {'pars' : self.__dict__['pars'].__dict__}
+                parsout = {}
+                for key in pars.keys():
+                    if pars[key] is not None:
+                        parsout[key] = pars[key]
+
+                yaml.dump(parsout, file, default_flow_style=False)
+            elif type(data) == dict:
+                if key == 'sources':
+                    # in case of sources, we want to have a
+                    # condensed list format as well, so we leave out
+                    # 'default_flow_style=False'
+                    yaml.dump({key : data}, file)
+                else: 
+                    yaml.dump({key : data}, file,
+                              default_flow_style=False)
+            elif type(data) == pd.DataFrame:
+                # in case of dataframes (for profiles), we want to have a
+                # condensed list format as well, so we leave out
+                # 'default_flow_style=False'
+                yaml.dump({key: data.to_dict(orient='list')},file)
+
+                # # these are trials to get it into a more human-readable
+                # fixed-width format, but it is too complex
+                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
+                #file.write(stream)
+                
+                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
+                #file.write(key+': !!str |\n')
+                #file.write(str(data)+'\n')
+       
+    def load_yaml_dict(self,yaml_dict,reset=True):
+        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
+            
+            Input: 
+                - yaml_dict: the dictionary from which we read 
+                - reset: reset data before reading        
+            Output:
+                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
+        """
+        
+        if reset:
+            for key in list(self.__dict__.keys()):
+                del(self.__dict__[key])
+            self.__init__()
+
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                self.__dict__[key] = model_input()
+                self.__dict__[key].__dict__ = data
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            elif key == 'sources':
+                self.__dict__[key] = data
+            elif key == 'diag':
+                self.__dict__[key] = data
+            else: 
+                warnings.warn("Key '"+key+"' may not be implemented.")
+                self.__dict__[key] = data
+
+    def update(self,source,**kwargs):
+        """ this procedure is to make updates of input parameters and tracking
+        of their source more convenient. It implements the assignment of
+        parameter source/sensitivity experiment IDs ('eg.,
+        'defaults', 'sounding balloon', any satellite information, climate
+        models, sensitivity tests etc.). These are all stored in a convenient
+        way with as class4gl_input.sources.  This way, the user can always consult with
+        from where parameters data originates from.  
+        
+        Input:
+            - source:    name of the underlying dataset
+            - **kwargs: a dictionary of data input, for which the key values
+            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
+            the values is a again a dictionary/dataframe of datakeys/columns
+            ('wg','PRES','datetime', ...) and datavalues (either single values,
+            profiles ...), eg., 
+
+                pars = {'wg': 0.007  , 'w2', 0.005}
+                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
+                                     300.,...]}
+            
+        Output:
+            - self.__dict__[datatype] : object to which the parameters are
+                                        assigned. They can be consulted with
+                                        self.pars, self.profiles, etc.
+                                        
+            - self.sources[source] : It supplements the overview overview of
+                                     data sources can be consulted with
+                                     self.sources. The structure is as follows:
+                                     as:
+                self.sources = { 
+                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
+                'GLEAM' :  ['pars:wg','pars:w2', ...],
+                 ...
+                }
+        
+        """
+
+        #print(source,kwargs)
+
+        for key,data in kwargs.items():
+
+            #print(key)
+            # if the key is not in class4gl_input object, then just add it. In
+            # that case, the update procedures below will just overwrite it 
+            if key not in self.__dict__:
+                self.__dict__[key] = data
+
+
+            
+
+            #... we do an additional check to see whether there is a type
+            # match. I not then raise a key error
+            if (type(data) != type(self.__dict__[key]) \
+                # we allow dict input for model_input pars
+                and not ((key == 'pars') and (type(data) == dict) and \
+                (type(self.__dict__[key]) == model_input))):
+
+                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
+
+
+            # This variable keeps track of the added data that is supplemented
+            # by the current source. We add this to class4gl_input.sources
+            datakeys = []
+
+            #... and we update the class4gl_input data, and this depends on the
+            # data type
+
+            if type(self.__dict__[key]) == pd.DataFrame:
+                # If the data type is a dataframe, then we update the columns
+                for column in list(data.columns):
+                    #print(column)
+                    self.__dict__[key][column] = data[column]
+                    datakeys.append(column)
+                    
+
+            elif type(self.__dict__[key]) == model_input:
+                # if the data type is a model_input, then we update its internal
+                # dictionary of parameters
+                if type(data) == model_input:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data.__dict__}
+                    datakeys = list(data.__dict__.keys())
+                elif type(data) == dict:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data}
+                    datakeys = list(data.keys())
+                else:
+                    raise TypeError('input key '+key+' is not of the same type\
+                                    as the one in the class4gl_object')
+
+            elif type(self.__dict__[key]) == dict:
+                # if the data type is a dictionary, we update the
+                # dictionary 
+                self.__dict__[key] = {self.__dict__[key] , data}
+                datakeys = list(data.keys())
+
+
+            # if source entry is not existing yet, we add it
+            if source not in self.sources.keys():
+                self.sources[source] = []
+
+
+            # self.logger.debug('updating section "'+\
+            #                  key+' ('+' '.join(datakeys)+')'\
+            #                  '" from source \
+            #                  "'+source+'"')
+
+            # Update the source dictionary: add the provided data keys to the
+            # specified source list
+            for datakey in datakeys:
+                # At first, remove the occurences of the keys in the other
+                # source lists
+                for sourcekey,sourcelist in self.sources.items():
+                    if key+':'+datakey in sourcelist:
+                        self.sources[sourcekey].remove(key+':'+datakey)
+                # Afterwards, add it to the current source list
+                self.sources[source].append(key+':'+datakey)
+
+
+        # # in case the datatype is a class4gl_input_pars, we update its keys
+        # # according to **kwargs dictionary
+        # if type(self.__dict__[datatype]) == class4gl_input_pars:
+        #     # add the data parameters to the datatype object dictionary of the
+        #     # datatype
+        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
+        #                                        **kwargs}
+        # # in case, the datatype reflects a dataframe, we update the columns according
+        # # to the *args list
+        # elif type(self.__dict__[datatype]) == pd.DataFrame:
+        #     for dataframe in args:
+        #         for column in list(dataframe.columns):
+        #             self.__dict__[datatype][column] = dataframe[column]
+        
+
+    def get_profile(self,IOBJ, *args, **argv):
+        # if type(IOBJ) == wyoming:
+        self.get_profile_wyoming(IOBJ,*args,**argv)
+        # else:
+        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
+        
+    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
+        """ 
+            Purpose: 
+                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
+
+            Input:
+                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
+                function will take the profile at the stream's current
+                position. 
+                2. air_ap_mode: which air profile do we take? 
+                    - b : best
+                    - l : according to lower limit for the mixed-layer height
+                            estimate
+                    - u : according to upper limit for the mixed-layer height
+                            estimate
+
+
+            Output:
+                1. all single-value parameters are stored in the
+                   class4gl_input.pars object
+                2. the souding profiles are stored in the in the
+                   class4gl_input.air_balloon dataframe
+                3. modified sounding profiles for which the mixed layer height
+                   is fitted
+                4. ...
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = 'wyoming'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = air_ap_mode
+
+        """ Temporary variables used for output """
+        # single value parameters derived from the sounding profile
+        dpars = dict()
+        # profile values
+        air_balloon = pd.DataFrame()
+        # fitted profile values
+        air_ap = pd.DataFrame()
+        
+        string = wy_strm.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = wy_strm.current.find_next('pre').find_next('pre').text
+        
+        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
+        dpars = {**dpars,
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+               }
+        
+        # we get weird output when it's a numpy Timestamp, so we convert it to
+        # pd.datetime type
+
+        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
+        dpars['STNID'] = dpars['Station number']
+
+        # altitude above ground level
+        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        # absolute humidity in g/kg
+        air_balloon['q']= (air_balloon.MIXR/1000.) \
+                              / \
+                             (air_balloon.MIXR/1000.+1.)
+        # convert wind speed from knots to m/s
+        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
+        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        
+        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+
+        
+
+        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+
+        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
+        air_balloon['p'] = air_balloon.PRES*100.
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        #is_valid = (air_balloon.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_balloon.index[is_valid].values
+        print(valid_indices)
+
+        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+
+        air_balloon['t'] = air_balloon['TEMP']+273.15
+        air_balloon['theta'] = (air_balloon.t) * \
+                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+
+
+        if np.isnan(dpars['h']):
+            dpars['Ps'] = np.nan
+
+
+
+
+        if ~np.isnan(dpars['h']):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u 
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+            
+
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        air_ap_head['HGHT'] = air_ap_head['z'] \
+                                + \
+                                np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = dpars['theta']
+        z_low =     dpars['h']
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+
+        
+        dpars['lat'] = dpars['Station latitude']
+        dpars['latitude'] = dpars['lat']
+        
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        dpars['longitude'] = dpars['Station longitude']
+        
+        dpars['ldatetime'] = dpars['datetime'] \
+                            + \
+                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+
+        # # we make a pars object that is similar to the destination object
+        # pars = model_input()
+        # for key,value in dpars.items():
+        #     pars.__dict__[key] = value
+
+
+        # we round the columns to a specified decimal, so that we get a clean
+        # output format for yaml
+        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
+                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
+                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+# 
+        for column,decimal in decimals.items():
+            air_balloon[column] = air_balloon[column].round(decimal)
+            air_ap[column] = air_ap[column].round(decimal)
+
+        self.update(source='wyoming',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+
+        
+    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
+    
+        """
+        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
+                 according to the position (lat lon) and the class datetime and timespan
+                 globaldata should be a globaldata multifile object
+        
+        Input: 
+            - globaldata: this is the library object
+            - only_keys: only extract specified keys
+            - exclude_keys: do not inherit specified keys
+        """
+        classdatetime      = np.datetime64(self.pars.datetime_daylight)
+        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
+                                           + \
+                                           dt.timedelta(seconds=self.pars.runtime)\
+                                          )
+
+
+        # # list of variables that we get from global ground data
+        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
+        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
+        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
+        #                 'texture', 'itex', 'isoil', 'BR',
+        #                 'b', 'cveg',
+        #                 'C1sat', 
+        #                 'C2ref', 'p', 'a',
+        #                 ] #globaldata.datasets.keys():
+
+        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
+        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
+
+
+        if type(globaldata) is not data_global:
+            raise TypeError("Wrong type of input library") 
+
+        # by default, we get all dataset keys
+        keys = list(globaldata.datasets.keys())
+
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
+        # # In case there is surface pressure, we also calculate the half-level
+        # # and full-level pressure fields
+        # if ('sp' in keys):
+        #     keys.append('pfull')
+        #     keys.append('phalf')
+
+        # If specified, we only take the keys that are in only_keys
+        if only_keys is not None:
+            for key in keys:
+                if key not in only_keys:
+                    keys.remove(key)
+                
+        # If specified, we take out keys that are in exclude keys
+        if exclude_keys is not None:
+            for key in keys:
+                if key in exclude_keys:
+                    keys.remove(key)
+
+        # we set everything to nan first in the pars section (non-profile parameters
+        # without lev argument), so that we can check afterwards whether the
+        # data is well-fetched or not.
+        for key in keys:
+            if not ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None) and \
+                ('lev' in globaldata.datasets[key].page[key].dims)):
+                self.update(source='globaldata',pars={key:np.nan})
+            # # we do not check profile input for now. We assume it is
+            # # available
+            #else:
+            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
+
+        self.logger.debug('getting keys "'+', '.join(keys)+'\
+                          from global data')
+
+        for key in keys:
+            # If we find it, then we obtain the variables
+            if ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None)):
+
+                # check first whether the dataset has a height coordinate (3d space)
+                if 'lev' in globaldata.datasets[key].page[key].dims:
+
+                    # first, we browse to the correct file that has the current time
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+                        globaldata.datasets[key].browse_page(time=classdatetime)
+
+                    
+                    if (globaldata.datasets[key].page is not None):
+                        # find longitude and latitude coordinates
+                        ilats = (np.abs(globaldata.datasets[key].page.lat -
+                                        self.pars.latitude) < 0.5)
+                        ilons = (np.abs(globaldata.datasets[key].page.lon -
+                                        self.pars.longitude) < 0.5)
+                        
+                        # if we have a time dimension, then we look up the required timesteps during the class simulation
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            itimes = ((globaldata.datasets[key].page.time >= \
+                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            # In case we didn't find any correct time, we take the
+                            # closest one.
+                            if np.sum(itimes) == 0.:
+
+
+                                classdatetimemean = \
+                                    np.datetime64(self.pars.datetime_daylight + \
+                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
+                                                ))
+
+                                dstimes = globaldata.datasets[key].page.time
+                                time = dstimes.sel(time=classdatetimemean,method='nearest')
+                                itimes = (globaldata.datasets[key].page.time ==
+                                          time)
+                                
+                        else:
+                            # we don't have a time coordinate so it doesn't matter
+                            # what itimes is
+                            itimes = 0
+
+                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+
+                        # over which dimensions we take a mean:
+                        dims = globaldata.datasets[key].page[key].dims
+                        namesmean = list(dims)
+                        namesmean.remove('lev')
+                        idxmean = [dims.index(namemean) for namemean in namesmean]
+                        
+                        value = \
+                        globaldata.datasets[key].page[key].isel(time=itimes,
+                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+                        # Ideally, source should be equal to the datakey of globaldata.library 
+                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+                        #  but therefore the globaldata class requires a revision to make this work
+                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+
+                else:
+                    # this procedure is for reading the ground fields (2d space). 
+                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
+
+    
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+    
+                       # first, we browse to the correct file
+                       #print(key)
+                       globaldata.datasets[key].browse_page(time=classdatetime)
+    
+                    if globaldata.datasets[key].page is not None:
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - self.pars.latitude))
+                        ilat = np.where((DIST) == np.min(DIST))[0][0]
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - self.pars.longitude))
+                        ilon = np.where((DIST) == np.min(DIST))[0][0]
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - (self.pars.latitude + 0.5)))
+                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmax = ilat
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - (self.pars.longitude  + 0.5)))
+                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmax = ilon
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lat.values\
+                                - (self.pars.latitude - 0.5)))
+                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmin = ilat
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lon.values\
+                                - (self.pars.longitude  - 0.5)))
+                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmin = ilon        
+                        
+                        if ilatmin < ilatmax:
+                            ilatrange = range(ilatmin,ilatmax+1)
+                        else:
+                            ilatrange = range(ilatmax,ilatmin+1)
+                            
+                        if ilonmin < ilonmax:
+                            ilonrange = range(ilonmin,ilonmax+1)
+                        else:
+                            ilonrange = range(ilonmax,ilonmin+1)     
+                            
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                idatetime += 1
+                            
+                            classdatetimeend = np.datetime64(\
+                                                             self.pars.datetime +\
+                                                             dt.timedelta(seconds=self.pars.runtime)\
+                                                            ) 
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+                            if key in ['wg', 'w2']:
+                                idatetime = idatetime - 1
+                                idatetimeend = idatetimeend - 1
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['Tsoil','T2']:
+                                idatetimeend = idatetime
+                            
+                            idts = range(idatetime,idatetimeend+1)
+                            
+                            count = 0
+                            self.__dict__[key] = 0.
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    for iidts in idts:
+                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
+                                        count += 1
+                            value = value/count
+                            self.update(source='globaldata',pars={key:value.item()})
+                                
+                        else:
+                                
+                            count = 0
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
+                                    count += 1
+                            value = value/count                        
+
+                            self.update(source='globaldata',pars={key:value.item()})
+
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            self.logger.debug('also update LAI based on LAIpixel and cveg') 
+            # I suppose LAI pixel is already determined in the previous
+            # procedure. Anyway...
+            key = 'LAIpixel'
+
+            if globaldata.datasets[key].page is not None:
+                # first, we browse to the correct file that has the current time
+                if 'time' in list(globaldata.datasets[key].page[key].dims):
+                    globaldata.datasets[key].browse_page(time=classdatetime)
+            
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - self.pars.latitude))
+                ilat = np.where((DIST) == np.min(DIST))[0][0]
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - self.pars.longitude))
+                ilon = np.where((DIST) == np.min(DIST))[0][0]
+                 
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude + 0.5)))
+                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmax = ilat
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values \
+                        - (self.pars.longitude  + 0.5)))
+                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmax = ilon
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude - 0.5)))
+                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmin = ilat
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - (self.pars.longitude  - 0.5)))
+                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmin = ilon        
+                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                
+                
+                if ilatmin < ilatmax:
+                    ilatrange = range(ilatmin,ilatmax+1)
+                else:
+                    ilatrange = range(ilatmax,ilatmin+1)
+                    
+                if ilonmin < ilonmax:
+                    ilonrange = range(ilonmin,ilonmax+1)
+                else:
+                    ilonrange = range(ilonmax,ilonmin+1)           
+                
+                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
+                LAIpixel = 0.
+                count = 0
+                for iilat in [ilat]: #ilatrange
+                    for iilon in [ilon]: #ilonrange
+                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
+                        
+                                        
+                        # if np.isnan(tarray[idatetime]):
+                        #     print("interpolating GIMMS LAIpixel nan value")
+                        #     
+                        #     mask = np.isnan(tarray)
+                        #     
+                        #     #replace each nan value with a interpolated value
+                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+                        #         
+                        #     else:
+                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
+                    
+                        #         tarray *= np.nan 
+                        
+                        count += 1
+                        #tarray_res += tarray
+                LAIpixel = LAIpixel/count
+                
+                count = 0
+                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
+  
+                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
+                #print('LAIpixel:',self.__dict__['LAIpixel'])
+                #print('cveg:',self.__dict__['cveg'])
+                
+                # finally, we rescale the LAI according to the vegetation
+                # fraction
+                value = 0. 
+                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
+                   value =self.pars.LAIpixel/self.pars.cveg
+                else:
+                    # in case of small vegetation fraction, we take just a standard 
+                    # LAI value. It doesn't have a big influence anyway for
+                    # small vegetation
+                    value = 2.
+                #print('LAI:',self.__dict__['LAI'])
+                self.update(source='globaldata',pars={'LAI':value}) 
+
+
+        # in case we have 'sp', we also calculate the 3d pressure fields at
+        # full level and half level
+        if ('sp' in keys) and ('sp' in self.pars.__dict__):
+            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
+
+            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # hydrostatic thickness of each model layer
+            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
+            # # dz = rhodz/(R * T / pfull)
+
+
+            # # subsidence multiplied by density. We calculate the subsidence of
+            # # the in class itself
+            # wrho = np.zeros_like(phalf)
+            # wrho[-1] = 0. 
+
+            # for ihlev in range(0,wrho.shape[0]-1):
+            #     # subsidence multiplied by density is the integral of
+            #     # divergences multiplied by the layer thicknessies
+            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
+            #                     self.air_ac['divU_y'][ihlev:]) * \
+            #                    delpdgrav[ihlev:]).sum()
+
+
+            
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'p':list(pfull)}))
+            self.update(source='globaldata',\
+                        air_ach=pd.DataFrame({'p':list(phalf)}))
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
+            # self.update(source='globaldata',\
+            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
+
+    def check_source(self,source,check_only_sections=None):
+        """ this procedure checks whether data of a specified source is valid.
+
+        INPUT:
+            source: the data source we want to check
+            check_only_sections: a string or list with sections to be checked
+        OUTPUT:
+            returns True or False
+        """
+
+        # we set source ok to false as soon as we find a invalid input
+        source_ok = True
+
+        # convert to a single-item list in case of a string
+        check_only_sections_def = (([check_only_sections]) if \
+                                   type(check_only_sections) is str else \
+                                    check_only_sections)
+                                  
+        if source not in self.sources.keys():
+            self.logger.info('Source '+source+' does not exist')
+            source_ok = False
+
+        for sectiondatakey in self.sources[source]:                             
+            section,datakey = sectiondatakey.split(':')                         
+            if ((check_only_sections_def is None) or \
+                (section in check_only_sections_def)):                          
+                checkdatakeys = []
+                if type(self.__dict__[section]) is pd.DataFrame:
+                    checkdata = self.__dict__[section]
+                elif type(self.__dict__[section]) is model_input:
+                    checkdata = self.__dict__[section].__dict__
+
+                if (datakey not in checkdata):                              
+                    # self.logger.info('Expected key '+datakey+\
+                    #                  ' is not in parameter input')                        
+                    source_ok = False                                           
+                elif (checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True):                    
+        
+                    # self.logger.info('Key value of "'+datakey+\
+                    #                  '" is invalid: ('+ \
+                    # str(self.__dict__[section].__dict__[datakey])+')')         
+                    source_ok = False
+
+        return source_ok
+
+    def check_source_globaldata(self):
+        """ this procedure checks whether all global parameter data is
+        available, according to the keys in the self.sources"""
+
+        source_globaldata_ok = True
+
+        #self.get_values_air_input()
+
+        # and now we can get the surface values
+        #class_settings = class4gl_input()
+        #class_settings.set_air_input(input_atm)
+        
+        # we only allow non-polar stations
+        if not (self.pars.lat <= 60.):
+            source_globaldata_ok = False
+            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+        
+        # check lat and lon
+        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
+            source_globaldata_ok = False
+            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+        else:
+            # we only check the ground parameter data (pars section). The 
+            # profile data (air_ap section) are supposed to be valid in any 
+            # case.
+            source_ok = self.check_source(source='globaldata',\
+                                          check_only_sections=['air_ac',\
+                                                               'air_ap',\
+                                                               'pars'])
+            if not source_ok:
+                source_globaldata_ok = False
+        
+            # Additional check: we exclude desert-like
+            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
+                source_globaldata_ok = False
+                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
+                source_globaldata_ok = False
+                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+            elif self.pars.cveg < 0.02:
+                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                source_globaldata_ok = False
+
+        return source_globaldata_ok
+
+
+class c4gli_iterator():
+    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
+    
+        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
+    """
+    def __init__(self,file):
+        # take file as IO stream
+        self.file = file
+        self.yaml_generator = yaml.load_all(file)
+        self.current_dict = {}
+        self.current_class4gl_input = class4gl_input()
+        separator = self.file.readline() # this is just dummy
+        self.header = file.readline()
+        if self.header != '# CLASS4GL record; format version: 0.1\n':
+            raise NotImplementedError("Wrong format version: '"+self.header+"'")
+    def __iter__(self):
+        return self
+    def __next__(self):
+        self.current_dict = self.yaml_generator.__next__()
+        self.current_class4gl_input.load_yaml_dict(self.current_dict)
+        return self.current_class4gl_input
+
+
+
+#get_cape and lift_parcel are adapted from the SkewT package
+    
+class gl_dia(object):
+    def get_lifted_index(self,timestep=-1):
+        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
+    
+#from SkewT
+#def get_lcl(startp,startt,startdp,nsteps=101):
+#    from numpy import interp
+#    #--------------------------------------------------------------------
+#    # Lift a parcel dry adiabatically from startp to LCL.
+#    # Init temp is startt in K, Init dew point is stwrtdp,
+#    # pressure levels are in Pa    
+#    #--------------------------------------------------------------------
+#
+#    assert startdp<=startt
+#
+#    if startdp==startt:
+#        return np.array([startp]),np.array([startt]),np.array([startdp]),
+#
+#    # Pres=linspace(startp,60000.,nsteps)
+#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
+#
+#    # Lift the dry parcel
+#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
+#    # Mixing ratio isopleth
+#    starte=VaporPressure(startdp)
+#    startw=MixRatio(starte,startp)
+#    e=Pres*startw/(.622+startw)
+#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
+#
+#    # Solve for the intersection of these lines (LCL).
+#    # interp requires the x argument (argument 2)
+#    # to be ascending in order!
+#    P_lcl=interp(0.,T_iso-T_dry,Pres)
+#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
+#
+#    # # presdry=linspace(startp,P_lcl)
+#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
+#
+#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
+#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
+#
+#    return P_lcl,T_lcl
+
+
+
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
+
+
+
+#from class
+def get_lcl(startp,startt,startqv):
+        # Find lifting condensation level iteratively
+    lcl = 20.
+    RHlcl = 0.5
+    
+    itmax = 30
+    it = 0
+    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHd
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self):
+       self.status = 'init'
+       self.found = False
+       self.DT = None
+       self.current = None
+       #self.mode = 'b'
+       self.profile_type = 'wyoming'  
+       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+         
+    def set_STNM(self,STNM):
+        self.__init__()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+        self.current = self.sounding_series.find('h2')
+        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+        
+        # go through other files and find first sounding when year is not specified
+        self.iFN=self.iFN+1
+        while keepsearching:
+            self.FN = self.FILES[self.iFN]
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            self.iFN=self.iFN+1
+            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+        self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        if self.found and get_atm:
+            self.get_values_air_input()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        #print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                if get_atm:
+                    self.get_values_air_input()
+                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        if self.found and get_atm:
+            self.get_values_air_input()
+       
+
+
+    def get_values_air_input(self,latitude=None,longitude=None):
+
+        # for iDT,DT in enumerate(DTS):
+        
+            #websource = urllib.request.urlopen(webpage)
+        #soup = BeautifulSoup(open(webpage), "html.parser")
+        
+       
+        #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        BLHV = np.max((BLHV,10.))
+        BLHVu = np.max((BLHVu,10.))
+        BLHVd = np.max((BLHVd,10.))
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+                
+                # get index of lowest valid observation. This seems to vary
+                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+                if len(idxvalid) > 0:
+                    #print('idxvalid',idxvalid)
+                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+                    else:
+                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+                    #print(col,meanabl)
+               
+                
+                # if col == 'PRES':
+                #     meanabl =  
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV',]:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+                        dtheta = np.max((0.1,dtheta_pre))
+                        #meanabl = meanabl - (dtheta - dtheta_pre)
+                        #print('dtheta_pre',dtheta_pre)
+                        #print('dtheta',dtheta)
+                        #print('meanabl',meanabl)
+                        #stop
+                        
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            
+            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+        
+            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+
+        try:
+            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+        except:
+            print("could not convert latitude coordinate")
+            PARAMS.insert(0,'latitude', np.nan)
+            PARAMS.insert(0,'lat', np.nan)
+        try:
+            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+            PARAMS.insert(0,'lon', 0.)
+        except:
+            print("could not convert longitude coordinate")
+            PARAMS.insert(0,'longitude', np.nan)
+            PARAMS.insert(0,'lon', 0.)
+
+        if latitude is not None:
+            print('overwriting latitude with specified value')
+            PARAMS['latitude'] = np.float(latitude)
+            PARAMS['lat'] = np.float(latitude)
+        if longitude is not None:
+            print('overwriting longitude with specified value')
+            PARAMS['longitude'] = np.float(longitude)
+        try:
+            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            # This is the nearest datetime when sun is up (for class)
+            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+            # apply the same time shift for UTC datetime
+            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+            
+        except:
+            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+
+        
+
+        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+        # as we are forcing lon equal to zero this is is expressed in local suntime
+        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
+        elif self.mode == 'b': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNb
+            BLCOLUMN = ONE_COLUMNb
+        elif self.mode == 'u': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNu
+            BLCOLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNd
+            BLCOLUMN = ONE_COLUMNd
+        else:
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb
+
+        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
+        # print(BLCOLUMN['HAGL'][lt6000])
+        # print(BLCOLUMN['HAGL'][lt2500])
+        # 
+        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+
+        #print(BLCOLUMN['HAGL'][lt2500])
+        PARAMS.insert(0,'OK',
+                      ((BLHVe < 200.) and 
+                       ( len(np.where(lt6000)[0]) > 5) and
+                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+                      )
+                     )
+
+        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+        
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = USE_ONECOLUMN
+        # if self.mode == 'o': #original 
+        #     self.ONE_COLUMN = ONE_COLUMN
+        # elif self.mode == 'b': # best BLH
+        #     self.ONE_COLUMN = ONE_COLUMNb
+        # elif self.mode == 'u':# upper BLH
+        #     self.ONE_COLUMN = ONE_COLUMNu
+        # elif self.mode == 'd': # lower BLH
+        #     self.ONE_COLUMN=ONE_COLUMNd
+        # else:
+        #     self.ONE_COLUMN = ONE_COLUMN
+
diff --git a/dist/class4gl-0.1dev/lib/data_global.py b/dist/class4gl-0.1dev/lib/data_global.py
new file mode 100644
index 0000000..9c3d9b5
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/data_global.py
@@ -0,0 +1,936 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: Hendrik Wouters
+
+Purpose: provides class routines for ground and atmosphere conditions used for
+the CLASS miced-layer model
+
+Usage:
+    from data_global import data_global
+    from class4gl import class4gl_input
+    from data_soundings import wyoming
+
+    # create a data_global object and load initial data pages
+    globaldata = data_global()
+    globaldata.load_datasets()
+    # create a class4gl_input object
+    c4gli = class4gl_input()
+    # Initialize it with profile data. We need to do this first. Actually this
+    # will set the coordinate parameters (datetime, latitude, longitude) in
+    # class4gl_input.pars.__dict__, which is required to read point data from
+    # the data_global object.
+
+    # open a Wyoming stream for a specific station
+    wy_strm = wyoming(STNM=91376)
+    # load the first profile
+    wy_strm.find_first()
+    # load the profile data into the class4gl_input object
+    c4gli.get_profile_wyoming(wy_strm)
+    
+    # and finally, read the global input data for this profile
+    c4gli.get_global_input(globaldata)
+
+
+"""
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+#import pynacolada as pcd
+import pandas as pd
+import xarray as xr
+import os
+import glob
+import sys
+import errno
+import warnings
+import logging
+
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+class book(object):
+    """ this is a class for a dataset spread over multiple files. It has a
+    similar purpose  open_mfdataset, but only 1 file (called current 'page')
+    one is loaded at a time. This saves precious memory.  """
+    def __init__(self,fn,concat_dim = None,debug_level=None):
+        self.logger = logging.getLogger('book')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # filenames are expanded as a list and sorted by filename
+        self.pages = glob.glob(fn); self.pages.sort()
+        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
+        if len(self.pages) == 0:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
+        self.ipage = -1; self.page = None
+        self.renames = {} # each time when opening a file, a renaming should be done.
+        self.set_page(0)
+
+        # we consider that the outer dimension is the one we concatenate
+        self.concat_dim = concat_dim
+        if self.concat_dim is None:
+            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
+
+    # this wraps the xarray sel-commmand
+    def sel(*args, **kwargs):
+        for dim in kwargs.keys():
+            if dim == self.concat_dim:
+                self.browse_page(**{dim: kwargs[dim]})
+        return page.sel(*args,**kwargs)
+
+
+    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
+    #def __getattr__(self,attr):
+    #    orig_attr = self.page.__getattribute__(attr)
+    #    if callable(orig_attr):
+    #        def hooked(*args, **kwargs):
+    #            for dim in kwargs.keys():
+    #                if dim == self.concat_dim:
+    #                    self.browse_page(**{dim: kwargs[dim]})
+    #
+    #            result = orig_attr(*args, **kwargs)
+    #            # prevent wrapped_class from becoming unwrapped
+    #            if result == self.page:
+    #                return self
+    #            self.post()
+    #            return result
+    #        return hooked
+    #    else:
+    #        return orig_attr
+
+    def set_renames(self,renames):
+        #first, we convert back to original names, and afterwards, we apply the update of the renames.
+        reverse_renames = dict((v,k) for k,v in self.renames.items())
+        self.renames = renames
+        if self.page is not None:
+            self.page = self.page.rename(reverse_renames)
+            self.page = self.page.rename(self.renames)
+
+    def set_page(self,ipage,page=None):
+        """ this sets the right page according to ipage:
+                - We do not switch the page if we are already at the right one
+                - we set the correct renamings (level -> lev, latitude -> lat,
+                etc.)
+                - The dataset is also squeezed.
+        """
+
+        if ((ipage != self.ipage) or (page is not None)):
+
+            if self.page is not None:
+                self.page.close()
+
+            self.ipage = ipage
+            if page is not None:
+                self.page = page
+            else:
+                if self.ipage == -1:
+                   self.page = None
+                else:
+                    #try:
+
+                    self.logger.info("Switching to page "+str(self.ipage)+': '\
+                                     +self.pages[self.ipage])
+                    self.page = xr.open_dataset(self.pages[self.ipage])
+
+
+            # do some final corrections to the dataset to make them uniform
+            if self.page is not None:
+               if 'latitude' in self.page.dims:
+#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
+               if 'level' in self.page.dims:
+                   self.page = self.page.rename({'level':'lev'})
+
+               self.page = self.page.rename(self.renames)
+               self.page = self.page.squeeze(drop=True)
+
+    def browse_page(self,rewind=2,**args):
+
+        # at the moment, this is only tested with files that are stacked according to the time dimension.
+        dims = args.keys()
+
+
+        if self.ipage == -1:
+            self.set_page(0)
+
+        found = False
+        iipage = 0
+        startipage = self.ipage - rewind
+        while (iipage < len(self.pages)) and not found:
+            ipage = (iipage+startipage) % len(self.pages)
+            for dim in args.keys():
+                this_file = True
+
+                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
+                if 'dims' not in self.__dict__:
+                    self.dims = {}
+                if dim not in self.dims.keys():
+                    self.dims[dim] = [None]*len(self.pages)
+
+                if self.dims[dim][ipage] is None:
+                    self.logger.info('Loading coordinates of dimension "'+dim+\
+                                     '" of page "' +str(ipage)+'".')
+                    self.set_page(ipage)
+                    # print(ipage)
+                    # print(dim)
+                    # print(dim,self.page[dim].values)
+                    self.dims[dim][ipage] = self.page[dim].values
+
+                # determine current time range of the current page
+                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
+                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
+
+                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
+                    this_file = False
+
+            if this_file:
+                found = True
+                self.set_page(ipage)
+            else:
+
+                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
+                #    iipage = len(self.pages) # we stop searching
+
+                iipage += 1
+
+        if not found:
+            self.logger.info("Page not found. Setting to page -1")
+            #iipage = len(self.pages) # we stop searching further
+            self.set_page(-1)
+
+        if self.ipage != -1:
+            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
+        else:
+            self.logger.debug("I'm now at page "+ str(self.ipage))
+
+
+class data_global(object):
+    def __init__(self,sources= {
+        # # old gleam
+        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
+        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
+        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
+        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
+        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
+        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
+        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
+        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
+        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
+        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
+        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
+        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
+        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
+        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
+        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
+        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
+        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
+        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
+        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
+        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
+        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
+        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
+        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
+        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
+        },debug_level=None):
+        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
+        self.sources = sources
+        self.datarefs = {}
+        self.datasets = {}
+        self.datetime = dt.datetime(1981,1,1)
+
+        self.logger = logging.getLogger('data_global')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+        self.debug_level = debug_level
+
+        warnings.warn('omitting pressure field p and advection')
+
+    def in_library(self,fn):
+        if fn not in self.library.keys():
+            return False
+        else:
+            print("Warning: "+fn+" is already in the library.")
+            return True
+
+    def add_to_library(self,fn):
+        if not self.in_library(fn):
+            print("opening: "+fn)
+            self.library[fn] = \
+                book(fn,concat_dim='time',debug_level=self.debug_level)
+
+            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
+            #if 'latitude' in self.library[fn].variables:
+            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+
+    # default procedure for loading datasets into the globaldata library
+    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
+        if type(varssource) is str:
+            varssource = [varssource]
+        if type(varsdest) is str:
+            varsdest = [varsdest]
+
+        self.add_to_library(input_fn)
+
+        if varssource is None:
+            varssource = []
+            for var in self.sources[input_fn].variables:
+                avoid = \
+                ['lat','lon','latitude','longitude','time','lev','level']
+                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
+                    varssource.append(var)
+
+        if varsdest is None:
+            varsdest = varssource
+
+        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        for ivar,vardest in enumerate(varsdest):
+            varsource = varssource[ivar]
+            print('setting '+vardest+' as '+varsource+' from '+input_fn)
+
+            if vardest in self.datarefs.keys():
+                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
+            #self.add_to_library(fn,varsource,vardest)
+            if vardest != varsource:
+                libkey = input_fn+'.'+varsource+'.'+vardest
+                if libkey not in self.library.keys():
+                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
+                    self.library[libkey] = book(input_fn,\
+                                                debug_level=self.debug_level)
+                    self.library[libkey].set_renames({varsource: vardest})
+
+                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+            else:
+                self.datarefs[vardest] = input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+
+            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
+            #     print('Warning: '+ vardest "not in " + input_fn)
+
+
+
+    def load_datasets(self,sources = None,recalc=0):
+
+        if sources is None:
+            sources = self.sources
+        for key in sources.keys():
+            #datakey,vardest,*args = key.split(':')
+            datakey,vardest = key.split(':')
+            #print(datakey)
+
+            fnvarsource = sources[key].split(':')
+            if len(fnvarsource) > 2:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource,fnargs = fnvarsource
+                fnargs = [fnargs]
+            elif len(fnvarsource) > 1:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource = fnvarsource
+                fnargs = []
+            else:
+                fn = sources[key]
+                varsource = vardest
+            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
+
+    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
+            # the default way of loading a 2d dataset
+            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
+                self.load_dataset_default(fn,varsource,vardest)
+            elif datakey == 'IGBPDIS':
+                if vardest == 'alpha':
+                    ltypes = ['W','B','H','TC']
+                    for ltype in ltypes:
+                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
+                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
+
+
+                    # landfr = {}
+                    # for ltype in ['W','B','H','TC']:
+                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
+
+
+
+                    keytemp = 'alpha'
+                    fnkeytemp = fn+':IGBPDIS:alpha'
+                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
+                        self.library[fnkeytemp]  = book(fnkeytemp,
+                                                        debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+                    else:
+                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
+                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
+                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
+                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
+                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
+                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
+                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
+
+                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+
+                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
+                        for ltype in ltypes:
+                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
+
+                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
+                        print('writing file to: '+fnkeytemp)
+                        os.system('rm '+fnkeytemp)
+                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
+                        self.library[fnkeytemp].close()
+
+
+                        self.library[fnkeytemp]  = \
+                            book(fnkeytemp,debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+
+
+                else:
+                    self.load_dataset_default(fn,varsource,vardest)
+
+
+            elif datakey == 'GLAS':
+                self.load_dataset_default(fn,varsource,vardest)
+                if vardest == 'z0m':
+                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
+                elif vardest == 'z0h':
+                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
+            elif datakey == 'DSMW':
+
+
+                # Procedure of the thermal properties:
+                # 1. determine soil texture from DSMW/10.
+                # 2. soil type with look-up table (according to DWD/EXTPAR)
+                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
+                #    with parameter look-up table from Noilhan and Planton (1989).
+                #    Note: The look-up table is inspired on DWD/COSMO
+
+                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
+
+
+
+                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
+                self.load_dataset_default(fn,'DSMW')
+                print('calculating texture')
+                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
+                TEMP  = {}
+                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
+                TEMP3 = {}
+                for SPKEY in SPKEYS:
+
+
+                    keytemp = SPKEY+'_values'
+                    fnoutkeytemp = fnout+':DSMW:'+keytemp
+                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                    else:
+                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
+                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
+                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+                        # for faster computation, we need to get it to memory out of Dask.
+                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
+                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
+
+                # yes, I know I only check the last file.
+                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
+                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
+                        print('idx',idx,SPKEY)
+                        SEL = (TEMP2 == idx)
+                    #     print(idx,len(TEMP3))
+                        for SPKEY in SPKEYS:
+                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
+
+                    for SPKEY in SPKEYS:
+                        keytemp = SPKEY+'_values'
+                        fnoutkeytemp = fnout+':DSMW:'+keytemp
+                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
+                        os.system('rm '+fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].close()
+
+
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                keytemp = 'texture'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+                else:
+                    self.library[fn+':DSMW:texture'] = xr.Dataset()
+                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
+                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
+                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
+                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+
+                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
+
+                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
+                    zundef[zundef < 0] = np.nan
+                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
+                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
+
+                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+
+
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+                print('calculating texture type')
+
+
+
+                keytemp = 'itex'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+                else:
+                    self.library[fnoutkeytemp] = xr.Dataset()
+                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+                    X = self.datasets['texture'].page['texture'].values*100
+                    X[pd.isnull(X)] = -9
+
+
+                    self.datasets[keytemp][keytemp].values = X
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
+                    self.datasets['itex'].close()
+
+
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+
+                keytemp = 'isoil'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                isoil_reprocessed = False
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+                else:
+                    isoil_reprocessed = True
+                    print('calculating soil type')
+                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    ITEX = self.datasets['itex'].page['itex'].values
+                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
+                    LOOKUP = [
+                              [-10 ,9],# ocean
+                              [0 ,7],# fine textured, clay (soil type 7)
+                              [20,6],# medium to fine textured, loamy clay (soil type 6)
+                              [40,5],# medium textured, loam (soil type 5)
+                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                              [80,3],# coarse textured, sand (soil type 3)
+                              [100,9],# coarse textured, sand (soil type 3)
+                            ]
+                    for iitex,iisoil in LOOKUP:
+                        ISOIL[ITEX > iitex] = iisoil
+                        print('iitex,iisoil',iitex,iisoil)
+
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    LOOKUP = [
+                              [9001, 1 ], # ice, glacier (soil type 1)
+                              [9002, 2 ], # rock, lithosols (soil type 2)
+                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                              [9,    9 ], # undefined (ocean)
+                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                              [9000, 9 ], # undefined (inland lake)
+                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                            ]
+                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
+
+                    CODE_VALUES[ITEX == 901200] = 9012
+                    for icode,iisoil in LOOKUP:
+                        ISOIL[CODE_VALUES == icode] = iisoil
+
+                    self.datasets['isoil']['isoil'].values = ISOIL
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+                    print('saved inbetween file to: '+fnoutkeytemp)
+
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                #adopted from data_soil.f90 (COSMO5.0)
+                SP_LOOKUP = {
+                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
+                  # (by index)                                           loam                    loam                                water      ice
+                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+                  # Important note: For peat, the unknown values below are set equal to that of loam
+                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
+                  #error in table 2 of NP89: values need to be multiplied by e-6
+                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
+
+                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
+                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
+                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
+                }
+
+
+                # isoil_reprocessed = False
+                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+
+                #     self.library[fn+':DSMW:isoil'] = \
+                #             book(fnoutkeytemp,debug_level=self.debug_level)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+                # else:
+                #     isoil_reprocessed = True
+                #     print('calculating soil type')
+                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+
+
+
+                # this should become cleaner in future but let's hard code it for now.
+                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
+                print('calculating soil parameter')
+                DATATEMPSPKEY = {}
+                if (recalc < 1) and (isoil_reprocessed == False): 
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        keytemp = SPKEY
+                        fnoutkeytemp=fnout+':DSMW:'+keytemp
+                        self.library[fn+':DSMW:'+SPKEY] =\
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
+                        self.datarefs[SPKEY] =fnoutkeytemp
+                else:
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+
+                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
+                    ISOIL = self.datasets['isoil'].page['isoil'].values
+                    print(np.where(ISOIL>0.))
+                    for i in range(11):
+                        SELECT = (ISOIL == i)
+                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
+
+                        os.system('rm '+fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].close()
+                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
+
+                        self.library[fn+':DSMW:'+SPKEY] = \
+                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+
+
+            else:
+                self.load_dataset_default(fn,varsource,vardest)
+
+
+
+
+
+
+#
+#                 # only print the last parameter value in the plot
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'cala'
+#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'crhoc'
+#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#     key = "CERES"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         CERES_start_date = dt.datetime(2000,3,1)
+#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+#
+#         var = 'cc'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#         print(class_settings.lat,class_settings.lon)
+#
+#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
+#
+#         input_nc.close()
+#
+
+
+#     key = "GIMMS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+#         print("Reading Leag Area Index from "+input_fn)
+#         var = 'LAI'
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+#
+#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+#
+#         if np.isnan(tarray[idatetime]):
+#             print("interpolating GIMMS cveg nan value")
+#
+#             mask = np.isnan(tarray)
+#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+#             else:
+#                 print("Warning. Could not interpolate GIMMS cveg nan value")
+#
+#         class_settings.__dict__[var] = tarray[idatetime]
+#
+#         input_nc.close()
+#
+#     key = "IGBPDIS_ALPHA"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         var = 'alpha'
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+#         print("Reading albedo from "+input_fn)
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#
+#         landfr = {}
+#         for ltype in ['W','B','H','TC']:
+#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+#
+#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+#
+#         alpha=0.
+#         for ltype in landfr.keys():
+#             alpha += landfr[ltype]*aweights[ltype]
+#
+#
+#         class_settings.__dict__[var] = alpha
+#         input_nc.close()
+#
+#
+#     key = "ERAINT_ST"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         print("Reading soil temperature from "+input_fn)
+#
+#         var = 'Tsoil'
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         var = 'T2'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+#
+#
+#         input_nc.close()
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #var = 'T2'
+#     #valold = class_settings.__dict__[var]
+#     #
+#     #class_settings.__dict__[var] = 305.
+#     #class_settings.__dict__['Tsoil'] = 302.
+#     #valnew = class_settings.__dict__[var]
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #
+#     #var = 'Lambda'
+#     #valold = class_settings.__dict__[var]
+#
+#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
+#     ## I need to ask Chiel.
+#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+#     #
+#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
+#     #class_settings.__dict__[var] = valnew
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     key = "GLAS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+#         print("Reading canopy height for determining roughness length from "+input_fn)
+#         var = 'z0m'
+#
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+#
+#         lowerlimit = 0.01
+#         if testval < lowerlimit:
+#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+#             class_settings.__dict__[var] = lowerlimit
+#         else:
+#             class_settings.__dict__[var] = testval
+#
+#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+#
+#
+#         input_nc.close()
+
+
+
+
+
diff --git a/dist/class4gl-0.1dev/lib/interface_functions.py b/dist/class4gl-0.1dev/lib/interface_functions.py
new file mode 100644
index 0000000..3e483f3
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/interface_functions.py
@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+#from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+#'_afternoon.yaml'
+def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+    filename = yaml_file.name
+    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+    #yaml_file = open(filename)
+
+    #print('going to next observation',filename)
+    yaml_file.seek(index_start)
+
+    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+
+    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer.write(buf)
+    filebuffer.close()
+    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+
+    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    print(command)
+    os.system(command)
+    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    record_dict = json.load(jsonstream)
+    jsonstream.close()
+    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+
+
+    if mode =='mod':
+        modelout = class4gl()
+        modelout.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        return modelout
+    elif mode == 'ini':
+
+ 
+        # datetimes are incorrectly converted to strings. We need to convert them
+        # again to datetimes
+        for key,value in record_dict['pars'].items():
+            # we don't want the key with columns that have none values
+            if value is not None: 
+                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+               # elif (type(value) == str):
+                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
+            if (value == 0.9e19) or (value == '.9e19'):
+                record_dict['pars'][key] = np.nan
+        for key in record_dict.keys():
+            #print(key)
+            if key in ['air_ap','air_balloon',]:
+                #NNprint('check')
+                for datakey,datavalue in record_dict[key].items():
+                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+
+        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        c4gli = class4gl_input()
+        print(c4gli.logger,'hello')
+        c4gli.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+
+class stations(object):
+    def __init__(self,path,suffix='ini',refetch_stations=False):
+
+        self.path = path
+
+        self.file = self.path+'/stations_list.csv'
+        if (os.path.isfile(self.file)) and (not refetch_stations):
+            self.table = pd.read_csv(self.file)
+        else:
+            self.table = self.get_stations(suffix=suffix)
+            self.table.to_csv(self.file)
+        
+        self.table = self.table.set_index('STNID')
+        #print(self.table)
+
+    def get_stations(self,suffix):
+        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        if len(stations_list_files) == 0:
+            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        stations_list_files.sort()
+        print(stations_list_files)
+        if len(stations_list_files) == 0:
+            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
+        stations_list = []
+        for stations_list_file in stations_list_files:
+            thisfile = open(stations_list_file,'r')
+            yamlgen = yaml.load_all(thisfile)
+            try:
+                first_record  = yamlgen.__next__()
+            except:
+                first_record = None
+            if first_record is not None:
+                stations_list.append({})
+                for column in ['STNID','latitude','longitude']:
+                    #print(first_record['pars'].keys())
+                    stations_list[-1][column] = first_record['pars'][column]
+                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
+            yamlgen.close()
+            thisfile.close()
+    
+        print(stations_list)
+        return pd.DataFrame(stations_list)
+
+class stations_iterator(object):
+    def __init__(self,stations):
+        self.stations = stations
+        self.ix = -1 
+    def __iter__(self):
+        return self
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.stations.table)) 
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_row(self,row):
+        self.ix = row
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_STNID(self,STNID):
+        self.ix = np.where((self.stations.table.index == STNID))[0][0]
+        print(self.ix)
+        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+    def close():
+        del(self.ix)
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.records))
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+# #'_afternoon.yaml'
+# def get_record_yaml(yaml_file,index_start,index_end):
+#     filename = yaml_file.name
+#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+#     #yaml_file = open(filename)
+# 
+#     #print('going to next observation',filename)
+#     yaml_file.seek(index_start)
+# 
+#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+# 
+#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+#     filebuffer.write(buf)
+#     filebuffer.close()
+#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+#     
+#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+# 
+#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+#     print(command)
+#     os.system(command)
+#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
+#     record_dict = json.load(jsonstream)
+#     jsonstream.close()
+#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+#  
+#     # datetimes are incorrectly converted to strings. We need to convert them
+#     # again to datetimes
+#     for key,value in record_dict['pars'].items():
+#         # we don't want the key with columns that have none values
+#         if value is not None: 
+#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
+#            # elif (type(value) == str):
+#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+#                 
+#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
+# 
+#         if (value == 0.9e19) or (value == '.9e19'):
+#             record_dict['pars'][key] = np.nan
+#     for key in record_dict.keys():
+#         print(key)
+#         if key in ['air_ap','air_balloon',]:
+#             print('check')
+#             for datakey,datavalue in record_dict[key].items():
+#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+# 
+#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+# 
+#     c4gli = class4gl_input()
+#     c4gli.load_yaml_dict(record_dict)
+#     return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
+
+    records = pd.DataFrame()
+    for STNID,station in stations.iterrows():
+        dictfnchunks = []
+        if getchunk is 'all':
+
+            # we try the old single-chunk filename format first (usually for
+            # original profile pairs)
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(fn):
+                chunk = 0
+                dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+            # otherwise, we use the new multi-chunk filename format
+            else:
+                chunk = 0
+                end_of_chunks = False
+                while not end_of_chunks:
+                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(fn):
+                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    else:
+                        end_of_chunks = True
+                    chunk += 1
+
+            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
+            # yamlfilenames = glob.glob(globyamlfilenames)
+            # yamlfilenames.sort()
+        else:
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
+            
+        if len(dictfnchunks) > 0:
+            for dictfnchunk in dictfnchunks:
+                yamlfilename = dictfnchunk['fn']
+                chunk = dictfnchunk['chunk']
+                print(chunk)
+
+                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                generate_pkl = False
+                if not os.path.isfile(pklfilename): 
+                    print('pkl file does not exist. I generate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                elif not (os.path.getmtime(yamlfilename) <  \
+                    os.path.getmtime(pklfilename)):
+                    print('pkl file older than yaml file, so I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+
+                if refetch_records:
+                    print('refetch_records flag is True. I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                if not generate_pkl:
+                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                   # irecord = 0
+                else:
+                    with open(yamlfilename) as yaml_file:
+
+                        dictout = {}
+
+                        next_record_found = False
+                        end_of_file = False
+                        while (not next_record_found) and (not end_of_file):
+                            linebuffer = yaml_file.readline()
+                            next_record_found = (linebuffer == '---\n')
+                            end_of_file = (linebuffer == '')
+                        next_tell = yaml_file.tell()
+                        
+                        while not end_of_file:
+
+                            print(' next record:',next_tell)
+                            current_tell = next_tell
+                            next_record_found = False
+                            yaml_file.seek(current_tell)
+                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            linebuffer = ''
+                            while ( (not next_record_found) and (not end_of_file)):
+                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                linebuffer = yaml_file.readline()
+                                next_record_found = (linebuffer == '---\n')
+                                end_of_file = (linebuffer == '')
+                            filebuffer.close()
+                            
+                            next_tell = yaml_file.tell()
+                            index_start = current_tell
+                            index_end = next_tell
+
+                            
+                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            print(command)
+                            
+                            os.system(command)
+                            #jsonoutput = subprocess.check_output(command,shell=True) 
+                            #print(jsonoutput)
+                            #jsonstream = io.StringIO(jsonoutput)
+                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            record = json.load(jsonstream)
+                            dictouttemp = {}
+                            for key,value in record['pars'].items():
+                                # we don't want the key with columns that have none values
+                                if value is not None: 
+                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                   if (type(value) in regular_numeric_types):
+                                        dictouttemp[key] = value
+                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                            recordindex = record['index']
+                            dictouttemp['chunk'] = chunk
+                            dictouttemp['index_start'] = index_start
+                            dictouttemp['index_end'] = index_end
+                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            for key,value in dictouttemp.items():
+                                if key not in dictout.keys():
+                                    dictout[key] = {}
+                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                            print(' obs record registered')
+                            jsonstream.close()
+                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                    records_station = pd.DataFrame.from_dict(dictout)
+                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
+                    print('writing table file ('+pklfilename+') for station '\
+                          +str(STNID))
+                    records_station.to_pickle(pklfilename)
+                    # else:
+                    #     os.system('rm '+pklfilename)
+                    records = pd.concat([records,records_station])
+    return records
+
+def stdrel(mod,obs,columns):
+    stdrel = pd.DataFrame(columns = columns)
+    for column in columns:
+        stdrel[column] = \
+                (mod.groupby('STNID')[column].transform('mean') -
+                 obs.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') + \
+                (mod[column] -
+                 mod.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') 
+    return stdrel
+
+def pct(obs,columns):
+    pct = pd.DataFrame(columns=columns)
+    for column in columns:
+        #print(column)
+        pct[column] = ""
+        pct[column] = obs[column].rank(pct=True)
+    return pct
+
+def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (obs_afternoon.ldatetime - \
+                               obs_morning.ldatetime).dt.seconds*3600.
+    return stats
+
diff --git a/dist/class4gl-0.1dev/lib/interface_multi.py b/dist/class4gl-0.1dev/lib/interface_multi.py
new file mode 100644
index 0000000..83148e5
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/interface_multi.py
@@ -0,0 +1,2061 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+# from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+cdictpres = {'blue': (\
+                   (0.,    0.,  0.),
+                   (0.25,  0.25, 0.25),
+                   (0.5,  .70, 0.70),
+                   (0.75, 1.0, 1.0),
+                   (1,     1.,  1.),
+                   ),
+       'green': (\
+                   (0. ,   0., 0.0),
+                   (0.25,  0.50, 0.50),
+                   (0.5,  .70, 0.70),
+                   (0.75,  0.50, 0.50),
+                   (1  ,    0,  0.),
+                   ),
+       'red':  (\
+                  (0 ,  1.0, 1.0),
+                  (0.25 ,  1.0, 1.0),
+                   (0.5,  .70, 0.70),
+                  (0.75 , 0.25, 0.25),
+                  (1,    0., 0.),
+                  )}
+
+statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+os.system('module load Ruby')
+
+class c4gl_interface_soundings(object):
+    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+        """ creates an interactive interface for analysing class4gl experiments
+
+        INPUT:
+            path_exp : path of the experiment output
+            path_obs : path of the observations 
+            globaldata: global data that is being shown on the map
+            refetch_stations: do we need to build the list of the stations again?
+        OUTPUT:
+            the procedure returns an interface object with interactive plots
+
+        """
+        
+        # set the ground
+        self.globaldata = globaldata
+
+ 
+        self.path_exp = path_exp
+        self.path_obs = path_obs
+        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
+
+        # # get the list of stations
+        # stationsfile = self.path_exp+'/stations_list.csv'
+        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
+        #     stations = pd.read_csv(stationsfile)
+        # else:
+        #     stations = get_stations(self.path_exp)
+        #     stations.to_csv(stationsfile)
+
+        # stations = stations.set_index('STNID')
+
+        self.frames = {}
+
+        self.frames['stats'] = {}
+        self.frames['worldmap'] = {}
+                
+        self.frames['profiles'] = {}
+        self.frames['profiles'] = {}
+        self.frames['profiles']['DT'] = None
+        self.frames['profiles']['STNID'] = None
+
+        #self.frames['worldmap']['stationsfile'] = stationsfile
+        self.frames['worldmap']['stations'] = stations(self.path_exp, \
+                                                       suffix='ini',\
+                                                       refetch_stations=refetch_stations)
+
+        # Initially, the stats frame inherets the values/iterators of
+        # worldmap
+        for key in self.frames['worldmap'].keys():
+            self.frames['stats'][key] = self.frames['worldmap'][key]
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_ini'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='ini',\
+                                           refetch_records=refetch_records
+                                           )
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_mod'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='mod',\
+                                           refetch_records=refetch_records
+                                           )
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_obs,\
+                                           subset='afternoon',\
+                                           refetch_records=refetch_records
+                                           )
+
+        self.frames['stats']['records_all_stations_mod'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['records_all_stations_ini']['dates'] = \
+            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+
+
+        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+
+        self.frames['stats']['records_all_stations_obs_afternoon'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['viewkeys'] = ['h','theta','q']
+        print('Calculating table statistics')
+        self.frames['stats']['records_all_stations_mod_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_mod'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+
+        self.frames['stats']['inputkeys'] = inputkeys
+        
+        # self.frames['stats']['inputkeys'] = \
+        #     [ key for key in \
+        #       self.globaldata.datasets.keys() \
+        #       if key in \
+        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
+
+
+        # get units from the class4gl units database
+        self.units = dict(units)
+        # for those that don't have a definition yet, we just ask a question
+        # mark
+        for var in self.frames['stats']['inputkeys']:
+            self.units[var] = '?'
+
+        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
+        self.frames['stats']['records_all_stations_ini_pct'] = \
+                  pct(self.frames['stats']['records_all_stations_ini'], \
+                      columns = self.frames['stats']['inputkeys'])
+
+        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
+        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+        #     mod['
+
+        # 
+        # 
+        # \
+        #        self.frames['stats']['records_all_stations_mod'], \
+
+
+
+        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        #               columns = [ 'd'+key+'dt' for key in \
+        #                           self.frames['stats']['viewkeys']], \
+        #              )
+
+        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
+        #               obs = self.frames['stats']['records_all_stations_ini'], \
+        #               columns = self.frames['stats']['viewkeys'], \
+        #              )
+        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+        
+        print('filtering pathological data')
+        # some observational sounding still seem problematic, which needs to be
+        # investigated. In the meantime, we filter them
+        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+        # we filter ALL data frames!!!
+        for key in self.frames['stats'].keys():
+            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+               (self.frames['stats'][key].index.names == indextype):
+                self.frames['stats'][key] = self.frames['stats'][key][valid]
+        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+
+
+        print("filtering stations from interface that have no records")
+        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
+            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                    == STNID).sum() == 0):
+                print("dropping", STNID)
+                self.frames['worldmap']['stations'].table = \
+                        self.frames['worldmap']['stations'].table.drop(STNID)
+                    
+        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+        
+        # TO TEST: should be removed, since it's is also done just below
+        self.frames['stats']['stations_iterator'] = \
+            self.frames['worldmap']['stations_iterator'] 
+
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
+        self.next_station()
+
+        # self.goto_datetime_worldmap(
+        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+        #     'after')
+    def sel_station(self,STNID=None,rownumber=None):
+
+        if (STNID is not None) and (rownumber is not None):
+            raise ValueError('Please provide either STNID or rownumber, not both.')
+
+        if (STNID is None) and (rownumber is None):
+            raise ValueError('Please provide either STNID or rownumber.')
+            
+        if STNID is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
+            print(
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+            )
+            self.update_station()
+        elif rownumber is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
+            self.update_station()
+
+
+
+    def next_station(self,event=None,jump=1):
+        with suppress(StopIteration):
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+                = self.frames['worldmap']['stations_iterator'].__next__(jump)
+            # self.frames['worldmap']['stations_iterator'].close()
+            # del(self.frames['worldmap']['stations_iterator'])
+            # self.frames['worldmap']['stations_iterator'] = \
+            #                 selfself.frames['worldmap']['stations'].iterrows()
+            # self.frames['worldmap']['STNID'],\
+            # self.frames['worldmap']['current_station'] \
+            #     = self.frames['worldmap']['stations_iterator'].__next__()
+
+        self.update_station()
+
+    def prev_station(self,event=None):
+        self.next_station(jump = -1,event=event)
+    def update_station(self):
+        for key in ['STNID','current_station','stations_iterator']: 
+            self.frames['stats'][key] = self.frames['worldmap'][key] 
+
+
+
+        # generate index of the current station
+        self.frames['stats']['records_current_station_index'] = \
+            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+             == \
+             self.frames['stats']['current_station'].name)
+
+        # create the value table of the records of the current station
+        tab_suffixes = \
+                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        for tab_suffix in tab_suffixes:
+            self.frames['stats']['records_current_station'+tab_suffix] = \
+                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+        # go to first record of current station
+        self.frames['stats']['records_iterator'] = \
+                        records_iterator(self.frames['stats']['records_current_station_mod'])
+        (self.frames['stats']['STNID'] , \
+        self.frames['stats']['current_record_chunk'] , \
+        self.frames['stats']['current_record_index']) , \
+        self.frames['stats']['current_record_mod'] = \
+                        self.frames['stats']['records_iterator'].__next__()
+
+        for key in self.frames['stats'].keys():
+            self.frames['profiles'][key] = self.frames['stats'][key]
+
+        STNID = self.frames['profiles']['STNID']
+        chunk = self.frames['profiles']['current_record_chunk']
+        if 'current_station_file_ini' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_ini'].close()
+        self.frames['profiles']['current_station_file_ini'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+        if 'current_station_file_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_mod'].close()
+        self.frames['profiles']['current_station_file_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_afternoon'].close()
+        self.frames['profiles']['current_station_file_afternoon'] = \
+            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+        self.frames['profiles']['records_iterator'] = \
+                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+        (self.frames['profiles']['STNID'] , \
+        self.frames['profiles']['current_record_chunk'] , \
+        self.frames['profiles']['current_record_index']) , \
+        self.frames['profiles']['current_record_mod'] = \
+                        self.frames['profiles']['records_iterator'].__next__()
+
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+
+        self.update_record()
+
+    def next_record(self,event=None,jump=1):
+        with suppress(StopIteration):
+            (self.frames['profiles']['STNID'] , \
+            self.frames['profiles']['current_record_chunk'] , \
+            self.frames['profiles']['current_record_index']) , \
+            self.frames['profiles']['current_record_mod'] = \
+                      self.frames['profiles']['records_iterator'].__next__(jump)
+        # except (StopIteration):
+        #     self.frames['profiles']['records_iterator'].close()
+        #     del( self.frames['profiles']['records_iterator'])
+        #     self.frames['profiles']['records_iterator'] = \
+        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #     (self.frames['profiles']['STNID'] , \
+        #     self.frames['profiles']['current_record_index']) , \
+        #     self.frames['profiles']['current_record_mod'] = \
+        #                     self.frames['profiles']['records_iterator'].__next__()
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        self.update_record()
+
+    def prev_record(self,event=None):
+        self.next_record(jump=-1,event=event)
+
+    def update_record(self):
+        self.frames['profiles']['current_record_ini'] =  \
+            self.frames['profiles']['records_current_station_ini'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'],\
+                  self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon'] =  \
+            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'] , \
+                  self.frames['profiles']['current_record_index'])]
+
+        self.frames['profiles']['current_record_mod_stats'] = \
+                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                    self.frames['profiles']['STNID'], \
+                    self.frames['profiles']['current_record_chunk'], \
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_ini_pct'] = \
+                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        # frame
+        # note that the current station, record is the same as the stats frame for initialization
+
+        # select first 
+        #self.frames['profiles']['current_record_index'], \
+        #self.frames['profiles']['record_yaml_mod'] = \
+        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
+        #                   self.frames['stats']['current_record_index'])
+        self.frames['profiles']['record_yaml_mod'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_mod'], \
+               self.frames['profiles']['current_record_mod'].index_start,
+               self.frames['profiles']['current_record_mod'].index_end,
+               mode='mod')
+                                
+        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_ini'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_ini'], \
+               record_ini.index_start,
+               record_ini.index_end,
+                mode='ini')
+
+        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_obs_afternoon'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_afternoon'], \
+               record_afternoon.index_start,
+               record_afternoon.index_end,
+                mode='ini')
+
+
+        key = self.frames['worldmap']['inputkey']
+        # only redraw the map if the current world map has a time
+        # dimension
+        if 'time' in self.globaldata.datasets[key].page[key].dims:
+            self.goto_datetime_worldmap(
+                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                'after')
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap',
+                                                  'profiles'])
+        else:
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap_stations',
+                                                  'profiles'])
+
+    def abline(self,slope, intercept,axis):
+        """Plot a line from slope and intercept"""
+        #axis = plt.gca()
+        x_vals = np.array(axis.get_xlim())
+        y_vals = intercept + slope * x_vals
+        axis.plot(x_vals, y_vals, 'k--')
+
+    def plot(self):
+        import pylab as pl
+        from matplotlib.widgets import Button
+        import matplotlib.pyplot as plt
+        import matplotlib as mpl
+        '''
+        Definition of the axes for the sounding table stats
+        '''
+        
+        fig = pl.figure(figsize=(14,9))
+        axes = {} #axes
+        btns = {} #buttons
+
+        # frames, which sets attributes for a group of axes, buttens, 
+        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            label = 'stats_'+str(key)
+            axes[label] = fig.add_subplot(\
+                            len(self.frames['stats']['viewkeys']),\
+                            5,\
+                            5*ikey+1,label=label)
+            # Actually, the axes should be a part of the frame!
+            #self.frames['stats']['axes'] = axes[
+
+            # pointer to the axes' point data
+            axes[label].data = {}
+
+            # pointer to the axes' color fields
+            axes[label].fields = {}
+
+
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
+
+        label ='stats_colorbar'
+        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
+        axes[label].fields = {}
+
+        from matplotlib.colors import LinearSegmentedColormap
+        cdictpres = {'blue': (\
+                           (0.,    0.,  0.),
+                           (0.25,  0.25, 0.25),
+                           (0.5,  .70, 0.70),
+                           (0.75, 1.0, 1.0),
+                           (1,     1.,  1.),
+                           ),
+               'green': (\
+                           (0. ,   0., 0.0),
+                           (0.25,  0.50, 0.50),
+                           (0.5,  .70, 0.70),
+                           (0.75,  0.50, 0.50),
+                           (1  ,    0,  0.),
+                           ),
+               'red':  (\
+                          (0 ,  1.0, 1.0),
+                          (0.25 ,  1.0, 1.0),
+                           (0.5,  .70, 0.70),
+                          (0.75 , 0.25, 0.25),
+                          (1,    0., 0.),
+                          )}
+        
+        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+        label = 'times'
+               
+        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+
+
+        label = 'worldmap'
+               
+        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+        axes[label].lat = None
+        axes[label].lon = None
+
+        label = 'worldmap_colorbar'
+        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+        axes[label].fields = {}
+
+        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
+        label = 'worldmap_stations'
+        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label].data = {}
+
+        fig.canvas.mpl_connect('pick_event', self.on_pick)
+        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
+
+
+        """ buttons definitions """
+        
+        label = 'bprev_dataset'
+        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous dataset')
+        btns[label].on_clicked(self.prev_dataset)
+
+        label = 'bnext_dataset'
+        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next dataset')
+        btns[label].on_clicked(self.next_dataset)
+
+        label = 'bprev_datetime'
+        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous datetime')
+        btns[label].on_clicked(self.prev_datetime)
+
+        label = 'bnext_datetime'
+        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next datetime')
+        btns[label].on_clicked(self.next_datetime)
+
+
+        label = 'bprev_station'
+        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous station')
+        btns[label].on_clicked(self.prev_station)
+
+        label = 'bnext_station'
+        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next station')
+        btns[label].on_clicked(self.next_station)
+
+        label = 'bprev_record'
+        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous record')
+        btns[label].on_clicked(self.prev_record)
+
+        label = 'bnext_record'
+        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next record')
+        btns[label].on_clicked(self.next_record)
+
+
+        # self.nstatsview = nstatsview
+        # self.statsviewcmap = statsviewcmap
+        self.fig = fig
+        self.axes = axes
+        self.btns = btns
+        self.tbox = {}
+        # self.hover_active = False
+
+        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
+        #                                transform=plt.gcf().transFigure)
+
+        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
+                                          transform=plt.gcf().transFigure)
+
+        label = 'air_ap:theta'
+        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
+
+        label = 'air_ap:q'
+        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
+
+        label = 'out:h'
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+
+        label = 'out:theta'
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+
+        label = 'out:q'
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+
+        label = 'SEB'
+        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
+
+
+        self.hover_active = False
+        self.fig = fig
+        self.fig.show()
+        self.fig.canvas.draw()
+        self.refresh_plot_interface()
+
+
+    # def scan_stations(self):
+    #     blabla
+        
+
+
+    # def get_records(current_file):
+    #     records = pd.DataFrame()
+
+    #     # initial position
+    #     next_record_found = False
+    #     while(not next_record_found):
+    #         next_record_found = (current_file.readline() == '---\n')
+    #     next_tell = current_file.tell() 
+    #     end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #     while not end_of_file:
+    #         current_tell = next_tell
+    #         next_record_found = False
+    #         current_file.seek(current_tell)
+    #         while ( (not next_record_found) and (not end_of_file)):
+    #             current_line = current_file.readline()
+    #             next_record_found = (currentline == '---\n')
+    #             end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #         # we store the position of the next record
+    #         next_tell = current_file.tell() 
+    #         
+    #         # we get the current record. Unfortunately we need to reset the
+    #         # yaml record generator first.
+    #         current_yamlgen.close()
+    #         current_yamlgen = yaml.load_all(current_file)
+    #         current_file.seek(current_tell)
+    #         current_record_mod = current_yamlgen.__next__()
+    #     current_yamlgen.close()
+
+    #     return records
+
+       #      next_record_found = False
+       #      while(not record):
+       #          next_record_found = (self.current_file.readline() == '---\n')
+       #      self.current_tell0 = self.current_file.tell() 
+
+       #  
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell0 = self.current_file.tell() 
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell1 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell0)
+       #  self.r0 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell1)
+       #  next_record_found = False
+       #  while ( (not next_record_found) and (not end_of_file):
+       #      current_line = self.current_file.readline()
+       #      next_record_found = (currentline == '---\n')
+       #      end_of_file = (currentline == '') # an empty line means we are at the end
+
+       #  self.current_tell2 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell1)
+       #  self.r1 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell2)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell3 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell2)
+       #  self.r2 = self.current_yamlgen.__next__()
+
+       #  # go to position of next record in file
+       #  self.current_file.seek(self.current_tell3)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell4 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell3)
+       #  self.r3 = self.current_yamlgen.__next__()
+ 
+       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
+
+    def goto_datetime_worldmap(self,DT,shift=None):
+        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                self.frames['worldmap']['iDT'] += 1
+            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                self.frames['worldmap']['iDT'] -= 1 
+            # for gleam, we take the values of the previous day
+            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                self.frames['worldmap']['iDT'] -= 2 
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+        #else:
+        #    self.frames['worldmap'].pop('DT')
+
+    def next_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def prev_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def next_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+
+
+    def sel_dataset(self,inputkey):
+        self.frames['worldmap']['inputkey'] = inputkey
+        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
+        self.goto_datetime_worldmap(
+            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+            'after')# get nearest datetime of the current dataset to the profile
+        if "fig" in self.__dict__.keys():
+            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+       
+    # def prev_station(self,event=None):
+    #     self.istation = (self.istation - 1) % self.stations.shape[0]
+    #     self.update_station()
+
+
+
+
+    #def update_datetime(self):
+    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
+    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
+    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
+    #        print(self.worldmapfocus['DT'])
+    #        self.refresh_plot_interface(only='worldmap')
+
+    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
+
+        #print('r1')
+        for argkey in args.keys():
+            self.__dict__[arg] = args[argkey]
+
+        axes = self.axes
+        tbox = self.tbox
+        frames = self.frames
+        fig = self.fig
+ 
+        if (only is None) or ('worldmap' in only):
+            globaldata = self.globaldata
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+            else:
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+            keystotranspose = ['lat','lon']
+            for key in dict(datasetxr.dims).keys():
+                if key not in keystotranspose:
+                    keystotranspose.append(key)
+
+            datasetxr = datasetxr.transpose(*keystotranspose)
+            datasetxr = datasetxr.sortby('lat',ascending=False)
+
+            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+            lonleft = lonleft - 360.
+            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+            label = 'worldmap'
+            axes[label].clear()
+            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+
+        if (only is None) or ('worldmap' in only):
+            #if 'axmap' not in self.__dict__ :
+            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
+            #else:
+
+            #stations = self.stations
+
+
+            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
+            #     resolution = 'l', 
+            # area_thresh = 0.1,
+            #     llcrnrlon=-180., llcrnrlat=-90.0,
+            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
+            # 
+            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
+            # self.gmap.drawcountries(color='white',linewidth=0.3)
+            # #self.gmap.fillcontinents(color = 'gray')
+            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
+            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
+            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
+            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # #self.ax5.shadedrelief()
+
+           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
+
+
+            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+
+            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
+            if 'lev' in field.dims:
+                field = field.isel(lev=-1)
+
+            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+            axes[label].axis('off')
+
+            from matplotlib import cm
+            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+            
+            
+            title=frames['worldmap']['inputkey']
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            axes[label].set_title(title)
+
+            label ='worldmap_colorbar'
+            axes[label].clear()
+            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+
+
+            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+            # x,y = self.gmap(lons,lats)
+            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+
+        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+
+            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            store_xlim = {}
+            store_ylim = {}
+            for ikey, key in enumerate(statskeys_out):
+                if (only is not None) and ('stats_lightupdate' in only):
+                    store_xlim[key] = axes['stats_'+key].get_xlim()
+                    store_ylim[key] = axes['stats_'+key].get_ylim()
+                self.axes['stats_'+key].clear()    
+
+            label = 'times'
+            self.axes[label].clear()
+
+            key = 'dthetadt'
+            x = self.frames['stats']['records_all_stations_ini']['datetime']
+            #print(x)
+            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            #print(y)
+            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            #print(z)
+
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            self.axes[label].data[label] = self.axes[label].scatter(x.values,
+                                                                    y.values,
+                                                                    c=z.values,
+                                                                    cmap=self.statsviewcmap,
+                                                                    s=2,
+                                                                    vmin=0.,
+                                                                    vmax=1.,
+                                                                    alpha=alpha_cloud_pixels)
+
+            
+            x = self.frames['stats']['records_current_station_ini']['datetime']
+            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+
+            x = self.frames['profiles']['records_current_station_ini']['datetime']
+            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
+            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
+
+            for ikey, key in enumerate(statskeys_out):
+
+                # show data of all stations
+                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                qvalmax = x.quantile(0.999)
+                qvalmin = x.quantile(0.001)
+                print('applying extra filter over extreme values for plotting stats')
+                selx = (x >= qvalmin) & (x < qvalmax)
+                sely = (x >= qvalmin) & (x < qvalmax)
+                x = x[selx & sely]
+                y = y[selx & sely]
+                z = z[selx & sely]
+                self.axes['stats_'+key].data['stats_'+key] = \
+                       self.axes['stats_'+key].scatter(x,y, c=z,\
+                                cmap=self.statsviewcmap,\
+                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
+
+                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
+
+                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
+                y = self.frames['stats']['current_record_mod_stats'][key]
+                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
+                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
+                    axes['stats_'+key].annotate(text, \
+                                               xy=(x,y),\
+                                               xytext=(0.05,0.05),\
+                                               textcoords='axes fraction',\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               color='white',\
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                # self.axes['stats_'+key].data[key+'_current_record'] = \
+                #        self.axes['stats_'+key].scatter(x,y, c=z,\
+                #                 cmap=self.statsviewcmap,\
+                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
+
+                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
+                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
+                # # highlight data for curent station
+                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+
+                if ikey == len(statskeys_out)-1:
+                    self.axes['stats_'+key].set_xlabel('external')
+                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
+                axes['stats_'+key].set_ylabel('model')
+
+
+                if (only is not None) and ('stats_lightupdate' in only):
+                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
+                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
+                else:
+                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
+                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
+                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
+                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
+                self.abline(1,0,axis=self.axes['stats_'+key])
+
+        if (only is None) or ('stats_colorbar' in only):
+            label ='stats_colorbar'
+            axes[label].clear()
+            import matplotlib as mpl
+            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
+            self.axes[label].fields[label] = \
+             mpl.colorbar.ColorbarBase(self.axes[label],\
+                        orientation='horizontal',\
+                        label="percentile of "+self.frames['worldmap']['inputkey'],
+                        alpha=1.,
+                                cmap=self.statsviewcmap,\
+                                       norm=norm
+                         )
+
+        #print('r1')
+        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
+            #print('r2')
+            label = 'worldmap_stations'
+            axes[label].clear()
+            
+            stations = self.frames['worldmap']['stations'].table
+            globaldata = self.globaldata
+            
+            key = label
+
+            #print('r3')
+            if (stations is not None):
+                xlist = []
+                ylist = []
+                #print('r4')
+                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
+            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    xlist.append(x)
+                    ylist.append(y)
+                #picker is needed to make it clickable (pick_event)
+                axes[label].data[label] = axes[label].scatter(xlist,ylist,
+                                                              c='r', s=15,
+                                                              picker = 15,
+                                                              label=key,
+                                                              edgecolor='k',
+                                                              linewidth=0.8)
+
+            # cb.set_label('Wilting point [kg kg-3]')
+                #print('r5')
+
+                
+            #     xseries = []
+            #     yseries = []
+            #     for iSTN,STN in stations.iterrows():
+            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
+            #         xseries.append(x)                    
+            #         yseries.append(y)
+            #         
+            #         
+            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
+                    
+                if ('current_station' in frames['worldmap']):
+                    #print('r5')
+                    STN = frames['stats']['current_station']
+                    STNID = frames['stats']['STNID']
+                    #print('r5')
+
+                    x,y = len(axes['worldmap'].lon)* \
+                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
+                          len(axes['worldmap'].lat)* \
+                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    #print('r6')
+                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
+                    #                          == \
+                    #                          self.frames['worldmap']['STNID'])\
+                    #                         & \
+                    #                         (self.seltablestats['DT'] \
+                    #                          == self.axes['statsview0].focus['DT']) \
+                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
+                    #print('r7')
+                    text = 'STNID: '+ format(STNID,'10.0f') + \
+                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
+                            ', LON: '+format(STN['longitude'],'3.3f')+ \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+
+                            #+', VAL: '+format(VAL,'.3e')
+
+                    axes[label].scatter(x,y, c='r', s=30,\
+                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
+                    #print('r8')
+            
+                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
+                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
+                    #colorstation = max((min((1.,colorstation)),0.))
+                    colorstation =0.2
+                    from matplotlib import cm
+                    axes[label].annotate(text,
+                                         xy=(x,y),
+                                         xytext=(0.05,0.05),
+                                         textcoords='axes fraction', 
+                                         bbox=dict(boxstyle="round",
+                                         fc = cm.viridis(colorstation)),
+                                         arrowprops=dict(arrowstyle="->",
+                                                         linewidth=1.1),
+                                         color='white' if colorstation < 0.5 else 'black')
+                    #print('r9')
+
+                    # #pos = sc.get_offsets()[ind["ind"][0]]
+                    # 
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label].data[label+'statannotate'].set_text(text)
+                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
+                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
+            #print('r9')
+            axes[label].axis('off')
+            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
+            axes[label].set_ylim((len(axes['worldmap'].lat),0))
+            #print('r10')
+
+        if (only is None) or ('profiles' in only): 
+            #print('r11')
+
+            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
+            # # self.update_station(goto_first_sounding=False)
+            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
+            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
+            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
+            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
+
+            label = 'air_ap:theta'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # +\
+                # ' -> '+ \
+                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+            
+            
+            
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+            #print('r12')
+
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
+            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
+            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            #print('r13')
+            # 
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r14')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+
+            #print('r15')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+                          
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r16')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r17')
+            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            print(hmax)
+            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            if valid_mod:
+
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="mod "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+
+            #print('r18')
+            axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('theta [K]')
+
+            label = 'air_ap:q'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+
+            #print('r19')
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            if valid_mod:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            else:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            # 
+            #print('r20')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r21')
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            #print('r23')
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r24')
+            if valid_mod:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="fit ")#+\
+                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #+'LT')
+            #print('r25')
+            #axes[label].legend()
+
+            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            #axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('q [kg/kg]')
+
+            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+
+            # #pl.subplots_adjust(right=0.6)
+
+            # label = 'q_pro'
+            # axes[label].clear()
+
+            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
+            # 
+            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
+            # 
+            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
+
+            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
+            # #pl.subplots_adjust(right=0.6)
+            # axes[label].set_xlabel('specific humidity [kg/kg]')
+ 
+
+            #print('r26')
+            time = self.frames['profiles']['record_yaml_mod'].out.time
+            for ilabel,label in enumerate(['h','theta','q']):
+                axes["out:"+label].clear()
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].set_ylabel(label)
+                if ilabel == 2:
+                    axes["out:"+label].set_xlabel('local sun time [h]')
+                
+            #print('r27')
+            label = 'SEB'
+            axes[label].clear()
+            
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
+            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
+            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
+                
+            #print('r28')
+            
+            axes[label].legend()
+            
+            #         for ax in self.fig_timeseries_axes:
+#             ax.clear()
+#         
+#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
+#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
+#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
+#         #print(self.morning_sounding.c4gl.out.Swin)
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
+#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
+#         self.fig_timeseries_axes[3].legend()
+#         self.fig.canvas.draw()
+            
+
+
+
+
+
+
+        #self.ready()
+        #print('r29')
+        fig.canvas.draw()
+        #fig.show()
+
+        self.axes = axes
+        self.tbox = tbox
+        self.fig = fig
+
+    def on_pick(self,event):
+        #print("HELLO")
+        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
+        #self.axes['theta_pro'].clear()
+        #self.axes['q_pro'].clear()
+        
+
+        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
+        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
+        keys_to_axes = {}
+        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
+
+        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
+        keys_to_axes['worldmap'] = 'worldmap'
+        
+        axes = self.axes
+        #nstatsview = self.nstatsview
+        #statsviewcmap = self.statsviewcmap
+        stations = self.frames['worldmap']['stations'].table
+
+
+        #print("p1")
+        current = event
+        artist = event.artist
+        
+        selkey = artist.get_label()
+        
+        #print(keys_to_axes)
+        
+        label = keys_to_axes[selkey]
+        #print("HELLO",selkey,label)
+
+        # # Get to know in which axes we are
+        # label = None
+        # for axeskey in axes.keys():
+        #     if event.inaxes == axes[axeskey]:
+        #         label = axeskey
+        #         
+
+        # cont, pos = None, None
+        
+        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
+        ind = event.ind
+        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
+        d = axes[label].collections[0]
+        #d.set_offset_position('data')
+        xy = d.get_offsets()
+        x, y =  xy[:,0],xy[:,1]
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+
+        #print("p2")
+        if len(ind) > 0:
+            #print("p3")
+            pos = x[ind[0]], y[ind[0]]
+
+            #if label[:-1] == 'statsview':
+            #    #seltablestatsstdrel = self.seltablestatsstdrel
+            #    #seltablestatspct = self.seltablestatspct
+
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    
+            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
+            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+            #    
+            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
+            #el
+            if (label == 'worldmap') or (label == 'worldmap_stations'):
+                self.hover_active = False
+                if (self.frames['worldmap']['STNID'] !=
+                    self.frames['profiles']['STNID']):
+                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
+                # so we just need to perform update_station
+                    self.update_station()
+            elif (label[:5] == 'stats'):
+
+                self.hover_active = False
+                if (self.frames['stats']['STNID'] !=
+                self.frames['profiles']['STNID']) or \
+                   (self.frames['stats']['current_record_chunk'] != 
+                    self.frames['profiles']['current_record_chunk']) or \
+                   (self.frames['stats']['current_record_index'] != 
+                    self.frames['profiles']['current_record_index']):
+
+
+
+                    for key in ['STNID','current_station','stations_iterator']: 
+                        self.frames['worldmap'][key] = self.frames['stats'][key] 
+
+                    for key in self.frames['stats'].keys():
+                        self.frames['profiles'][key] = self.frames['stats'][key]
+
+                    STNID = self.frames['profiles']['STNID']
+                    chunk = self.frames['profiles']['current_record_chunk']
+                    if 'current_station_file_ini' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_ini'].close()
+                    self.frames['profiles']['current_station_file_ini'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+                    if 'current_station_file_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_mod'].close()
+                    self.frames['profiles']['current_station_file_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_afternoon'].close()
+                    self.frames['profiles']['current_station_file_afternoon'] = \
+                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+                    # go to hovered record of current station
+                    self.frames['profiles']['records_iterator'] = \
+                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # ... and go to the record of the profile window (last one that
+                    # was picked by the user)
+                    found = False
+                    EOF = False
+                    while (not found) and (not EOF):
+                        try:
+                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
+                            #print("hello*")
+                            #print(self.frames['profiles']['current_record_index'])
+                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
+                               (index == self.frames['profiles']['current_record_index']) and \
+                               (STNID == self.frames['profiles']['STNID']):
+                                #print('found!')
+                                found = True
+                        except StopIteration:
+                            EOF = True
+                    if found:
+                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_chunk'] = chunk
+                        self.frames['stats']['current_record_index'] = index
+                    # # for the profiles we make a distinct record iterator, so that the
+                    # # stats iterator can move independently
+                    # self.frames['profiles']['records_iterator'] = \
+                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # (self.frames['profiles']['STNID'] , \
+                    # self.frames['profiles']['current_record_index']) , \
+                    # self.frames['profiles']['current_record_mod'] = \
+                    #                 self.frames['profiles']['records_iterator'].__next__()
+
+
+                    # for the profiles we make a distinct record iterator, so that the
+                    # stats iterator can move independently
+
+                    self.update_record()
+
+
+
+    def on_plot_hover(self,event):
+        axes = self.axes
+        #print('h1')
+
+        # Get to know in which axes we are
+        label = None
+        for axeskey in axes.keys():
+            if event.inaxes == axes[axeskey]:
+                label = axeskey
+                
+        #print('h2')
+
+        cont, pos = None, None
+        #print (label)
+        
+        if label is not None:
+            if  ('data' in axes[label].__dict__.keys()) and \
+                (label in axes[label].data.keys()) and \
+                (axes[label].data[label] is not None):
+                
+                #print('h3')
+                cont, ind =  axes[label].data[label].contains(event)
+                selkey = axes[label].data[label].get_label()
+                if len(ind["ind"]) > 0:
+                    #print('h4')
+                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
+                    #print('pos',pos,selkey)
+
+
+                    #if label[:-1] == 'statsview':
+                    #    seltablestatsstdrel = self.seltablestatsstdrel
+                    #    seltablestatspct = self.seltablestatspct
+
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
+                    #    self.hover_active = True
+                    #    
+                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
+                    #    
+                    #el
+                    #print(label[:5])
+                    if (label[:5] == 'stats') or (label == 'times'):
+                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        
+
+                        if label[:5] == 'stats':
+                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            (self.frames['stats']['STNID'] ,
+                             self.frames['stats']['current_record_chunk'], 
+                             self.frames['stats']['current_record_index']) = \
+                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        # elif label[:5] == 'stats':
+                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                        #     (self.frames['stats']['STNID'] ,
+                        #      self.frames['stats']['current_record_chunk'], 
+                        #      self.frames['stats']['current_record_index']) = \
+                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+
+
+                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+                        
+                        # # TO TEST: should be removed, since it's is also done just below
+                        # self.frames['stats']['stations_iterator'] = \
+                        #     self.frames['worldmap']['stations_iterator'] 
+                
+                
+                        # self.goto_datetime_worldmap(
+                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+                        #     'after')
+
+
+                        # scrolling to the right station
+                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                        EOF = False
+                        found = False
+                        while (not found and not EOF):
+                            if (STNID == self.frames['stats']['STNID']):
+                                   found = True 
+                            if not found:
+                                try:
+                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                                except (StopIteration):
+                                    EOF = True
+                        if found:
+                        #    self.frames['stats']['STNID'] = STNID
+                            self.frames['stats']['current_station'] =  station
+
+                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
+                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
+
+
+                        # generate index of the current station
+                        self.frames['stats']['records_current_station_index'] = \
+                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                             == self.frames['stats']['STNID'])
+
+
+                        tab_suffixes = \
+                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            self.frames['stats']['records_current_station'+tab_suffix] = \
+                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+
+                        # go to hovered record of current station
+                        self.frames['stats']['records_iterator'] = \
+                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+
+
+                        # ... and go to the record of the profile window (last one that
+                        # was picked by the user)
+                        found = False
+                        EOF = False
+                        while (not found) and (not EOF):
+                            try:
+                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                                #print("hello*")
+                                #print(self.frames['profiles']['current_record_index'])
+                                if (index == self.frames['stats']['current_record_index']) and \
+                                   (chunk == self.frames['stats']['current_record_chunk']) and \
+                                   (STNID == self.frames['stats']['STNID']):
+                                    #print('found!')
+                                    found = True
+                            except StopIteration:
+                                EOF = True
+                        if found:
+                            #print('h5')
+                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_chunk'] = chunk
+                            self.frames['stats']['current_record_index'] = index
+
+                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
+                        tab_suffixes = \
+                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            #print(tab_suffix)
+                            #print(self.frames['stats']['records_current_station'+tab_suffix])
+                            self.frames['stats']['current_record'+tab_suffix] =  \
+                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                      (self.frames['stats']['STNID'] , \
+                                       self.frames['stats']['current_record_chunk'] , \
+                                       self.frames['stats']['current_record_index'])]
+
+
+                        self.hover_active = True
+                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                        # print('h13')
+                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
+                        #     self.goto_datetime_worldmap(
+                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                        #         'after')
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap',
+                        #                                           'profiles'])
+                        # else:
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap_stations',
+                        #                                           'profiles'])
+
+
+
+                    elif label in ['worldmap_stations','worldmap']:
+                        #print('h5')
+
+                        if (self.axes['worldmap'].lat is not None) and \
+                           (self.axes['worldmap'].lon is not None):
+
+
+                            #self.loading()
+                            self.fig.canvas.draw()
+                            self.fig.show()
+
+
+                            # get position of 
+                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
+                                                                 self.axes['worldmap'].lat[0]) + \
+                                           self.axes['worldmap'].lat[0],4)
+                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
+                                                                 self.axes['worldmap'].lon[0]) + \
+                                           self.axes['worldmap'].lon[0],4)
+                        
+                            stations = self.frames['worldmap']['stations'].table
+                            #print('h7')
+                        
+                            #reset stations iterator:
+                            # if 'stations_iterator' in self.frames['worldmap'].keys():
+                            #     self.frames['worldmap']['stations_iterator'].close()
+                            #     del(self.frames['worldmap']['stations_iterator'])
+                            # if 'stations_iterator' in self.frames['stats'].keys():
+                            #     self.frames['stats']['stations_iterator'].close()
+                            #     del(self.frames['stats']['stations_iterator'])
+                            self.frames['worldmap']['stations_iterator'] =\
+                               stations_iterator(self.frames['worldmap']['stations'])
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                            EOF = False
+                            found = False
+                            while (not found and not EOF):
+                                #print('h8',station.latitude,latmap)
+                                #print('h8',station.longitude,lonmap)
+                                if (round(station.latitude,3) == round(latmap,3)) and \
+                                    (round(station.longitude,3) == round(lonmap,3)):
+                                       found = True 
+                                if not found:
+                                    try:
+                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                                    except (StopIteration):
+                                        EOF = True
+                            if found:
+                                self.frames['worldmap']['STNID'] = STNID
+                                self.frames['worldmap']['current_station'] = \
+                                        station
+                        
+                            self.frames['stats']['stations_iterator'] = \
+                                self.frames['worldmap']['stations_iterator'] 
+                            #print('h8')
+                            # inherit station position for the stats frame...
+                            for key in self.frames['worldmap'].keys():
+                                self.frames['stats'][key] = self.frames['worldmap'][key]
+                                
+                            ## fetch records of current station...
+                            #self.frames['stats']['records_current_station_mod'] =\
+                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                            # ... and their indices
+                            self.frames['stats']['records_current_station_index'] = \
+                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                                     == \
+                                     self.frames['stats']['current_station'].name)
+
+
+                            tab_suffixes = \
+                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['records_current_station'+tab_suffix] = \
+                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+                            # ... create a record iterator ...
+                            #self.frames['stats']['records_iterator'].close()
+                            del(self.frames['stats']['records_iterator'])
+                            self.frames['stats']['records_iterator'] = \
+                                self.frames['stats']['records_current_station_mod'].iterrows()
+
+
+
+                        
+                            #print('h9')
+                            # ... and go to to the first record of the current station
+                            (self.frames['stats']['STNID'] , \
+                             self.frames['stats']['current_record_chunk'] , \
+                             self.frames['stats']['current_record_index']) , \
+                            self.frames['stats']['current_record_mod'] = \
+                                self.frames['stats']['records_iterator'].__next__()
+                        
+
+
+
+                            #print('h10')
+                            # cash the current record
+                            tab_suffixes = \
+                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['current_record'+tab_suffix] =  \
+                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                          (self.frames['stats']['STNID'] , \
+                                           self.frames['stats']['current_record_chunk'] , \
+                                           self.frames['stats']['current_record_index'])]
+
+                            #print('h11')
+                            
+                            self.hover_active = True
+                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                            #print('h13')
+
+                        
+
+            #if (stations is not None):
+            #    for iSTN,STN in stations.iterrows():
+            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
+            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
+
+        # self.fig.show()
+ 
+        # we are hovering on nothing, so we are going back to the position of
+        # the profile sounding
+        if pos is None:
+            if self.hover_active == True:
+                #print('h1*')
+                
+                #self.loading()
+                # to do: reset stations iterators
+
+                # get station and record index from the current profile
+                for key in ['STNID', 'current_station']:
+                    self.frames['stats'][key] = self.frames['profiles'][key]
+
+                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
+                self.frames['stats']['current_station'] = \
+                        self.frames['profiles']['current_station']
+                #print('h3a*')
+                self.frames['stats']['records_current_station_mod'] = \
+                        self.frames['profiles']['records_current_station_mod']
+                #print('h3b*')
+
+                # the next lines recreate the records iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+
+                # reset stations iterator...
+                #self.frames['stats']['records_iterator'].close()
+                del(self.frames['stats']['records_iterator'])
+                self.frames['stats']['records_iterator'] = \
+                    self.frames['stats']['records_current_station_mod'].iterrows()
+                #print('h4*')
+
+                # ... and go to the record of the profile window (last one that
+                # was picked by the user)
+                found = False
+                EOF = False
+                while (not found) and (not EOF):
+                    try:
+                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                        #print("hello*")
+                        #print(self.frames['profiles']['current_record_index'])
+                        #print(self.frames['profiles']['STNID'])
+                        #print(STNID,index)
+                        if (index == self.frames['profiles']['current_record_index']) and \
+                            (chunk == self.frames['profiles']['current_record_chunk']) and \
+                            (STNID == self.frames['profiles']['STNID']):
+                            #print('found!')
+                            found = True
+                    except StopIteration:
+                        EOF = True
+                if found:
+                    #print('h5*')
+                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_chunk'] = chunk
+                    self.frames['stats']['current_record_index'] = index
+
+                #print('h6*')
+
+
+
+                # # fetch records of current station...
+                # self.frames['stats']['records_current_station_mod'] =\
+                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                # ... and their indices
+                self.frames['stats']['records_current_station_index'] = \
+                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                         == \
+                         self.frames['stats']['current_station'].name)
+
+
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['records_current_station'+tab_suffix] = \
+                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+                
+
+                # cash the records of the current stations
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['current_record'+tab_suffix] =  \
+                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                              (self.frames['stats']['STNID'] , \
+                               self.frames['stats']['current_record_chunk'] , \
+                               self.frames['stats']['current_record_index'])]
+
+
+                # the next lines recreate the stations iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+                #print('h7*')
+
+                # reset the stations iterators
+                for framekey in ['stats','worldmap']:
+                    ##print(framekey)
+                    if 'stations_iterator' in self.frames[framekey]:
+                        #self.frames[framekey]['stations_iterator'].close()
+                        del(self.frames[framekey]['stations_iterator'])
+
+                self.frames['worldmap']['current_station'] = \
+                        self.frames['profiles']['current_station']
+
+                #recreate the stations iterator for the worldmap...
+                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+
+                # ... and go the position of the profile
+                #print('h8*')
+                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                EOF = False
+                found = False
+                while (not found and not EOF):
+                    if STNID == self.frames['profiles']['STNID'] :
+                        found = True 
+                    if not found:
+                        try:
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                        except (StopIteration):
+                            EOF = True
+                if found:
+                    self.frames['worldmap']['current_station'] = station
+                    self.frames['worldmap']['STNID'] = STNID
+                #print('h9*')
+                self.frames['stats']['stations_iterator'] = \
+                    self.frames['worldmap']['stations_iterator'] 
+
+                # the stats window now inherits the current station from the
+                # worldmap
+                for key in ['STNID','current_station','stations_iterator']: 
+                    self.frames['stats'][key] = self.frames['worldmap'][key] 
+                #print('h10*')
+
+                # # we now only need inherit station position and go to first record
+                # for key in self.frames['worldmap'].keys():
+                #     self.frames['stats'][key] = self.frames['worldmap'][key]
+
+                # self.frames['stats']['records_current_station'] =\
+                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
+
+                # #print(self.frames['stats']['records_current_station'])
+                # self.frames['stats']['records_iterator'] = \
+                #                 self.frames['stats']['records_current_station'].iterrows()
+                # (self.frames['stats']['STNID'] , \
+                # self.frames['stats']['current_record_index']) , \
+                # self.frames['stats']['current_record_mod'] = \
+                #                 self.frames['stats']['records_iterator'].__next__()
+                
+
+
+
+
+
+
+                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
+                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
+                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
+                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+                self.hover_active = False
+                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
+    # def loading(self):
+    #     self.tbox['loading'].set_text('Loading...')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+    #     sleep(0.1)
+    # def ready(self):
+    #     self.tbox['loading'].set_text('Ready')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+
+
+
diff --git a/dist/class4gl-0.1dev/lib/model.py b/dist/class4gl-0.1dev/lib/model.py
new file mode 100644
index 0000000..8760411
--- /dev/null
+++ b/dist/class4gl-0.1dev/lib/model.py
@@ -0,0 +1,2214 @@
+# 
+# CLASS
+# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
+# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
+# Copyright (c) 2011-2015 Chiel van Heerwaarden
+# Copyright (c) 2011-2015 Bart van Stratum
+# Copyright (c) 2011-2015 Kees van den Dries
+# 
+# This file is part of CLASS
+# 
+# CLASS is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published bygamma
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# CLASS is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with CLASS.  If not, see .
+#
+
+import copy as cp
+import numpy as np
+import sys
+import warnings
+import pandas as pd
+from ribtol_hw import zeta_hs2 , funcsche
+import logging
+#from SkewT.thermodynamics import Density
+#import ribtol
+
+grav = 9.81
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+
+def qsat(T,p):
+    return 0.622 * esat(T) / p
+
+
+def ribtol(Rib, zsl, z0m, z0h): 
+    Rib = np.float64(Rib)
+    zsl = np.float64(zsl)
+    z0m = np.float64(z0m)
+    z0h = np.float64(z0h)
+    #print(Rib,zsl,z0m,z0h)
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    #print(Rib,zsl,z0m,z0h)
+    while (abs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+        #print(L,fx/fxdif)
+        if(abs(L) > 1e12):
+            break
+
+    return L
+  
+def psim(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psim = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+  
+def psih(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * np.log( (1. + x*x) / 2.)
+        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+ 
+class model:
+    def __init__(self, model_input = None,debug_level=None):
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        self.logger = logging.getLogger('model')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        """ initialize the different components of the model """ 
+
+        if model_input is not None:
+            # class4gl style input
+            if 'pars' in model_input.__dict__.keys():
+
+                # we make a reference to the full input first, so we can dump it
+                # afterwards
+                self.input_c4gl = model_input
+
+                # we copy the regular parameters first. We keep the classical input
+                # format as self.input so that we don't have to change the entire
+                # model code.
+                self.input = cp.deepcopy(model_input.pars)
+
+                # we copy other sections we are interested in, such as profile
+                # data, and store it also under input
+
+                # I know we mess up a bit the structure of the class4gl_input, but
+                # we will make it clean again at the time of dumping data
+
+                # So here, we copy the profile data into self.input
+                # 1. Air circulation data 
+                if 'sw_ac' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ac']:
+                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
+                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
+
+                    # correct pressure of levels according to surface pressure
+                    # error (so that interpolation is done in a consistent way)
+
+                    p_e = self.input.Ps - self.input.sp
+                    for irow in self.input.air_ac.index[::-1]:
+                       self.input.air_ac.p.iloc[irow] =\
+                        self.input.air_ac.p.iloc[irow] + p_e
+                       p_e = p_e -\
+                       (self.input.air_ac.p.iloc[irow]+p_e)/\
+                        self.input.air_ac.p.iloc[irow] *\
+                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+
+
+
+                # 2. Air circulation data 
+                if 'sw_ap' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ap']:
+                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
+
+            # standard class input
+            else:
+                self.input = cp.deepcopy(model_input)
+
+    def load_yaml_dict(self,yaml_dict):
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                for keydata,value in data.items():
+                    self.__dict__[keydata] = value
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            #elif key == 'sources':
+            #    self.__dict__[key] = data
+            elif key == 'out':
+                # lets convert it to a list of dictionaries
+                dictouttemp = pd.DataFrame(data).to_dict('list')
+            else: 
+                 warnings.warn("Key '"+key+"' is be implemented.")
+            #     self.__dict__[key] = data
+
+
+        self.tsteps = len(dictouttemp['h'])
+        self.out = model_output(self.tsteps)
+        for keydictouttemp in dictouttemp.keys():
+            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+
+
+  
+    def run(self):
+        # initialize model variables
+        self.init()
+  
+        # time integrate model 
+        #for self.t in range(self.tsteps):
+        while self.t < self.tsteps:
+          
+            # time integrate components
+            self.timestep()
+  
+        # delete unnecessary variables from memory
+        self.exitmodel()
+    
+    def init(self):
+        # assign variables from input data
+        # initialize constants
+        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
+        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        self.rho        = 1.2                   # density of air [kg m-3]
+        self.k          = 0.4                   # Von Karman constant [-]
+        self.g          = 9.81                  # gravity acceleration [m s-2]
+        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+        self.bolz       = 5.67e-8               # Bolzman constant [-]
+        self.rhow       = 1000.                 # density of water [kg m-3]
+        self.S0         = 1368.                 # solar constant [W m-2]
+
+        # A-Gs constants and settings
+        # Plant type:       -C3-     -C4-
+        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
+        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
+        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
+        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
+        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
+        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
+        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
+        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
+        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
+        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
+        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
+
+        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
+        self.mair       =  28.9;                # molecular weight air [g mol -1]
+        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
+
+        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
+        self.wmax       =  0.55;                # upper reference value soil water [-]
+        self.wmin       =  0.005;               # lower reference value soil water [-]
+        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
+        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
+
+        # Read switches
+        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
+        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
+        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
+        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
+        self.sw_sl      = self.input.sw_sl      # surface layer switch
+        self.sw_rad     = self.input.sw_rad     # radiation switch
+        self.sw_ls      = self.input.sw_ls      # land surface switch
+        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
+        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
+
+        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
+        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
+        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+  
+        # initialize mixed-layer
+        self.h          = self.input.h          # initial ABL height [m]
+        self.Ps         = self.input.Ps         # surface pressure [Pa]
+        self.sp         = self.input.sp         # This is also surface pressure
+                                                #but derived from the global data [Pa]
+        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
+        self.ws         = None                  # large-scale vertical velocity [m s-1]
+        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
+        self.we         = -1.                   # entrainment velocity [m s-1]
+       
+         # Temperature 
+        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
+        
+        
+        self.substep    = False
+        self.substeps   = 0
+
+
+
+        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
+        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
+        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
+        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
+        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
+ 
+        self.wstar      = 0.                    # convective velocity scale [m s-1]
+ 
+        # 2m diagnostic variables 
+        self.T2m        = None                  # 2m temperature [K]
+        self.q2m        = None                  # 2m specific humidity [kg kg-1]
+        self.e2m        = None                  # 2m vapor pressure [Pa]
+        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
+        self.u2m        = None                  # 2m u-wind [m s-1]
+        self.v2m        = None                  # 2m v-wind [m s-1]
+ 
+        # Surface variables 
+        self.thetasurf  = self.input.theta      # surface potential temperature [K]
+        self.thetavsurf = None                  # surface virtual potential temperature [K]
+        self.qsurf      = None                  # surface specific humidity [g kg-1]
+
+        # Mixed-layer top variables
+        self.P_h        = None                  # Mixed-layer top pressure [pa]
+        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
+        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
+        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
+        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
+        self.dz_h       = None                  # Transition layer thickness [-]
+        self.lcl        = None                  # Lifting condensation level [m]
+
+        # Virtual temperatures and fluxes
+        self.thetav     = None                  # initial mixed-layer potential temperature [K]
+        self.dthetav    = None                  # initial virtual temperature jump at h [K]
+        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
+        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
+       
+        
+        
+        
+        
+        
+        # Moisture 
+        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
+
+        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
+        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
+        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
+  
+        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
+        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
+        self.e          = None                  # mixed-layer vapor pressure [Pa]
+        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
+        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
+      
+        
+        
+        # CO2
+        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
+        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
+        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
+        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
+        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
+        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
+        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
+        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
+        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
+        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
+       
+        # Wind 
+        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
+        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
+        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = self.input.advu       # advection of u-wind [m s-2]
+        
+        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
+        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = self.input.advv       # advection of v-wind [m s-2]
+         
+  # BEGIN -- HW 20170606
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        if self.sw_ac:
+        # this is the data frame with the grided profile on the L60 grid
+        # (subsidence, and advection) 
+            self.air_ac      = self.input.air_ac  # full level air circulation
+                                                  # forcing
+            # self.air_ach     = self.input.air_ach # half level air circulation
+            #                                       # forcing
+            
+
+        if self.sw_ap:
+        # this is the data frame with the fitted profile (including HAGL,
+        # THTA,WSPD, SNDU,WNDV PRES ...)
+            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
+
+            # just for legacy reasons...
+            if 'z' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
+            if 'p' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
+
+            indexh = np.where(self.air_ap.z.values == self.h)
+            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
+                raise ValueError("Error input profile consistency: mixed- \
+                                 layer height needs to be equal to the second \
+                                 and third \
+                                 level of the vertical profile input!")
+            # initialize q from its profile when available
+            p_old = self.Ps
+            p_new = self.air_ap.p[indexh[0][0]]
+            
+            if ((p_old is not None) & (p_old != p_new)):
+                print("Warning: Ps input was provided ("+str(p_old)+\
+                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+                    +str(p_new)+"Pa).")
+                                    
+            self.Ps = p_new
+            # these variables/namings are more convenient to work with in the code
+            # we will update the original variables afterwards
+            #self.air_ap['q'] = self.air_ap.QABS/1000.
+
+            self.air_ap = \
+                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
+            # we require the temperature fields, since we need to consider
+            # advection
+            # if self.sw_ac:
+            #     #self.air_ap['theta'] = self.air_ap['t'] *
+
+            #     # we consider self.sp in case of air-circulation input (for
+            #     # consistence)
+            #     self.air_ap['t'] = \
+            #                 self.air_ap.theta *  \
+            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
+            # else:
+            # we consider self.Ps in case of balloon input only 
+            self.air_ap = self.air_ap.assign(t = lambda x: \
+                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
+
+            #self.air_ap['theta'] = self.air_ap.THTA
+            if 'u' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
+            if 'v' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
+
+            for var in ['theta','q','u','v']:
+
+                
+                if self.air_ap[var][1] != self.air_ap[var][0]:
+                    raise ValueError("Error input profile consistency: two \
+                                     lowest profile levels for "+var+" should \
+                                     be equal.")
+                
+                # initialize the value from its profile when available
+                value_old = self.__dict__[var]
+                value_new = self.air_ap[var][indexh[0][0]]
+                
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided \
+                                     ("+str(value_old)+ "kg kg-1), \
+                                     but it is now overwritten by the first \
+                                     level (index 0) of air_ap]var\ which is \
+                                     different (" +str(value_new)+"K).")
+                                        
+                self.__dict__[var] = value_new
+
+                # make a profile of the stratification 
+                # please note that the stratification between z_pro[i] and
+                # z_pro[i+1] is given by air_ap.GTHT[i]
+
+                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
+                # np.gradient(self.z_pro)
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
+
+
+                self.__dict__['gamma'+var] = \
+                    self.air_ap['gamma'+var][np.where(self.h >= \
+                                                     self.air_ap.z)[0][-1]]
+
+
+
+        # the variable p_pro is just for diagnosis of lifted index
+            
+            
+
+            # input Ph is wrong, so we correct it according to hydrostatic equation
+            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+
+            #if self.sw_ac:
+                # note that we use sp as surface pressure, which is determined
+                # from era-interim instead of the observations. This is to
+                # avoid possible failure of the interpolation routine
+                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
+                #                          + \
+                #                          list(self.air_ap.p[3:]))
+
+            # else:
+                # in the other case, it is updated at the time of calculting
+                # the statistics 
+
+# END -- HW 20170606      
+        #print(self.air_ap)
+
+        if self.sw_ac and not self.sw_ap:
+            raise ValueError("air circulation switch only possible when air \
+                             profiles are given")
+        
+        if self.sw_ac:
+
+            # # # we comment this out, because subsidence is calculated
+            # according to advection
+            # #interpolate subsidence towards the air_ap height coordinate
+            # self.air_ap['w'] = np.interp(self.air_ap.p,\
+            #                               self.air_ac.p,\
+            #                               self.air_ac.w) 
+            # #subsidence at the mixed-layer top
+            # self.w = self.air_ap.w[1]
+        
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+                # in case we didn't find any points, we just take the lowest one.
+                # actually, this can happen if ERA-INTERIM pressure levels are
+                # inconsistent with 
+                if in_ml.sum() == 0:
+                    warnings.warn(" no circulation points in the mixed layer \
+                                  found. We just take the bottom one.")
+                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+
+                for var in ['t','q','u','v']:
+    
+                   # calculation of the advection variables for the mixed layer
+                   # we weight by the hydrostatic thickness of each layer and
+                   # divide by the total thickness
+                   self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+
+                   # calculation of the advection variables for the profile above
+                   # (lowest 3 values are not used by class)
+                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
+                   self.air_ap['adv'+var] = \
+                           np.interp(self.air_ap.p,\
+                                     self.air_ac.p,\
+                                     self.air_ac['adv'+var+'_x']) \
+                           + \
+                           np.interp(self.air_ap.p, \
+                                       self.air_ac.p, \
+                                       self.air_ac['adv'+var+'_y'])
+
+                # as an approximation, we consider that advection of theta in the
+                # mixed layer is equal to advection of t. This is a sufficient
+                # approximation since theta and t are very similar at the surface
+                # pressure.
+                self.__dict__['advtheta'] = self.__dict__['advt']
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # self.wrho = np.interp(self.P_h,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) 
+            # self.ws   = self.air_ap.w.iloc[1]
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
+                self.air_ap = self.air_ap.assign(R = 0.)
+                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                     self.Rv*self.air_ap.q)
+                self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+                
+                self.air_ap = self.air_ap.assign(w = 0.)
+                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+                #print('hello w ini')
+
+                # Note: in case of sw_ac is False, we update it from prescribed
+                # divergence
+                self.ws   = self.air_ap.w[1]
+
+                # self.ws   = self.wrho/self.rho
+                # self.ws   = self.wrho/(self.P_h/ \
+                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
+                #                         self.theta) # this should be T!!!
+
+                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+                #                         + \
+                #                         self.air_ac['divU_y'][in_ml])* \
+                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                #             self.air_ac['delpdgrav'][in_ml].sum() \
+        
+
+        # Tendencies 
+        self.htend      = None                  # tendency of CBL [m s-1]
+        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
+        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
+        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
+        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
+        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
+        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
+        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
+        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
+        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
+        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
+        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
+  
+        # initialize surface layer
+        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
+        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
+        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
+        self.z0m        = self.input.z0m        # roughness length for momentum [m]
+        self.z0h        = self.input.z0h        # roughness length for scalars [m]
+        self.Cm         = 1e12                  # drag coefficient for momentum [-]
+        self.Cs         = 1e12                  # drag coefficient for scalars [-]
+        self.L          = None                  # Obukhov length [m]
+        self.Rib        = None                  # bulk Richardson number [-]
+        self.ra         = None                  # aerodynamic resistance [s m-1]
+  
+        # initialize radiation
+        self.lat        = self.input.lat        # latitude [deg]
+        #self.fc         = self.input.fc         # coriolis parameter [s-1]
+        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
+        self.lon        = self.input.lon        # longitude [deg]
+        self.doy        = self.input.doy        # day of the year [-]
+        self.tstart     = self.input.tstart     # time of the day [-]
+        self.cc         = self.input.cc         # cloud cover fraction [-]
+        self.Swin       = None                  # incoming short wave radiation [W m-2]
+        self.Swout      = None                  # outgoing short wave radiation [W m-2]
+        self.Lwin       = None                  # incoming long wave radiation [W m-2]
+        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
+        self.Q          = self.input.Q          # net radiation [W m-2]
+        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
+  
+        # initialize land surface
+        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
+        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
+        self.T2         = self.input.T2         # temperature deeper soil layer [K]
+                           
+        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
+        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
+        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
+        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
+                           
+        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
+        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
+        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
+                           
+        self.C1sat      = self.input.C1sat      
+        self.C2ref      = self.input.C2ref      
+
+        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
+        
+        self.LAI        = self.input.LAI        # leaf area index [-]
+        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
+        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = self.input.alpha      # surface albedo [-]
+  
+        self.rs         = 1.e6                  # resistance transpiration [s m-1]
+        self.rssoil     = 1.e6                  # resistance soil [s m-1]
+                           
+        self.Ts         = self.input.Ts         # surface temperature [K]
+                           
+        self.cveg       = self.input.cveg       # vegetation fraction [-]
+        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
+        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
+        self.cliq       = None                  # wet fraction [-]
+                          
+        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
+  
+        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
+        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
+        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
+  
+        self.H          = None                  # sensible heat flux [W m-2]
+        self.LE         = None                  # evapotranspiration [W m-2]
+        self.LEliq      = None                  # open water evaporation [W m-2]
+        self.LEveg      = None                  # transpiration [W m-2]
+        self.LEsoil     = None                  # soil evaporation [W m-2]
+        self.LEpot      = None                  # potential evaporation [W m-2]
+        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
+        self.G          = None                  # ground heat flux [W m-2]
+
+        # initialize A-Gs surface scheme
+        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
+
+        # initialize cumulus parameterization
+        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
+        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
+        self.ac         = 0.                    # Cloud core fraction [-]
+        self.M          = 0.                    # Cloud core mass flux [m s-1] 
+        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
+  
+        # initialize time variables
+        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
+        self.dt     = self.input.dt
+        self.dtcur      = self.dt
+        self.firsttime = True
+        self.t      = 0
+ 
+        # Some sanity checks for valid input
+        if (self.c_beta is None): 
+            self.c_beta = 0                     # Zero curvature; linear response
+        assert(self.c_beta >= 0 or self.c_beta <= 1)
+
+        # initialize output
+        self.out = model_output(self.tsteps)
+ 
+        self.statistics()
+  
+        # calculate initial diagnostic variables
+        if(self.sw_rad):
+            self.run_radiation()
+ 
+        if(self.sw_sl):
+            for i in range(10): 
+                self.run_surface_layer()
+  
+        if(self.sw_ls):
+            self.run_land_surface()
+
+        if(self.sw_cu):
+            self.run_mixed_layer()
+            self.run_cumulus()
+        
+        if(self.sw_ml):
+            self.run_mixed_layer()
+
+    def timestep(self):
+
+        self.dtmax = +np.inf
+        self.logger.debug('before stats') 
+        self.statistics()
+
+        # run radiation model
+        self.logger.debug('before rad') 
+        if(self.sw_rad):
+            self.run_radiation()
+  
+        # run surface layer model
+        if(self.sw_sl):
+            self.logger.debug('before surface layer') 
+            self.run_surface_layer()
+        
+        # run land surface model
+        if(self.sw_ls):
+            self.logger.debug('before land surface') 
+            self.run_land_surface()
+ 
+        # run cumulus parameterization
+        if(self.sw_cu):
+            self.logger.debug('before cumulus') 
+            self.run_cumulus()
+   
+        self.logger.debug('before mixed layer') 
+        # run mixed-layer model
+        if(self.sw_ml):
+            self.run_mixed_layer()
+        self.logger.debug('after mixed layer') 
+ 
+        #get first profile data point above mixed layer
+        if self.sw_ap:
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                # here we correct for the fact that the upper profile also
+                # shifts in the vertical.
+
+                diffhtend = self.htend - self.air_ap.w[zidx_first]
+                if diffhtend > 0:
+                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            else:
+                if self.htend > 0:
+                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            #print(self.h,zidx_first,self.ws,self.air_ap.z)
+
+        
+        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
+        self.logger.debug('before store') 
+        self.substep =  (self.dtcur > self.dtmax)
+        if self.substep:
+            dtnext = self.dtcur - self.dtmax
+            self.dtcur = self.dtmax
+
+        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
+
+        # HW: this will be done multiple times in case of a substep is needed
+        # store output before time integration
+        if self.firsttime:
+            self.store()
+  
+        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
+        # time integrate land surface model
+        if(self.sw_ls):
+            self.integrate_land_surface()
+        self.logger.debug('before integrate mixed layer') 
+        # time integrate mixed-layer model
+        if(self.sw_ml):
+            self.integrate_mixed_layer() 
+        self.logger.debug('after integrate mixed layer') 
+        if self.substep:
+            self.dtcur = dtnext
+            self.firsttime = False
+            self.substeps += 1
+        else:
+            self.dtcur = self.dt
+            self.t += 1 
+            self.firsttime = True
+            self.substeps = 0
+        self.logger.debug('going to next step')
+        
+        
+  
+    def statistics(self):
+        # Calculate virtual temperatures 
+        self.thetav   = self.theta  + 0.61 * self.theta * self.q
+        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
+        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
+        # Mixed-layer top properties
+        self.P_h    = self.Ps - self.rho * self.g * self.h
+        # else:
+            # in the other case, it is updated at the time that the profile is
+            # updated (and at the initialization
+
+        self.T_h    = self.theta - self.g/self.cp * self.h
+
+        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
+        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
+
+        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
+
+        # Find lifting condensation level iteratively
+        if(self.t == 0):
+            self.lcl = self.h
+            RHlcl = 0.5
+        else:
+            RHlcl = 0.9998 
+
+        itmax = 30
+        it = 0
+        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
+            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
+            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
+        else:
+            self.q2_h   = 0.
+            self.CO22_h = 0.
+
+        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
+        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
+        self.M      = self.ac * self.wstar
+        self.wqM    = self.M * self.q2_h**0.5
+
+        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
+        if(self.dCO2 < 0):
+            self.wCO2M  = self.M * self.CO22_h**0.5
+        else:
+            self.wCO2M  = 0.
+
+    def run_mixed_layer(self):
+        if(not self.sw_sl):
+            # decompose ustar along the wind components
+            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
+            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
+
+
+
+        # calculate large-scale vertical velocity (subsidence)
+        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
+            self.ws = -self.divU * self.h
+        # else:
+        #     in case the air circulation switch is turned on, subsidence is
+        #     calculated from the circulate profile at the initialization and
+        #     in the integrate_mixed_layer routine
+              
+        # calculate compensation to fix the free troposphere in case of subsidence 
+        if(self.sw_fixft):
+            w_th_ft  = self.gammatheta * self.ws
+            w_q_ft   = self.gammaq     * self.ws
+            w_CO2_ft = self.gammaCO2   * self.ws 
+        else:
+            w_th_ft  = 0.
+            w_q_ft   = 0.
+            w_CO2_ft = 0. 
+      
+        # calculate mixed-layer growth due to cloud top radiative divergence
+        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
+       
+        # calculate convective velocity scale w* 
+        if(self.wthetav > 0.):
+            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
+        else:
+            self.wstar  = 1e-6;
+      
+        # Virtual heat entrainment flux 
+        self.wthetave    = -self.beta * self.wthetav 
+        
+        # compute mixed-layer tendencies
+        if(self.sw_shearwe):
+            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
+        else:
+            self.we    = -self.wthetave / self.dthetav
+        # Don't allow boundary layer shrinking if wtheta < 0 
+        if(self.we < 0):
+            self.we = 0.
+
+        # Calculate entrainment fluxes
+        self.wthetae     = -self.we * self.dtheta
+        self.wqe         = -self.we * self.dq
+        self.wCO2e       = -self.we * self.dCO2
+        
+        htend_pre       = self.we + self.ws + self.wf - self.M
+        
+        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+        
+ 
+        #print('thetatend_pre',thetatend_pre)
+        
+        #preliminary boundary-layer top chenage
+        #htend_pre = self.we + self.ws + self.wf - self.M
+        #preliminary change in temperature jump
+        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
+                          thetatend_pre + w_th_ft
+        
+        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
+        l_entrainment = True
+
+        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
+            l_entrainment = False
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! temperature jump is at the lower limit \
+                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+        elif dtheta_pre < 0.1:
+            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
+            l_entrainment = True
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          " Warning! Potential temperature jump at mixed- \
+                          layer height would become too low limiting timestep \
+                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+            self.dtmax = min(self.dtmax,dtmax_new)
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "next subtimestep, entrainment will be disabled")
+            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
+
+
+
+        # when entrainment is disabled, we just use the simplified formulation
+        # as in Wouters et al., 2013 (section 2.2.1)
+
+        self.dthetatend = l_entrainment*dthetatend_pre + \
+                        (1.-l_entrainment)*0.
+        self.thetatend = l_entrainment*thetatend_pre + \
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = l_entrainment*htend_pre + \
+                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
+        #stop
+
+
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+
+
+        # self.qtend = l_entrainment*qtend_pre + \
+        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
+        # self.CO2tend = l_entrainment*CO2tend_pre + \
+        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+
+
+        #     # part of the timestep for which the temperature mixed-layer jump
+        #     # was changing, and for which entrainment took place. For the other
+        #     # part, we don't assume entrainment anymore, and we use the
+        #     # simplified formulation  of Wouters et al., 2013
+
+        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
+        #   
+        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
+        #                      self.dthetatend + w_th_ft) + \
+        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
+        #     self.htend = fac*self.htend + \
+        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
+        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
+        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+
+        # else:
+        #     #self.htend = htend_pre
+        #     self.dthetatend = dthetatend_pre
+        #     self.thetatend = thetatend_pre
+        
+        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+     
+        # assume u + du = ug, so ug - u = du
+        if(self.sw_wind):
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+  
+            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
+        
+        # tendency of the transition layer thickness
+        if(self.ac > 0 or self.lcl - self.h < 300):
+            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
+        else:
+            self.dztend = 0.
+
+   
+    def integrate_mixed_layer(self):
+        # set values previous time step
+        h0      = self.h
+        
+        theta0  = self.theta
+        dtheta0 = self.dtheta
+        q0      = self.q
+        dq0     = self.dq
+        CO20    = self.CO2
+        dCO20   = self.dCO2
+        
+        u0      = self.u
+        du0     = self.du
+        v0      = self.v
+        dv0     = self.dv
+
+        dz0     = self.dz_h
+  
+        # integrate mixed-layer equations
+        
+            
+
+# END -- HW 20170606        
+        self.h        = h0      + self.dtcur * self.htend
+        # print(self.h,self.htend)
+        # stop
+        self.theta    = theta0  + self.dtcur * self.thetatend
+        #print(dtheta0,self.dtcur,self.dthetatend)
+        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+        self.q        = q0      + self.dtcur * self.qtend
+        self.dq       = dq0     + self.dtcur * self.dqtend
+        self.CO2      = CO20    + self.dtcur * self.CO2tend
+        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
+        self.dz_h     = dz0     + self.dtcur * self.dztend
+            
+        # Limit dz to minimal value
+        dz0 = 50
+        if(self.dz_h < dz0):
+            self.dz_h = dz0 
+  
+        if(self.sw_wind):
+            self.u        = u0      + self.dtcur * self.utend
+            self.du       = du0     + self.dtcur * self.dutend
+            self.v        = v0      + self.dtcur * self.vtend
+            self.dv       = dv0     + self.dtcur * self.dvtend
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+            for var in ['t','q','u','v']:
+                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
+
+            # take into account advection for the whole profile
+                
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
+
+            var = 'z'
+            #print(self.air_ap[var])
+                #     print(self.air_ap['adv'+var])
+
+
+
+
+            #moving the profile vertically according to the vertical wind
+                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
+
+
+            # air_apvarold = pd.Series(np.array(self.air_ap.z))
+            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
+            # stop
+
+
+                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
+                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
+
+            #As t is updated, we also need to recalculate theta (and R)
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+
+            # air_aptheta_old = pd.Series(self.air_ap['theta'])
+            self.air_ap['theta'] = \
+                        self.air_ap.t * \
+                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
+                                         self.dtcur * self.air_ap.w[zidx_first:]
+
+#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
+#            print(self.t, self.dtcur,self.dt,self.htend)
+
+            # # the pressure levels of the profiles are recalculated according to
+            # # there new height (after subsidence)
+            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
+            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
+            #         * self.dtcur *  self.air_ap.w[zidx_first:]
+
+            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
+                    self.dtcur * self.air_ap.wp[zidx_first:]
+
+            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
+        # note that theta and q itself are updatet by class itself
+
+    
+        if self.sw_ap:
+            # Just for model consistency preservation purposes, we set the
+            # theta variables of the mixed-layer to nan values, since the
+            # mixed-layer values should overwritte by the mixed-layer
+            # calculations of class.
+            self.air_ap['theta'][0:3] = np.nan 
+            self.air_ap['p'][0:3] = np.nan 
+            self.air_ap['q'][0:3] = np.nan 
+            self.air_ap['u'][0:3] = np.nan 
+            self.air_ap['v'][0:3] = np.nan 
+            self.air_ap['t'][0:3] = np.nan 
+            self.air_ap['z'][0:3] = np.nan 
+
+            # Update the vertical profiles: 
+            #   - new mixed layer properties( h, theta, q ...)
+            #   - any data points below the new ixed-layer height are removed
+
+            # Three data points at the bottom that describe the mixed-layer
+            # properties
+            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
+                                           # columns as air_ap
+            # air_ap_head['z'].iloc[0] = 2.
+            # air_ap_head['z'].iloc[1] = self.__dict__['h']
+            # air_ap_head['z'].iloc[2] = self.__dict__['h']
+            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
+                        [2.,self.__dict__['h'],self.__dict__['h']]
+            for var in ['theta','q','u','v']:
+
+                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
+                        [self.__dict__[var], \
+                         self.__dict__[var], \
+                         self.__dict__[var] + self.__dict__['d'+var]]
+                
+            #print(self.air_ap)
+
+            # This is the remaining profile considering the remaining
+            # datapoints above the mixed layer height
+            air_ap_tail = self.air_ap.iloc[3:]
+            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
+
+            # print('h',self.h)
+            # # only select samples monotonically increasing with height
+            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            # air_ap_tail = pd.DataFrame()
+            # theta_low = self.theta
+            # z_low =     self.h
+            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            # for ibottom in range(1,len(air_ap_tail_orig)):
+            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
+            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+            # make theta increase strong enough to avoid numerical
+            # instability
+            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            air_ap_tail = pd.DataFrame()
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            theta_low = self.theta
+            z_low =     self.h
+            ibottom = 0
+            itop = 0
+            # print(air_ap_tail_orig)
+            # stop
+
+            # HW: this is the lower limit that we use for gammatheta, which is
+            # there to avoid model crashes. Besides on this limit, the upper
+            # air profile is modified in a way that is still conserves total
+            # quantities of moisture and temperature. The limit is set by trial
+            # and error. The numerics behind the crash should be investigated
+            # so that a cleaner solution can be provided.
+            gammatheta_lower_limit = 0.002
+            while ((itop in range(0,1)) or (itop != ibottom)):
+                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+                if (
+                    #(z_mean > (z_low+0.2)) and \
+                    #(theta_mean > (theta_low+0.02) ) and \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                  (itop >= (len(air_ap_tail_orig)-1)) \
+                   :
+
+                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                    ibottom = itop+1
+                    theta_low = air_ap_tail.theta.iloc[-1]
+                    z_low =     air_ap_tail.z.iloc[-1]
+    
+
+                itop +=1
+                # elif  (itop > len(air_ap_tail_orig)-10):
+                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+                #print(itop,ibottom)
+
+            if itop > 1:
+                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! Temperature profile was too steep. \
+                                  Modifying profile: "+ \
+                                  str(itop - 1)+ " measurements were dropped \
+                                  and replaced with its average \
+                                  Modifying profile. \
+                                  mean with next profile point(s).") 
+
+
+            self.air_ap = pd.concat((air_ap_head,\
+                                     air_ap_tail,\
+                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
+                                                                      axis=1)
+
+            if  self.sw_ac:
+                qvalues = \
+                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
+
+                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
+                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
+                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+                self.P_h    = self.Ps - self.rho * self.g * self.h
+                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
+                        [self.Ps,  self.P_h, self.P_h-0.1]
+
+                self.air_ap.t = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
+
+
+        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
+
+
+
+
+        # else:
+            # in the other case, it is updated at the time the statistics are
+            # calculated 
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if in_ml.sum() == 0:
+                warnings.warn(" no circulation points in the mixed layer \
+                              found. We just take the bottom one.")
+                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+            for var in ['t','q','u','v']:
+
+                # calculation of the advection variables for the mixed-layer
+                # these will be used for the next timestep
+                # Warning: w is excluded for now.
+
+                self.__dict__['adv'+var] = \
+                        ((self.air_ac['adv'+var+'_x'][in_ml] \
+                         + \
+                         self.air_ac['adv'+var+'_y'][in_ml])* \
+                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                        self.air_ac['delpdgrav'][in_ml].sum()
+
+                # calculation of the advection variables for the profile above
+                # the mixed layer (also for the next timestep)
+                self.air_ap['adv'+var] = \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p,\
+                                              self.air_ac['adv'+var+'_x']) \
+                                    + \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p, \
+                                              self.air_ac['adv'+var+'_y'])
+                # if var == 't':
+                #     print(self.air_ap['adv'+var])
+                #     stop
+
+            # as an approximation, we consider that advection of theta in the
+            # mixed layer is equal to advection of t. This is a sufficient
+            # approximation since theta and t are very similar at the surface
+            # pressure.
+
+            self.__dict__['advtheta'] = self.__dict__['advt']
+
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            # update the vertical wind profile
+            self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                          self.air_ac.p, \
+                                          self.air_ac['wp'])
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+            
+            air_apwold = self.air_ap['w']
+            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+            #print('hello w upd')
+
+            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # # self.wrho = np.interp(self.P_h,\
+            # #                      self.air_ach.p,\
+            # #                      self.air_ach['wrho']) \
+
+
+
+            # Also update the vertical wind at the mixed-layer height
+            # (subsidence)
+            self.ws   = self.air_ap.w[1]
+        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
+
+            ## Finally, we update he 
+            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+            #                        + \
+            #                        self.air_ac['divU_y'][in_ml])* \
+            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+            #            self.air_ac['delpdgrav'][in_ml].sum() 
+            
+
+        if self.sw_ap:
+            for var in ['theta','q','u','v']:
+
+                # update of the slope (gamma) for the different variables, for
+                # the next timestep!
+
+                # there is an warning message that tells about dividing through
+                # zero, which we ignore
+
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                    # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap['gamma'+var] = gammavar
+
+                # Based on the above, update the gamma value at the mixed-layer
+                # top
+                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
+                                                                     self.air_ap.z)[0][-1]]
+
+            
+    def run_radiation(self):
+        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
+        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
+        sinlea = max(sinlea, 0.0001)
+        
+        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
+  
+        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
+  
+        self.Swin  = self.S0 * Tr * sinlea
+        self.Swout = self.alpha * self.S0 * Tr * sinlea
+        
+        
+        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
+        self.Lwout = self.bolz * self.Ts ** 4.
+          
+        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
+        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
+  
+    def run_surface_layer(self):
+        # HW: I had to raise the minimum wind speed to make the simulation with
+        # the non-iterative solution stable (this solution was a wild guess, so I don't
+        # know the exact problem of the instability in case of very low wind
+        # speeds yet)
+        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        # version of 20180730 where there are still some runs crashing. Maybe
+        # an upper limit should be set on the monin-obukhov length instead of
+        # a lower limmit on the wind speed?
+        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        
+        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
+        qsatsurf       = qsat(self.thetasurf, self.Ps)
+        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
+        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
+
+        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
+  
+        zsl       = 0.1 * self.h
+        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
+        
+
+
+        if self.sw_lit:
+            self.Rib  = min(self.Rib, 0.2)
+            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
+            self.zeta  = zsl/self.L
+            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
+            
+        
+            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
+            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
+            
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+        
+     
+            # diagnostic meteorological variables
+            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
+            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
+            
+            # diagnostic meteorological variables
+        else:
+            
+            ## circumventing any iteration with Wouters et al., 2012
+            self.zslz0m = np.max((zsl/self.z0m,10.))
+            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
+            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
+            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
+            self.L = zsl/self.zeta
+            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
+        
+            self.Cm = self.k**2.0/funm/funm
+            self.Cs = self.k**2.0/funm/funh
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+            
+            # extrapolation from mixed layer (instead of from surface) to 2meter
+            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
+            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
+            self.u2m    =                - self.uw     / self.ustar / self.k * funm
+            self.v2m    =                - self.vw     / self.ustar / self.k * funm
+        
+        
+        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
+        self.e2m    = self.q2m * self.Ps / 0.622
+     
+    def ribtol(self, Rib, zsl, z0m, z0h): 
+        if(Rib > 0.):
+            L    = 1.
+            L0   = 2.
+        else:
+            L  = -1.
+            L0 = -2.
+        #print(Rib,zsl,z0m,z0h)
+        
+        while (abs(L - L0) > 0.001):
+            L0      = L
+            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
+            Lstart  = L - 0.001*L
+            Lend    = L + 0.001*L
+            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
+                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+            L       = L - fx / fxdif
+            #print(L)
+            if(abs(L) > 1e12):
+                break
+
+        return L
+      
+    def psim(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psim = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+        return psim
+      
+    def psih(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psih  = 2. * np.log( (1. + x*x) / 2.)
+            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+        return psih
+ 
+    def jarvis_stewart(self):
+        # calculate surface resistances using Jarvis-Stewart model
+        if(self.sw_rad):
+            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
+        else:
+            f1 = 1.
+  
+        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
+            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
+        else:
+            f2 = 1.e8
+ 
+        # Limit f2 in case w2 > wfc, where f2 < 1
+        f2 = max(f2, 1.);
+ 
+        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
+        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
+  
+        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
+
+    def factorial(self,k):
+        factorial = 1
+        for n in range(2,k+1):
+            factorial = factorial * float(n)
+        return factorial;
+
+    def E1(self,x):
+        E1sum = 0
+        for k in range(1,100):
+            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
+        return -0.57721566490153286060 - np.log(x) - E1sum
+ 
+    def ags(self):
+        # Select index for plant type
+        if(self.c3c4 == 'c3'):
+            c = 0
+        elif(self.c3c4 == 'c4'):
+            c = 1
+        else:
+            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
+
+        # calculate CO2 compensation concentration
+        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
+
+        # calculate mesophyll conductance
+        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
+                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
+        gm            = gm / 1000. # conversion from mm s-1 to m s-1
+  
+        # calculate CO2 concentration inside the leaf (ci)
+        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
+        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
+  
+        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
+        D0            = (self.f0[c] - fmin) / self.ad[c]
+  
+        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
+        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
+        ci            = cfrac * (co2abs - CO2comp) + CO2comp
+  
+        # calculate maximal gross primary production in high light conditions (Ag)
+        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
+  
+        # calculate effect of soil moisture stress on gross assimilation rate
+        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
+  
+        # calculate stress function
+        if (self.c_beta == 0):
+            fstr = betaw;
+        else:
+            # Following Combe et al (2016)
+            if (self.c_beta < 0.25):
+                P = 6.4 * self.c_beta
+            elif (self.c_beta < 0.50):
+                P = 7.6 * self.c_beta - 0.3
+            else:
+                P = 2**(3.66 * self.c_beta + 0.34) - 1
+            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
+  
+        # calculate gross assimilation rate (Am)
+        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
+        Rdark        = (1. / 9.) * Am
+        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
+  
+        # calculate  light use efficiency
+        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
+  
+        # calculate gross primary productivity
+        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
+  
+        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
+        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
+        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
+  
+        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
+        a1           = 1. / (1. - self.f0[c])
+        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
+  
+        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
+  
+        # calculate surface resistance for moisture and carbon dioxide
+        self.rs      = 1. / (1.6 * gcco2)
+        rsCO2        = 1. / gcco2
+  
+        # calculate net flux of CO2 into the plant (An)
+        An           = -(co2abs - ci) / (self.ra + rsCO2)
+  
+        # CO2 soil surface flux
+        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
+        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
+  
+        # CO2 flux
+        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
+        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
+        self.wCO2    = self.wCO2A + self.wCO2R
+ 
+    def run_land_surface(self):
+        # compute ra
+        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
+        #print('ueff',self.u,self.v,self.wstar)
+
+        if(self.sw_sl):
+          self.ra = (self.Cs * ueff)**-1.
+        else:
+          self.ra = ueff / max(1.e-3, self.ustar)**2.
+
+        #print('ra',self.ra,self.ustar,ueff)
+
+        # first calculate essential thermodynamic variables
+        self.esat    = esat(self.theta)
+        self.qsat    = qsat(self.theta, self.Ps)
+        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
+        self.dqsatdT = 0.622 * desatdT / self.Ps
+        self.e       = self.q * self.Ps / 0.622
+
+        if(self.ls_type == 'js'): 
+            self.jarvis_stewart() 
+        elif(self.ls_type == 'ags'):
+            self.ags()
+        else:
+            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
+
+        # recompute f2 using wg instead of w2
+        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
+          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
+        else:
+          f2        = 1.e8
+        self.rssoil = self.rssoilmin * f2 
+ 
+        Wlmx = self.LAI * self.Wmax
+        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
+        self.cliq = min(1., self.Wl / Wlmx) 
+     
+        # calculate skin temperature implictly
+        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
+            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
+            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
+            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
+
+        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        #print('Ts',self.rs)
+
+        esatsurf      = esat(self.Ts)
+        self.qsatsurf = qsat(self.Ts, self.Ps)
+
+        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+  
+        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
+  
+        self.LE     = self.LEsoil + self.LEveg + self.LEliq
+        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
+        #print('H',self.ra,self.Ts,self.theta)
+        self.G      = self.Lambda * (self.Ts - self.Tsoil)
+        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
+        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
+        
+        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
+  
+        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
+   
+        d1          = 0.1
+        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
+        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
+        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
+        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
+  
+        # calculate kinematic heat fluxes
+        self.wtheta   = self.H  / (self.rho * self.cp)
+        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
+        self.wq       = self.LE / (self.rho * self.Lv)
+ 
+    def integrate_land_surface(self):
+        # integrate soil equations
+        Tsoil0        = self.Tsoil
+        wg0           = self.wg
+        Wl0           = self.Wl
+  
+        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
+        self.wg       = wg0     + self.dtcur * self.wgtend
+        self.Wl       = Wl0     + self.dtcur * self.Wltend
+  
+    # store model output
+    def store(self):
+        t                      = self.t
+        
+        self.out.time[t]          = t * self.dt / 3600. + self.tstart
+
+        # in case we are at the end of the simulation, we store the vertical
+        # profiles to the output
+        
+        # if t == (len(self.out.time) - 1):
+        #     self.out.air_ac = self.air_ac
+        #     self.out.air_ap = self.air_ap
+
+        
+        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
+        #  for key in self.out.__dict__.keys():
+        #      if key in self.__dict__:
+        #          self.out.__dict__[key][t]  = self.__dict__[key]
+        
+        self.out.h[t]          = self.h
+        
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
+        
+        self.out.gammatheta[t] = self.gammatheta
+        self.out.gammau[t]     = self.gammau
+        self.out.gammav[t]     = self.gammav
+        self.out.gammaq[t]     = self.gammaq
+        self.out.theta[t]      = self.theta
+        self.out.thetav[t]     = self.thetav
+        self.out.dtheta[t]     = self.dtheta
+        self.out.dthetav[t]    = self.dthetav
+        self.out.wtheta[t]     = self.wtheta
+        self.out.wthetav[t]    = self.wthetav
+        self.out.wthetae[t]    = self.wthetae
+        self.out.wthetave[t]   = self.wthetave
+        
+        self.out.q[t]          = self.q
+        self.out.dq[t]         = self.dq
+        self.out.wq[t]         = self.wq
+        self.out.wqe[t]        = self.wqe
+        self.out.wqM[t]        = self.wqM
+      
+        self.out.qsat[t]       = self.qsat
+        self.out.e[t]          = self.e
+        self.out.esat[t]       = self.esat
+      
+        fac = (self.rho*self.mco2)/self.mair
+        self.out.CO2[t]        = self.CO2
+        self.out.dCO2[t]       = self.dCO2
+        self.out.wCO2[t]       = self.wCO2  * fac
+        self.out.wCO2e[t]      = self.wCO2e * fac
+        self.out.wCO2R[t]      = self.wCO2R * fac
+        self.out.wCO2A[t]      = self.wCO2A * fac
+
+        self.out.u[t]          = self.u
+        self.out.du[t]         = self.du
+        self.out.uw[t]         = self.uw
+        
+        self.out.v[t]          = self.v
+        self.out.dv[t]         = self.dv
+        self.out.vw[t]         = self.vw
+        
+        self.out.T2m[t]        = self.T2m
+        self.out.q2m[t]        = self.q2m
+        self.out.u2m[t]        = self.u2m
+        self.out.v2m[t]        = self.v2m
+        self.out.e2m[t]        = self.e2m
+        self.out.esat2m[t]     = self.esat2m
+
+
+        self.out.Tsoil[t]      = self.Tsoil
+        self.out.T2[t]         = self.T2
+        self.out.Ts[t]         = self.Ts
+        self.out.wg[t]         = self.wg
+        
+        self.out.thetasurf[t]  = self.thetasurf
+        self.out.thetavsurf[t] = self.thetavsurf
+        self.out.qsurf[t]      = self.qsurf
+        self.out.ustar[t]      = self.ustar
+        self.out.Cm[t]         = self.Cm
+        self.out.Cs[t]         = self.Cs
+        self.out.L[t]          = self.L
+        self.out.Rib[t]        = self.Rib
+  
+        self.out.Swin[t]       = self.Swin
+        self.out.Swout[t]      = self.Swout
+        self.out.Lwin[t]       = self.Lwin
+        self.out.Lwout[t]      = self.Lwout
+        self.out.Q[t]          = self.Q
+  
+        self.out.ra[t]         = self.ra
+        self.out.rs[t]         = self.rs
+        self.out.H[t]          = self.H
+        self.out.LE[t]         = self.LE
+        self.out.LEliq[t]      = self.LEliq
+        self.out.LEveg[t]      = self.LEveg
+        self.out.LEsoil[t]     = self.LEsoil
+        self.out.LEpot[t]      = self.LEpot
+        self.out.LEref[t]      = self.LEref
+        self.out.G[t]          = self.G
+
+        self.out.zlcl[t]       = self.lcl
+        self.out.RH_h[t]       = self.RH_h
+
+        self.out.ac[t]         = self.ac
+        self.out.M[t]          = self.M
+        self.out.dz[t]         = self.dz_h
+        self.out.substeps[t]   = self.substeps
+  
+    # delete class variables to facilitate analysis in ipython
+    def exitmodel(self):
+        del(self.Lv)
+        del(self.cp)
+        del(self.rho)
+        del(self.k)
+        del(self.g)
+        del(self.Rd)
+        del(self.Rv)
+        del(self.bolz)
+        del(self.S0)
+        del(self.rhow)
+  
+        del(self.t)
+        del(self.dt)
+        del(self.tsteps)
+         
+        del(self.h)          
+        del(self.Ps)        
+        del(self.fc)        
+        del(self.ws)
+        del(self.we)
+        
+        del(self.theta)
+        del(self.dtheta)
+        del(self.gammatheta)
+        del(self.advtheta)
+        del(self.beta)
+        del(self.wtheta)
+    
+        del(self.T2m)
+        del(self.q2m)
+        del(self.e2m)
+        del(self.esat2m)
+        del(self.u2m)
+        del(self.v2m)
+        
+        del(self.thetasurf)
+        del(self.qsatsurf)
+        del(self.thetav)
+        del(self.dthetav)
+        del(self.thetavsurf)
+        del(self.qsurf)
+        del(self.wthetav)
+        
+        del(self.q)
+        del(self.qsat)
+        del(self.dqsatdT)
+        del(self.e)
+        del(self.esat)
+        del(self.dq)
+        del(self.gammaq)
+        del(self.advq)
+        del(self.wq)
+        
+        del(self.u)
+        del(self.du)
+        del(self.gammau)
+        del(self.advu)
+        
+        del(self.v)
+        del(self.dv)
+        del(self.gammav)
+        del(self.advv)
+  
+        del(self.htend)
+        del(self.thetatend)
+        del(self.dthetatend)
+        del(self.qtend)
+        del(self.dqtend)
+        del(self.utend)
+        del(self.dutend)
+        del(self.vtend)
+        del(self.dvtend)
+     
+        del(self.Tsoiltend) 
+        del(self.wgtend)  
+        del(self.Wltend) 
+  
+        del(self.ustar)
+        del(self.uw)
+        del(self.vw)
+        del(self.z0m)
+        del(self.z0h)        
+        del(self.Cm)         
+        del(self.Cs)
+        del(self.L)
+        del(self.Rib)
+        del(self.ra)
+  
+        del(self.lat)
+        del(self.lon)
+        del(self.doy)
+        del(self.tstart)
+   
+        del(self.Swin)
+        del(self.Swout)
+        del(self.Lwin)
+        del(self.Lwout)
+        del(self.cc)
+  
+        del(self.wg)
+        del(self.w2)
+        del(self.cveg)
+        del(self.cliq)
+        del(self.Tsoil)
+        del(self.T2)
+        del(self.a)
+        del(self.b)
+        del(self.p)
+        del(self.CGsat)
+  
+        del(self.wsat)
+        del(self.wfc)
+        del(self.wwilt)
+  
+        del(self.C1sat)
+        del(self.C2ref)
+  
+        del(self.LAI)
+        del(self.rs)
+        del(self.rssoil)
+        del(self.rsmin)
+        del(self.rssoilmin)
+        del(self.alpha)
+        del(self.gD)
+  
+        del(self.Ts)
+  
+        del(self.Wmax)
+        del(self.Wl)
+  
+        del(self.Lambda)
+        
+        del(self.Q)
+        del(self.H)
+        del(self.LE)
+        del(self.LEliq)
+        del(self.LEveg)
+        del(self.LEsoil)
+        del(self.LEpot)
+        del(self.LEref)
+        del(self.G)
+  
+        del(self.sw_ls)
+        del(self.sw_rad)
+        del(self.sw_sl)
+        del(self.sw_wind)
+        del(self.sw_shearwe)
+
+# class for storing mixed-layer model output data
+class model_output:
+    def __init__(self, tsteps):
+        self.time          = np.zeros(tsteps)    # time [s]
+
+        # mixed-layer variables
+        self.h          = np.zeros(tsteps)    # ABL height [m]
+        
+        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammau     = np.zeros(tsteps)
+        self.gammav     = np.zeros(tsteps)
+        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
+        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
+        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
+        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
+        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
+        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
+        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
+        
+        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
+        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
+        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
+        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
+
+        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
+        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
+        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
+
+        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
+        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
+        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
+        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
+        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
+        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
+        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
+        
+        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
+        
+        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
+
+        # diagnostic meteorological variables
+        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
+        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
+        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
+        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
+        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
+        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
+
+        # ground variables
+        self.Tsoil       = np.zeros(tsteps)
+        self.T2          = np.zeros(tsteps)
+        self.Ts          = np.zeros(tsteps)
+        self.wg          = np.zeros(tsteps)
+
+        # surface-layer variables
+        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
+        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
+        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
+        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
+        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
+        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
+        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
+        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
+        self.L          = np.zeros(tsteps)    # Obukhov length [m]
+        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
+
+        # radiation variables
+        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
+        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
+        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
+        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
+        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
+
+        # land surface variables
+        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
+        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
+        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
+        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
+        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
+        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
+        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
+        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
+        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
+        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
+
+        # Mixed-layer top variables
+        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
+        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
+
+        # cumulus variables
+        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
+        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
+        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
+        
+        
+        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
+
+# class for storing mixed-layer model input data
+class model_input:
+    def __init__(self):
+
+        # # comment not valid
+        # we comment out the initialization, because there is a problem when
+        # inheriting values from one the another class4gl_iput. We also expect
+        # that the user specifies all the required parmameters (if not, an error
+        # is raised). 
+
+        # general model variables
+        self.runtime    = None  # duration of model run [s]
+        self.dt         = None  # time step [s]
+
+        # mixed-layer variables
+        self.sw_ml      = None  # mixed-layer model switch
+        self.sw_shearwe = None  # Shear growth ABL switch
+        self.sw_fixft   = None  # Fix the free-troposphere switch
+        self.h          = None  # initial ABL height [m]
+        self.Ps         = None  # surface pressure [Pa]
+        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
+        self.fc         = None  # Coriolis parameter [s-1]
+        
+        self.theta      = None  # initial mixed-layer potential temperature [K]
+        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
+
+        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
+
+        self.dtheta     = None  # initial temperature jump at h [K]
+        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = None  # advection of heat [K s-1]
+        self.beta       = None  # entrainment ratio for virtual heat [-]
+        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
+        
+        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
+        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
+
+        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = None  # advection of moisture [kg kg-1 s-1]
+        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
+
+        self.CO2        = None  # initial mixed-layer potential temperature [K]
+        self.dCO2       = None  # initial temperature jump at h [K]
+        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advCO2     = None  # advection of heat [K s-1]
+        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
+        
+        self.sw_wind    = None  # prognostic wind switch
+        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.du         = None  # initial u-wind jump at h [m s-1]
+        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = None  # advection of u-wind [m s-2]
+
+        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = None  # initial u-wind jump at h [m s-1]
+        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = None  # advection of v-wind [m s-2]
+
+        # surface layer variables
+        self.sw_sl      = None  # surface layer switch
+        self.ustar      = None  # surface friction velocity [m s-1]
+        self.z0m        = None  # roughness length for momentum [m]
+        self.z0h        = None  # roughness length for scalars [m]
+        self.Cm         = None  # drag coefficient for momentum [-]
+        self.Cs         = None  # drag coefficient for scalars [-]
+        self.L          = None  # Obukhov length [-]
+        self.Rib        = None  # bulk Richardson number [-]
+
+        # radiation parameters
+        self.sw_rad     = None  # radiation switch
+        self.lat        = None  # latitude [deg]
+        self.lon        = None  # longitude [deg]
+        self.doy        = None  # day of the year [-]
+        self.tstart     = None  # time of the day [h UTC]
+        self.cc         = None  # cloud cover fraction [-]
+        self.Q          = None  # net radiation [W m-2] 
+        self.dFz        = None  # cloud top radiative divergence [W m-2] 
+
+        # land surface parameters
+        self.sw_ls      = None  # land surface switch
+        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
+        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = None  # temperature top soil layer [K]
+        self.T2         = None  # temperature deeper soil layer [K]
+        
+        self.a          = None  # Clapp and Hornberger retention curve parameter a
+        self.b          = None  # Clapp and Hornberger retention curve parameter b
+        self.p          = None  # Clapp and Hornberger retention curve parameter p 
+        self.CGsat      = None  # saturated soil conductivity for heat
+        
+        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
+        self.wfc        = None  # volumetric water content field capacity [-]
+        self.wwilt      = None  # volumetric water content wilting point [-]
+        
+        self.C1sat      = None 
+        self.C2ref      = None
+
+        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
+        
+        self.LAI        = None  # leaf area index [-]
+        self.gD         = None  # correction factor transpiration for VPD [-]
+        self.rsmin      = None  # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = None  # surface albedo [-]
+        
+        self.Ts         = None  # initial surface temperature [K]
+        
+        self.cveg       = None  # vegetation fraction [-]
+        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
+        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
+        
+        self.Lambda     = None  # thermal diffusivity skin layer [-]
+
+        # A-Gs parameters
+        self.c3c4       = None  # Plant type ('c3' or 'c4')
+
+        # Cumulus parameters
+        self.sw_cu      = None  # Cumulus parameterization switch
+        self.dz_h       = None  # Transition layer thickness [m]
+        
+# BEGIN -- HW 20171027
+        # self.cala       = None      # soil heat conductivity [W/(K*m)]
+        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+# END -- HW 20171027
diff --git a/dist/class4gl-0.1dev/setup.py b/dist/class4gl-0.1dev/setup.py
new file mode 100644
index 0000000..a806fa0
--- /dev/null
+++ b/dist/class4gl-0.1dev/setup.py
@@ -0,0 +1,9 @@
+from distutils.core import setup
+
+setup(
+        name='class4gl',
+        version='0.1dev',
+        packages=['lib','bin'],
+        license='GPLv3 licence',
+        long_description=open('README.md').read(),
+)
diff --git a/examples/run_soundings/batch_run_soundings.py b/examples/run_soundings/batch_run_soundings.py
new file mode 100644
index 0000000..c4fc40e
--- /dev/null
+++ b/examples/run_soundings/batch_run_soundings.py
@@ -0,0 +1,76 @@
+
+import argparse
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if 'path-soundings' in args.__dict__.keys():
+#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+# else:
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
+    parser.add_argument('--experiments')#should be ';'-seperated list
+    parser.add_argument('--split-by',default=-1)
+    args = parser.parse_args()
+
+experiments = args.experiments.split(';')
+#SET = 'GLOBAL'
+SET = args.dataset
+print(args.experiments)
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+for expname in experiments:
+    #exp = EXP_DEFS[expname]
+    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+    os.system('rm -R '+path_exp)
+
+totalchunks = 0
+for istation,current_station in all_stations.iterrows():
+    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
+    totalchunks +=chunks_current_station
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
+                                       ',split_by='+str(args.split_by)+\
+                                       ',exec='+str(args.exec)+\
+                                       ',experiments='+str(args.experiments))
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/examples/run_soundings/run.py b/examples/run_soundings/run.py
new file mode 100644
index 0000000..46eeea1
--- /dev/null
+++ b/examples/run_soundings/run.py
@@ -0,0 +1,264 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--error-handling',default='dump_on_success')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    parser.add_argument('--c4gl-path',default='')
+    args = parser.parse_args()
+
+
+
+if args.c4gl_path == '': 
+    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+else:
+    sys.path.insert(0, args.c4gl_path)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_succes':
+                        try:
+                            c4gl.run()
+                            print('run not succesfull')
+                            onerun = True
+
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/examples/run_soundings/run_iter.py b/examples/run_soundings/run_iter.py
new file mode 100644
index 0000000..5dfbaff
--- /dev/null
+++ b/examples/run_soundings/run_iter.py
@@ -0,0 +1,364 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk',default=0)
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if 'global_chunk' in args.__dict__.keys():
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if 'last_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    
+    if 'first_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    if 'station_chunk' in args.__dict__.keys():
+        run_station_chunk = args.station_chunk
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+    for istation,current_station in run_stations.iterrows():
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                #if iexp == 11:
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+
+                        c4gli_morning.pars.itersteps = i
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                   #   timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    except:
+                        print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/examples/run_soundings/run_iter_test.py b/examples/run_soundings/run_iter_test.py
new file mode 100644
index 0000000..eefd475
--- /dev/null
+++ b/examples/run_soundings/run_iter_test.py
@@ -0,0 +1,367 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+                #if iexp == 11:
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+                        onerun = True
+
+                        c4gli_morning.pars.itersteps = i
+                    except:
+                        print('run not succesfull')
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                                  include_input=False,\
+                               #   timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/examples/run_soundings/trash/run_test.py b/examples/run_soundings/trash/run_test.py
new file mode 100644
index 0000000..767d960
--- /dev/null
+++ b/examples/run_soundings/trash/run_test.py
@@ -0,0 +1,241 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    parser.add_argument('--c4gl-path',default='')
+    args = parser.parse_args()
+
+if args.c4gl_path == '': 
+    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+else:
+    sys.path.insert(0, args.c4gl_path)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    try:
+                        c4gl.run()
+                    except:
+                        print('run not succesfull')
+                    onerun = True
+
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                              include_input=False,\
+                              #timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/examples/setup_global.py b/examples/setup_global.py
deleted file mode 100644
index 8b13789..0000000
--- a/examples/setup_global.py
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/examples/setup_soundings/setup_bllast.py b/examples/setup_soundings/setup_bllast.py
new file mode 100644
index 0000000..af8c8bb
--- /dev/null
+++ b/examples/setup_soundings/setup_bllast.py
@@ -0,0 +1,719 @@
+# -*- coding: utf-8 -*-
+# Read data from BLLAST campaing and convert it to class4gl input
+
+# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
+#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 42.971834,
+                  "longitude" : 0.3671169,
+                  "name" : "the BLLAST experiment"
+                })
+current_station.name = 90001
+
+
+
+
+
+# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
+# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
+
+HOUR_FILES = \
+{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110619_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110620_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110625_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110702_1655_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
+}
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
+                                     #widths=[14]*19,
+                                     #skiprows=9,
+                                     #skipfooter=15,
+                                     #decimal='.',
+                                     #header=None,
+                                     #names = columns,
+                                     #na_values='-----')
+        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
+        print(air_balloon_in.columns)
+        rowmatches = {
+            't':      lambda x: x['TaRad']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['Press']*100.,
+            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
+            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
+            'z':      lambda x: x['Altitude'] -582.,
+            # from virtual temperature to absolute humidity
+            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+        # filter data so that potential temperature always increases with
+        # height 
+        cols = []
+        for column in air_ap_tail.columns:
+            #if column != 'z':
+                cols.append(column)
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        # 
+        # # we copy the pressure at ground level from balloon sounding. The
+        # # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
+        # write local solar time, we need to assign the timezone to UTC (which
+        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
+        # tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise']+dt.timedelta(hours=2))\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        print('ldatetime_daylight',dpars['ldatetime_daylight'])
+        print('ldatetime',dpars['ldatetime'])
+        print('lSunrise',dpars['lSunrise'])
+        dpars['day'] = dpars['ldatetime'].day
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        print('tstart',dpars['tstart'])
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='bllast',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
+    
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+
+    
+    c4gli_morning.pars.sw_ac = []
+    c4gli_morning.pars.sw_ap = True
+    c4gli_morning.pars.sw_lit = False
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/examples/setup_soundings/setup_global.py b/examples/setup_soundings/setup_global.py
new file mode 100644
index 0000000..79224d9
--- /dev/null
+++ b/examples/setup_soundings/setup_global.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+    one_run = False
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+                
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            try:
+                c4gli.get_profile_wyoming(wy_strm)
+                #print(STN['ID'],c4gli.pars.datetime)
+                #c4gli.get_global_input(globaldata)
+
+                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+                logic = dict()
+                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+                logic['daylight'] = \
+                    ((c4gli.pars.ldatetime_daylight - 
+                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
+                
+                logic['springsummer'] = (c4gli.pars.theta > 278.)
+                
+                # we take 3000 because previous analysis (ie., HUMPPA) has
+                # focussed towards such altitude
+                le3000 = (c4gli.air_balloon.z <= 3000.)
+                logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+
+                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+                
+                print('logic:', logic)
+                # the result
+                morning_ok = np.mean(list(logic.values()))
+                print(morning_ok,c4gli.pars.ldatetime)
+
+            except:
+                morning_ok =False
+                print('obtain morning not good')
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                print('MORNING OK!')
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                print('AFTERNOON PROFILE CLEARED')
+                try:
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+                    print('AFTERNOON PROFILE OK')
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # we will dump the latest afternoon sounding that fits the
+                    # minimum criteria specified by logic_afternoon
+                    print(current_date,current_date_afternoon)
+                    c4gli_afternoon_for_dump = None
+                    while ((current_date_afternoon == current_date) and \
+                           (wy_strm.current is not None)):
+                        logic_afternoon =dict()
+
+                        logic_afternoon['afternoon'] = \
+                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        logic_afternoon['daylight'] = \
+                          ((c4gli_afternoon.pars.ldatetime - \
+                            c4gli_afternoon.pars.ldatetime_daylight \
+                           ).total_seconds()/3600. <= 0.)
+
+
+                        le3000_afternoon = \
+                            (c4gli_afternoon.air_balloon.z <= 3000.)
+                        logic_afternoon['5measurements'] = \
+                            (np.sum(le3000_afternoon) >= 5) 
+
+                        # we only store the last afternoon sounding that fits these
+                        # minimum criteria
+
+                        afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                        print('logic_afternoon: ',logic_afternoon)
+                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                        if afternoon_ok == 1.:
+                            # # doesn't work :(
+                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                            
+                            # so we just create a new one from the same wyoming profile
+                            c4gli_afternoon_for_dump = class4gl_input()
+                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                        wy_strm.find_next()
+                        c4gli_afternoon.clear()
+                        c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                        if wy_strm.current is not None:
+                            current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                        else:
+                            # a dummy date: this will be ignored anyway
+                            current_date_afternoon = dt.date(1900,1,1)
+
+                        # Only in the case we have a good pair of soundings, we
+                        # dump them to disk
+                    if c4gli_afternoon_for_dump is not None:
+                        c4gli.update(source='pairs',pars={'runtime' : \
+                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                                 c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                        print('ALMOST...')
+                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                                
+        
+                            c4gli.get_global_input(globaldata)
+                            print('VERY CLOSE...')
+                            if c4gli.check_source_globaldata() and \
+                                (c4gli.check_source(source='wyoming',\
+                                                   check_only_sections='pars')):
+                                c4gli.dump(fileout)
+                                
+                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                
+                                
+                                # for keyEXP,dictEXP in experiments.items():
+                                #     
+                                #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                #     c4gl = class4gl(c4gli)
+                                #     # c4gl.run()
+                                #     
+                                #     c4gl.dump(c4glfiles[key])
+                                
+                                print('HIT!!!')
+                                one_run = True
+                except:
+                    print('get profile failed')
+                
+    if one_run:
+        STN.name = STN['ID']
+        all_records_morning = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='morning',
+                                      refetch_records=True,
+                                      )
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    else:
+        os.system('rm '+fnout)
+        os.system('rm '+fnout_afternoon)
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/examples/setup_soundings/setup_goamazon.py b/examples/setup_soundings/setup_goamazon.py
new file mode 100644
index 0000000..f9efe2c
--- /dev/null
+++ b/examples/setup_soundings/setup_goamazon.py
@@ -0,0 +1,740 @@
+# -*- coding: utf-8 -*-
+
+import xarray as xr
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+import glob
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : -3.21,
+                  "longitude" : -60.6,
+                  "name" : "the GOAMAZON experiment"
+                })
+current_station.name = 90002
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
+DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
+
+
+DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
+HOUR_FILES = {}
+for iDT, DT in enumerate(DTS):
+    morning_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
+    if len(possible_files)>0:
+        morning_file= possible_files[0]
+    afternoon_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
+    if len(possible_files)>0:
+        afternoon_file= possible_files[0]
+
+    if (morning_file is not None) and (afternoon_file is not None):
+        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
+                          'afternoon':[17.5,afternoon_file]}
+
+print(HOUR_FILES)
+
+# HOUR_FILES = \
+# {
+#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
+#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+# }
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
+        print(balloon_file)
+        
+        xrin = balloon_file
+        air_balloon = pd.DataFrame()
+
+        air_balloon['t'] = xrin.tdry.values+273.15
+        air_balloon['p'] = xrin.pres.values*100.
+        
+        air_balloon['u'] = xrin.u_wind.values
+        air_balloon['v'] = xrin.v_wind.values
+        air_balloon['WSPD'] = xrin['wspd'].values
+        
+        print(xrin.rh.values.shape)
+        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
+        
+
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        # air_balloon_in = pd.read_fwf(balloon_file,
+        #                              widths=[14]*19,
+        #                              skiprows=9,
+        #                              skipfooter=15,
+        #                              decimal=',',
+        #                              header=None,
+        #                              names = columns,
+        #                              na_values='-----')
+    
+
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
+            'rho': lambda x: x.p /x.t / x.R ,
+        }
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        print('alt in xrin?:','alt' in xrin)
+        if 'alt' in xrin:
+            air_balloon['z'] = xrin.alt.values
+        else:
+            air_balloon['z'] = 0.
+            for irow,row in air_balloon.iloc[1:].iterrows():
+                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
+                        
+             
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn =pair['morning'][1]
+    print(humpafn)
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn = pair['afternoon'][1]
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+"""
+stations_for_iter = stations(path_exp)
+for STNID,station in stations_iterator(stations_for_iter):
+    records_current_station_index = \
+            (records_ini.index.get_level_values('STNID') == STNID)
+    file_current_station_mod = STNID
+
+    with \
+    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        for (STNID,index),record_ini in records_iterator(records_ini):
+            c4gli_ini = get_record_yaml(file_station_ini, 
+                                        record_ini.index_start, 
+                                        record_ini.index_end,
+                                        mode='ini')
+            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+
+            record_mod = records_mod.loc[(STNID,index)]
+            c4gl_mod = get_record_yaml(file_station_mod, 
+                                        record_mod.index_start, 
+                                        record_mod.index_end,
+                                        mode='mod')
+            record_afternoon = records_afternoon.loc[(STNID,index)]
+            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+                                        record_afternoon.index_start, 
+                                        record_afternoon.index_end,
+                                        mode='ini')
+"""
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/examples/setup_soundings/setup_humppa.py b/examples/setup_soundings/setup_humppa.py
new file mode 100644
index 0000000..ff37628
--- /dev/null
+++ b/examples/setup_soundings/setup_humppa.py
@@ -0,0 +1,732 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 61.8448,
+                  "longitude" : 24.2882,
+                  "name" : "the HUMMPA experiment"
+                })
+current_station.name = 90000
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+
+HOUR_FILES = \
+{ dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
+  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[18,'humppa_071310_1800.txt']},
+  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
+  dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
+  dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
+  dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
+  dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
+  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[21,'humppa_071910_2100.txt']},
+#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
+  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
+  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[18,'humppa_072210_1800.txt']},
+ # something is wrong with ths profile
+ # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
+  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
+  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
+  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
+  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
+  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
+  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[18,'humppa_072910_1800.txt']},
+  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
+  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[15,'humppa_073110_1500.txt']},
+  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
+  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[18,'humppa_080210_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
+  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
+  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
+  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
+  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
+  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[18,'humppa_081010_1800.txt']},
+}
+
+
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_fwf(balloon_file,
+                                     widths=[14]*19,
+                                     skiprows=9,
+                                     skipfooter=15,
+                                     decimal=',',
+                                     header=None,
+                                     names = columns,
+                                     na_values='-----')
+    
+        rowmatches = {
+            't':      lambda x: x['T[C]']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['P[hPa]']*100.,
+            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'z':      lambda x: x['Altitude[m]'],
+            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/examples/setup_soundings/trash/setup_global_old.py b/examples/setup_soundings/trash/setup_global_old.py
new file mode 100644
index 0000000..d812684
--- /dev/null
+++ b/examples/setup_soundings/trash/setup_global_old.py
@@ -0,0 +1,284 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            c4gli.get_profile_wyoming(wy_strm)
+            #print(STN['ID'],c4gli.pars.datetime)
+            #c4gli.get_global_input(globaldata)
+
+            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+            logic = dict()
+            logic['morning'] =  (c4gli.pars.ldatetime.hour < 12.)
+            logic['daylight'] = \
+                ((c4gli.pars.ldatetime_daylight - 
+                  c4gli.pars.ldatetime).total_seconds()/3600. <= 5.)
+            
+            logic['springsummer'] = (c4gli.pars.theta > 278.)
+            
+            # we take 3000 because previous analysis (ie., HUMPPA) has
+            # focussed towards such altitude
+            le3000 = (c4gli.air_balloon.z <= 3000.)
+            logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+            try:
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+            except:
+                logic['mlerrlow'] = False
+                print('rmse probably failed')
+
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+            
+            print('logic:', logic)
+            # the result
+            morning_ok = np.mean(list(logic.values()))
+            print(morning_ok,c4gli.pars.ldatetime)
+            
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                if wy_strm.current is not None:
+                    current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                else:
+                    # a dummy date: this will be ignored anyway
+                    current_date_afternoon = dt.date(1900,1,1)
+
+                # we will dump the latest afternoon sounding that fits the
+                # minimum criteria specified by logic_afternoon
+                c4gli_afternoon_for_dump = None
+                while ((current_date_afternoon == current_date) and \
+                       (wy_strm.current is not None)):
+                    logic_afternoon =dict()
+
+                    logic_afternoon['afternoon'] = \
+                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                    logic_afternoon['daylight'] = \
+                      ((c4gli_afternoon.pars.ldatetime - \
+                        c4gli_afternoon.pars.ldatetime_daylight \
+                       ).total_seconds()/3600. <= 2.)
+
+
+                    le3000_afternoon = \
+                        (c4gli_afternoon.air_balloon.z <= 3000.)
+                    logic_afternoon['5measurements'] = \
+                        (np.sum(le3000_afternoon) >= 5) 
+
+                    # we only store the last afternoon sounding that fits these
+                    # minimum criteria
+
+                    afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                    print('logic_afternoon: ',logic_afternoon)
+                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                    if afternoon_ok == 1.:
+                        # # doesn't work :(
+                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                        
+                        # so we just create a new one from the same wyoming profile
+                        c4gli_afternoon_for_dump = class4gl_input()
+                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                    wy_strm.find_next()
+                    c4gli_afternoon.clear()
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # Only in the case we have a good pair of soundings, we
+                    # dump them to disk
+                if c4gli_afternoon_for_dump is not None:
+                    c4gli.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                             c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                    print('ALMOST...')
+                    if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                            
+        
+                        c4gli.get_global_input(globaldata)
+                        print('VERY CLOSE...')
+                        if c4gli.check_source_globaldata() and \
+                            (c4gli.check_source(source='wyoming',\
+                                               check_only_sections='pars')):
+                            c4gli.dump(fileout)
+                            
+                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                            
+                            
+                            # for keyEXP,dictEXP in experiments.items():
+                            #     
+                            #     c4gli.update(source=keyEXP,pars = dictEXP)
+                            #     c4gl = class4gl(c4gli)
+                            #     # c4gl.run()
+                            #     
+                            #     c4gl.dump(c4glfiles[key])
+                            
+                            print('HIT!!!')
+                
+                
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/lib/class4gl.py b/lib/class4gl.py
new file mode 100644
index 0000000..7baaa51
--- /dev/null
+++ b/lib/class4gl.py
@@ -0,0 +1,1611 @@
+# -*- coding: utf-8 -*-
+
+"""
+
+Created on Mon Jan 29 12:33:51 2018
+
+Module file for class4gl, which  extents the class-model to be able to take
+global air profiles as input. It exists of:
+
+CLASSES:
+    - an input object, namely class4gl_input. It includes:
+        - a function to read Wyoming sounding data from a yyoming stream object
+        - a function to read global data from a globaldata library object 
+    - the model object: class4gl
+    - ....    
+
+DEPENDENCIES:
+    - xarray
+    - numpy
+    - data_global
+    - Pysolar
+    - yaml
+
+@author: Hendrik Wouters
+
+"""
+
+
+
+""" Setup of envirnoment """
+
+# Standard modules of the stand class-boundary-layer model
+from model import model
+from model import model_output as class4gl_output
+from model import model_input
+from model import qsat
+#from data_soundings import wyoming 
+import Pysolar
+import yaml
+import logging
+import warnings
+import pytz
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+
+# Generic Python Packages
+import numpy as np
+import datetime as dt
+import pandas as pd
+import xarray as xr
+import io
+#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
+from data_global import data_global
+grav = 9.81
+
+# this is just a generic input object
+class generic_input(object):
+    def __init__(self):
+        self.init = True
+
+
+# all units from all variables in CLASS(4GL) should be defined here!
+units = {
+         'h':'m',
+         'theta':'K', 
+         'q':'kg/kg',
+         'cc': '-',
+         'cveg': '-',
+         'wg': 'm3 m-3',
+         'w2': 'm3 m-3',
+         #'wg': 'kg/kg',
+         'Tsoil': 'K',
+         'T2': 'K',
+         'z0m': 'm',
+         'alpha': '-',
+         'LAI': '-',
+         'dhdt':'m/h',
+         'dthetadt':'K/h',
+         'dqdt':'kg/kg/h',
+         'BR': '-',
+         'EF': '-',
+}
+
+class class4gl_input(object):
+# this was the way it was defined previously.
+#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+
+    def __init__(self,set_pars_defaults=True,debug_level=None):
+
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        print('hello')
+        self.logger = logging.getLogger('class4gl_input')
+        print(self.logger)
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # # create logger
+        # self.logger = logging.getLogger('class4gl_input')
+        # self.logger.setLevel(debug_level)
+
+        # # create console handler and set level to debug
+        # ch = logging.StreamHandler()
+        # ch.setLevel(debug_level)
+
+        # # create formatter
+        # formatter = logging.Formatter('%(asctime)s - \
+        #                                %(name)s - \
+        #                                %(levelname)s - \
+        #                                %(message)s')
+        # add formatter to ch
+        # ch.setFormatter(formatter)
+     
+        # # add ch to logger
+        # self.logger.addHandler(ch)
+
+        # """ end set up logger """
+
+
+
+        # these are the standard model input single-value parameters for class
+        self.pars = model_input()
+
+        # diagnostic parameters of the initial profile
+        self.diag = dict()
+
+        # In this variable, we keep track of the different parameters from where it originates from. 
+        self.sources = {}
+
+        if set_pars_defaults:
+            self.set_pars_defaults()
+
+    def set_pars_defaults(self):
+
+        """ 
+        Create empty model_input and set up case
+        """
+        defaults = dict( 
+        dt         = 60.    , # time step [s] 
+        runtime    = 6*3600 ,  # total run time [s]
+        
+        # mixed-layer input
+        sw_ml      = True   ,  # mixed-layer model switch
+        sw_shearwe = False  ,  # shear growth mixed-layer switch
+        sw_fixft   = False  ,  # Fix the free-troposphere switch
+        h          = 200.   ,  # initial ABL height [m]
+        Ps         = 101300.,  # surface pressure [Pa]
+        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
+        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
+        
+        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
+        dtheta     = 1.     ,  # initial temperature jump at h [K]
+        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        advtheta   = 0.     ,  # advection of heat [K s-1]
+        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
+        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
+        
+        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
+        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
+        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
+        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
+        
+        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
+        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
+        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
+        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
+        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
+        sw_wind    = True  ,  # prognostic wind switch
+        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
+        du         = 0.     ,  # initial u-wind jump at h [m s-1]
+        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
+        advu       = 0.     ,  # advection of u-wind [m s-2]
+        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
+        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
+        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
+        advv       = 0.     ,  # advection of v-wind [m s-2]
+        sw_sl      = True   , # surface layer switch
+        ustar      = 0.3    ,  # surface friction velocity [m s-1]
+        z0m        = 0.02   ,  # roughness length for momentum [m]
+        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
+        sw_rad     = True   , # radiation switch
+        lat        = 51.97  ,  # latitude [deg]
+        lon        = -4.93  ,  # longitude [deg]
+        doy        = 268.   ,  # day of the year [-]
+        tstart     = 6.8    ,  # time of the day [h UTC]
+        cc         = 0.0    ,  # cloud cover fraction [-]
+        Q          = 400.   ,  # net radiation [W m-2] 
+        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
+        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
+        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
+        cveg       = 0.85   ,  # vegetation fraction [-]
+        Tsoil      = 295.   ,  # temperature top soil layer [K]
+        Ts         = 295.   ,    # initial surface temperature [K]
+        T2         = 296.   ,  # temperature deeper soil layer [K]
+        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
+        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
+        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
+        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
+        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
+        wfc        = 0.323  ,  # volumetric water content field capacity [-]
+        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
+        C1sat      = 0.132  ,  
+        C2ref      = 1.8    ,
+        LAI        = 2.     ,  # leaf area index [-]
+        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
+        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
+        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
+        alpha      = 0.25   ,  # surface albedo [-]
+        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
+        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
+        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
+        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
+        sw_cu      = False  ,  # Cumulus parameterization switch
+        dz_h       = 150.   ,  # Transition layer thickness [m]
+        cala       = None   ,  # soil heat conductivity [W/(K*m)]
+        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
+        sw_ls      = True   ,
+        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
+        sw_lit     = False,
+        )
+        pars = model_input()
+        for key in defaults:
+            pars.__dict__[key] = defaults[key]
+        
+        self.update(source='defaults',pars=pars)
+        
+    def clear(self):
+        """ this procudure clears the class4gl_input """
+
+        for key in list(self.__dict__.keys()):
+            del(self.__dict__[key])
+        self.__init__()
+
+    def dump(self,file):
+        """ this procedure dumps the class4gl_input object into a yaml file
+            
+            Input: 
+                - self.__dict__ (internal): the dictionary from which we read 
+            Output:
+                - file: All the parameters in self.__init__() are written to
+                the yaml file, including pars, air_ap, sources etc.
+        """
+        file.write('---\n')
+        index = file.tell()
+        file.write('# CLASS4GL input; format version: 0.1\n')
+
+        # write out the position of the current record
+        yaml.dump({'index':index}, file, default_flow_style=False)
+
+        # we do not include the none values
+        for key,data in self.__dict__.items():
+            #if ((type(data) == model_input) or (type(class4gl_input):
+            if key == 'pars':
+
+                pars = {'pars' : self.__dict__['pars'].__dict__}
+                parsout = {}
+                for key in pars.keys():
+                    if pars[key] is not None:
+                        parsout[key] = pars[key]
+
+                yaml.dump(parsout, file, default_flow_style=False)
+            elif type(data) == dict:
+                if key == 'sources':
+                    # in case of sources, we want to have a
+                    # condensed list format as well, so we leave out
+                    # 'default_flow_style=False'
+                    yaml.dump({key : data}, file)
+                else: 
+                    yaml.dump({key : data}, file,
+                              default_flow_style=False)
+            elif type(data) == pd.DataFrame:
+                # in case of dataframes (for profiles), we want to have a
+                # condensed list format as well, so we leave out
+                # 'default_flow_style=False'
+                yaml.dump({key: data.to_dict(orient='list')},file)
+
+                # # these are trials to get it into a more human-readable
+                # fixed-width format, but it is too complex
+                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
+                #file.write(stream)
+                
+                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
+                #file.write(key+': !!str |\n')
+                #file.write(str(data)+'\n')
+       
+    def load_yaml_dict(self,yaml_dict,reset=True):
+        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
+            
+            Input: 
+                - yaml_dict: the dictionary from which we read 
+                - reset: reset data before reading        
+            Output:
+                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
+        """
+        
+        if reset:
+            for key in list(self.__dict__.keys()):
+                del(self.__dict__[key])
+            self.__init__()
+
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                self.__dict__[key] = model_input()
+                self.__dict__[key].__dict__ = data
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            elif key == 'sources':
+                self.__dict__[key] = data
+            elif key == 'diag':
+                self.__dict__[key] = data
+            else: 
+                warnings.warn("Key '"+key+"' may not be implemented.")
+                self.__dict__[key] = data
+
+    def update(self,source,**kwargs):
+        """ this procedure is to make updates of input parameters and tracking
+        of their source more convenient. It implements the assignment of
+        parameter source/sensitivity experiment IDs ('eg.,
+        'defaults', 'sounding balloon', any satellite information, climate
+        models, sensitivity tests etc.). These are all stored in a convenient
+        way with as class4gl_input.sources.  This way, the user can always consult with
+        from where parameters data originates from.  
+        
+        Input:
+            - source:    name of the underlying dataset
+            - **kwargs: a dictionary of data input, for which the key values
+            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
+            the values is a again a dictionary/dataframe of datakeys/columns
+            ('wg','PRES','datetime', ...) and datavalues (either single values,
+            profiles ...), eg., 
+
+                pars = {'wg': 0.007  , 'w2', 0.005}
+                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
+                                     300.,...]}
+            
+        Output:
+            - self.__dict__[datatype] : object to which the parameters are
+                                        assigned. They can be consulted with
+                                        self.pars, self.profiles, etc.
+                                        
+            - self.sources[source] : It supplements the overview overview of
+                                     data sources can be consulted with
+                                     self.sources. The structure is as follows:
+                                     as:
+                self.sources = { 
+                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
+                'GLEAM' :  ['pars:wg','pars:w2', ...],
+                 ...
+                }
+        
+        """
+
+        #print(source,kwargs)
+
+        for key,data in kwargs.items():
+
+            #print(key)
+            # if the key is not in class4gl_input object, then just add it. In
+            # that case, the update procedures below will just overwrite it 
+            if key not in self.__dict__:
+                self.__dict__[key] = data
+
+
+            
+
+            #... we do an additional check to see whether there is a type
+            # match. I not then raise a key error
+            if (type(data) != type(self.__dict__[key]) \
+                # we allow dict input for model_input pars
+                and not ((key == 'pars') and (type(data) == dict) and \
+                (type(self.__dict__[key]) == model_input))):
+
+                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
+
+
+            # This variable keeps track of the added data that is supplemented
+            # by the current source. We add this to class4gl_input.sources
+            datakeys = []
+
+            #... and we update the class4gl_input data, and this depends on the
+            # data type
+
+            if type(self.__dict__[key]) == pd.DataFrame:
+                # If the data type is a dataframe, then we update the columns
+                for column in list(data.columns):
+                    #print(column)
+                    self.__dict__[key][column] = data[column]
+                    datakeys.append(column)
+                    
+
+            elif type(self.__dict__[key]) == model_input:
+                # if the data type is a model_input, then we update its internal
+                # dictionary of parameters
+                if type(data) == model_input:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data.__dict__}
+                    datakeys = list(data.__dict__.keys())
+                elif type(data) == dict:
+                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
+                                                   **data}
+                    datakeys = list(data.keys())
+                else:
+                    raise TypeError('input key '+key+' is not of the same type\
+                                    as the one in the class4gl_object')
+
+            elif type(self.__dict__[key]) == dict:
+                # if the data type is a dictionary, we update the
+                # dictionary 
+                self.__dict__[key] = {self.__dict__[key] , data}
+                datakeys = list(data.keys())
+
+
+            # if source entry is not existing yet, we add it
+            if source not in self.sources.keys():
+                self.sources[source] = []
+
+
+            # self.logger.debug('updating section "'+\
+            #                  key+' ('+' '.join(datakeys)+')'\
+            #                  '" from source \
+            #                  "'+source+'"')
+
+            # Update the source dictionary: add the provided data keys to the
+            # specified source list
+            for datakey in datakeys:
+                # At first, remove the occurences of the keys in the other
+                # source lists
+                for sourcekey,sourcelist in self.sources.items():
+                    if key+':'+datakey in sourcelist:
+                        self.sources[sourcekey].remove(key+':'+datakey)
+                # Afterwards, add it to the current source list
+                self.sources[source].append(key+':'+datakey)
+
+
+        # # in case the datatype is a class4gl_input_pars, we update its keys
+        # # according to **kwargs dictionary
+        # if type(self.__dict__[datatype]) == class4gl_input_pars:
+        #     # add the data parameters to the datatype object dictionary of the
+        #     # datatype
+        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
+        #                                        **kwargs}
+        # # in case, the datatype reflects a dataframe, we update the columns according
+        # # to the *args list
+        # elif type(self.__dict__[datatype]) == pd.DataFrame:
+        #     for dataframe in args:
+        #         for column in list(dataframe.columns):
+        #             self.__dict__[datatype][column] = dataframe[column]
+        
+
+    def get_profile(self,IOBJ, *args, **argv):
+        # if type(IOBJ) == wyoming:
+        self.get_profile_wyoming(IOBJ,*args,**argv)
+        # else:
+        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
+        
+    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
+        """ 
+            Purpose: 
+                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
+
+            Input:
+                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
+                function will take the profile at the stream's current
+                position. 
+                2. air_ap_mode: which air profile do we take? 
+                    - b : best
+                    - l : according to lower limit for the mixed-layer height
+                            estimate
+                    - u : according to upper limit for the mixed-layer height
+                            estimate
+
+
+            Output:
+                1. all single-value parameters are stored in the
+                   class4gl_input.pars object
+                2. the souding profiles are stored in the in the
+                   class4gl_input.air_balloon dataframe
+                3. modified sounding profiles for which the mixed layer height
+                   is fitted
+                4. ...
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = 'wyoming'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = air_ap_mode
+
+        """ Temporary variables used for output """
+        # single value parameters derived from the sounding profile
+        dpars = dict()
+        # profile values
+        air_balloon = pd.DataFrame()
+        # fitted profile values
+        air_ap = pd.DataFrame()
+        
+        string = wy_strm.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = wy_strm.current.find_next('pre').find_next('pre').text
+        
+        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
+        dpars = {**dpars,
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+               }
+        
+        # we get weird output when it's a numpy Timestamp, so we convert it to
+        # pd.datetime type
+
+        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
+        dpars['STNID'] = dpars['Station number']
+
+        # altitude above ground level
+        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        # absolute humidity in g/kg
+        air_balloon['q']= (air_balloon.MIXR/1000.) \
+                              / \
+                             (air_balloon.MIXR/1000.+1.)
+        # convert wind speed from knots to m/s
+        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
+        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        
+        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+
+        
+
+        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+
+        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
+        air_balloon['p'] = air_balloon.PRES*100.
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        #is_valid = (air_balloon.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_balloon.index[is_valid].values
+        print(valid_indices)
+
+        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+
+        air_balloon['t'] = air_balloon['TEMP']+273.15
+        air_balloon['theta'] = (air_balloon.t) * \
+                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+
+
+        if np.isnan(dpars['h']):
+            dpars['Ps'] = np.nan
+
+
+
+
+        if ~np.isnan(dpars['h']):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u 
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+            
+
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        air_ap_head['HGHT'] = air_ap_head['z'] \
+                                + \
+                                np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = dpars['theta']
+        z_low =     dpars['h']
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+
+        
+        dpars['lat'] = dpars['Station latitude']
+        dpars['latitude'] = dpars['lat']
+        
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        dpars['longitude'] = dpars['Station longitude']
+        
+        dpars['ldatetime'] = dpars['datetime'] \
+                            + \
+                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+
+        # # we make a pars object that is similar to the destination object
+        # pars = model_input()
+        # for key,value in dpars.items():
+        #     pars.__dict__[key] = value
+
+
+        # we round the columns to a specified decimal, so that we get a clean
+        # output format for yaml
+        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
+                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
+                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+# 
+        for column,decimal in decimals.items():
+            air_balloon[column] = air_balloon[column].round(decimal)
+            air_ap[column] = air_ap[column].round(decimal)
+
+        self.update(source='wyoming',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+
+        
+    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
+    
+        """
+        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
+                 according to the position (lat lon) and the class datetime and timespan
+                 globaldata should be a globaldata multifile object
+        
+        Input: 
+            - globaldata: this is the library object
+            - only_keys: only extract specified keys
+            - exclude_keys: do not inherit specified keys
+        """
+        classdatetime      = np.datetime64(self.pars.datetime_daylight)
+        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
+                                           + \
+                                           dt.timedelta(seconds=self.pars.runtime)\
+                                          )
+
+
+        # # list of variables that we get from global ground data
+        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
+        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
+        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
+        #                 'texture', 'itex', 'isoil', 'BR',
+        #                 'b', 'cveg',
+        #                 'C1sat', 
+        #                 'C2ref', 'p', 'a',
+        #                 ] #globaldata.datasets.keys():
+
+        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
+        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
+
+
+        if type(globaldata) is not data_global:
+            raise TypeError("Wrong type of input library") 
+
+        # by default, we get all dataset keys
+        keys = list(globaldata.datasets.keys())
+
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
+        # # In case there is surface pressure, we also calculate the half-level
+        # # and full-level pressure fields
+        # if ('sp' in keys):
+        #     keys.append('pfull')
+        #     keys.append('phalf')
+
+        # If specified, we only take the keys that are in only_keys
+        if only_keys is not None:
+            for key in keys:
+                if key not in only_keys:
+                    keys.remove(key)
+                
+        # If specified, we take out keys that are in exclude keys
+        if exclude_keys is not None:
+            for key in keys:
+                if key in exclude_keys:
+                    keys.remove(key)
+
+        # we set everything to nan first in the pars section (non-profile parameters
+        # without lev argument), so that we can check afterwards whether the
+        # data is well-fetched or not.
+        for key in keys:
+            if not ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None) and \
+                ('lev' in globaldata.datasets[key].page[key].dims)):
+                self.update(source='globaldata',pars={key:np.nan})
+            # # we do not check profile input for now. We assume it is
+            # # available
+            #else:
+            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
+
+        self.logger.debug('getting keys "'+', '.join(keys)+'\
+                          from global data')
+
+        for key in keys:
+            # If we find it, then we obtain the variables
+            if ((key in globaldata.datasets) and \
+                (globaldata.datasets[key].page is not None)):
+
+                # check first whether the dataset has a height coordinate (3d space)
+                if 'lev' in globaldata.datasets[key].page[key].dims:
+
+                    # first, we browse to the correct file that has the current time
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+                        globaldata.datasets[key].browse_page(time=classdatetime)
+
+                    
+                    if (globaldata.datasets[key].page is not None):
+                        # find longitude and latitude coordinates
+                        ilats = (np.abs(globaldata.datasets[key].page.lat -
+                                        self.pars.latitude) < 0.5)
+                        ilons = (np.abs(globaldata.datasets[key].page.lon -
+                                        self.pars.longitude) < 0.5)
+                        
+                        # if we have a time dimension, then we look up the required timesteps during the class simulation
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            itimes = ((globaldata.datasets[key].page.time >= \
+                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            # In case we didn't find any correct time, we take the
+                            # closest one.
+                            if np.sum(itimes) == 0.:
+
+
+                                classdatetimemean = \
+                                    np.datetime64(self.pars.datetime_daylight + \
+                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
+                                                ))
+
+                                dstimes = globaldata.datasets[key].page.time
+                                time = dstimes.sel(time=classdatetimemean,method='nearest')
+                                itimes = (globaldata.datasets[key].page.time ==
+                                          time)
+                                
+                        else:
+                            # we don't have a time coordinate so it doesn't matter
+                            # what itimes is
+                            itimes = 0
+
+                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+
+                        # over which dimensions we take a mean:
+                        dims = globaldata.datasets[key].page[key].dims
+                        namesmean = list(dims)
+                        namesmean.remove('lev')
+                        idxmean = [dims.index(namemean) for namemean in namesmean]
+                        
+                        value = \
+                        globaldata.datasets[key].page[key].isel(time=itimes,
+                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+                        # Ideally, source should be equal to the datakey of globaldata.library 
+                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+                        #  but therefore the globaldata class requires a revision to make this work
+                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+
+                else:
+                    # this procedure is for reading the ground fields (2d space). 
+                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
+
+    
+                    if 'time' in list(globaldata.datasets[key].page[key].dims):
+    
+                       # first, we browse to the correct file
+                       #print(key)
+                       globaldata.datasets[key].browse_page(time=classdatetime)
+    
+                    if globaldata.datasets[key].page is not None:
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - self.pars.latitude))
+                        ilat = np.where((DIST) == np.min(DIST))[0][0]
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - self.pars.longitude))
+                        ilon = np.where((DIST) == np.min(DIST))[0][0]
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
+                                - (self.pars.latitude + 0.5)))
+                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmax = ilat
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
+                                - (self.pars.longitude  + 0.5)))
+                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmax = ilon
+                        
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lat.values\
+                                - (self.pars.latitude - 0.5)))
+                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                            ilatmin = ilat
+                        DIST = \
+                        np.abs((globaldata.datasets[key].page.lon.values\
+                                - (self.pars.longitude  - 0.5)))
+                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                            ilonmin = ilon        
+                        
+                        if ilatmin < ilatmax:
+                            ilatrange = range(ilatmin,ilatmax+1)
+                        else:
+                            ilatrange = range(ilatmax,ilatmin+1)
+                            
+                        if ilonmin < ilonmax:
+                            ilonrange = range(ilonmin,ilonmax+1)
+                        else:
+                            ilonrange = range(ilonmax,ilonmin+1)     
+                            
+                        if 'time' in list(globaldata.datasets[key].page[key].dims):
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                idatetime += 1
+                            
+                            classdatetimeend = np.datetime64(\
+                                                             self.pars.datetime +\
+                                                             dt.timedelta(seconds=self.pars.runtime)\
+                                                            ) 
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+                            if key in ['wg', 'w2']:
+                                idatetime = idatetime - 1
+                                idatetimeend = idatetimeend - 1
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['Tsoil','T2']:
+                                idatetimeend = idatetime
+                            
+                            idts = range(idatetime,idatetimeend+1)
+                            
+                            count = 0
+                            self.__dict__[key] = 0.
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    for iidts in idts:
+                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
+                                        count += 1
+                            value = value/count
+                            self.update(source='globaldata',pars={key:value.item()})
+                                
+                        else:
+                                
+                            count = 0
+                            value = 0.
+                            for iilat in ilatrange:
+                                for iilon in ilonrange:
+                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
+                                    count += 1
+                            value = value/count                        
+
+                            self.update(source='globaldata',pars={key:value.item()})
+
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            self.logger.debug('also update LAI based on LAIpixel and cveg') 
+            # I suppose LAI pixel is already determined in the previous
+            # procedure. Anyway...
+            key = 'LAIpixel'
+
+            if globaldata.datasets[key].page is not None:
+                # first, we browse to the correct file that has the current time
+                if 'time' in list(globaldata.datasets[key].page[key].dims):
+                    globaldata.datasets[key].browse_page(time=classdatetime)
+            
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - self.pars.latitude))
+                ilat = np.where((DIST) == np.min(DIST))[0][0]
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - self.pars.longitude))
+                ilon = np.where((DIST) == np.min(DIST))[0][0]
+                 
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude + 0.5)))
+                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmax = ilat
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values \
+                        - (self.pars.longitude  + 0.5)))
+                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmax = ilon
+                
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lat.values\
+                        - (self.pars.latitude - 0.5)))
+                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
+                    ilatmin = ilat
+                DIST = \
+                np.abs((globaldata.datasets[key].page.lon.values\
+                        - (self.pars.longitude  - 0.5)))
+                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
+                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
+                    ilonmin = ilon        
+                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                
+                
+                if ilatmin < ilatmax:
+                    ilatrange = range(ilatmin,ilatmax+1)
+                else:
+                    ilatrange = range(ilatmax,ilatmin+1)
+                    
+                if ilonmin < ilonmax:
+                    ilonrange = range(ilonmin,ilonmax+1)
+                else:
+                    ilonrange = range(ilonmax,ilonmin+1)           
+                
+                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
+                LAIpixel = 0.
+                count = 0
+                for iilat in [ilat]: #ilatrange
+                    for iilon in [ilon]: #ilonrange
+                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
+                        
+                                        
+                        # if np.isnan(tarray[idatetime]):
+                        #     print("interpolating GIMMS LAIpixel nan value")
+                        #     
+                        #     mask = np.isnan(tarray)
+                        #     
+                        #     #replace each nan value with a interpolated value
+                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+                        #         
+                        #     else:
+                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
+                    
+                        #         tarray *= np.nan 
+                        
+                        count += 1
+                        #tarray_res += tarray
+                LAIpixel = LAIpixel/count
+                
+                count = 0
+                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
+  
+                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
+                #print('LAIpixel:',self.__dict__['LAIpixel'])
+                #print('cveg:',self.__dict__['cveg'])
+                
+                # finally, we rescale the LAI according to the vegetation
+                # fraction
+                value = 0. 
+                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
+                   value =self.pars.LAIpixel/self.pars.cveg
+                else:
+                    # in case of small vegetation fraction, we take just a standard 
+                    # LAI value. It doesn't have a big influence anyway for
+                    # small vegetation
+                    value = 2.
+                #print('LAI:',self.__dict__['LAI'])
+                self.update(source='globaldata',pars={'LAI':value}) 
+
+
+        # in case we have 'sp', we also calculate the 3d pressure fields at
+        # full level and half level
+        if ('sp' in keys) and ('sp' in self.pars.__dict__):
+            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
+
+            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # hydrostatic thickness of each model layer
+            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
+            # # dz = rhodz/(R * T / pfull)
+
+
+            # # subsidence multiplied by density. We calculate the subsidence of
+            # # the in class itself
+            # wrho = np.zeros_like(phalf)
+            # wrho[-1] = 0. 
+
+            # for ihlev in range(0,wrho.shape[0]-1):
+            #     # subsidence multiplied by density is the integral of
+            #     # divergences multiplied by the layer thicknessies
+            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
+            #                     self.air_ac['divU_y'][ihlev:]) * \
+            #                    delpdgrav[ihlev:]).sum()
+
+
+            
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'p':list(pfull)}))
+            self.update(source='globaldata',\
+                        air_ach=pd.DataFrame({'p':list(phalf)}))
+            self.update(source='globaldata',\
+                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
+            # self.update(source='globaldata',\
+            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
+
+    def check_source(self,source,check_only_sections=None):
+        """ this procedure checks whether data of a specified source is valid.
+
+        INPUT:
+            source: the data source we want to check
+            check_only_sections: a string or list with sections to be checked
+        OUTPUT:
+            returns True or False
+        """
+
+        # we set source ok to false as soon as we find a invalid input
+        source_ok = True
+
+        # convert to a single-item list in case of a string
+        check_only_sections_def = (([check_only_sections]) if \
+                                   type(check_only_sections) is str else \
+                                    check_only_sections)
+                                  
+        if source not in self.sources.keys():
+            self.logger.info('Source '+source+' does not exist')
+            source_ok = False
+
+        for sectiondatakey in self.sources[source]:                             
+            section,datakey = sectiondatakey.split(':')                         
+            if ((check_only_sections_def is None) or \
+                (section in check_only_sections_def)):                          
+                checkdatakeys = []
+                if type(self.__dict__[section]) is pd.DataFrame:
+                    checkdata = self.__dict__[section]
+                elif type(self.__dict__[section]) is model_input:
+                    checkdata = self.__dict__[section].__dict__
+
+                if (datakey not in checkdata):                              
+                    # self.logger.info('Expected key '+datakey+\
+                    #                  ' is not in parameter input')                        
+                    source_ok = False                                           
+                elif (checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True):                    
+        
+                    # self.logger.info('Key value of "'+datakey+\
+                    #                  '" is invalid: ('+ \
+                    # str(self.__dict__[section].__dict__[datakey])+')')         
+                    source_ok = False
+
+        return source_ok
+
+    def check_source_globaldata(self):
+        """ this procedure checks whether all global parameter data is
+        available, according to the keys in the self.sources"""
+
+        source_globaldata_ok = True
+
+        #self.get_values_air_input()
+
+        # and now we can get the surface values
+        #class_settings = class4gl_input()
+        #class_settings.set_air_input(input_atm)
+        
+        # we only allow non-polar stations
+        if not (self.pars.lat <= 60.):
+            source_globaldata_ok = False
+            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+        
+        # check lat and lon
+        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
+            source_globaldata_ok = False
+            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+        else:
+            # we only check the ground parameter data (pars section). The 
+            # profile data (air_ap section) are supposed to be valid in any 
+            # case.
+            source_ok = self.check_source(source='globaldata',\
+                                          check_only_sections=['air_ac',\
+                                                               'air_ap',\
+                                                               'pars'])
+            if not source_ok:
+                source_globaldata_ok = False
+        
+            # Additional check: we exclude desert-like
+            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
+                source_globaldata_ok = False
+                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
+                source_globaldata_ok = False
+                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+            elif self.pars.cveg < 0.02:
+                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                source_globaldata_ok = False
+
+        return source_globaldata_ok
+
+
+class c4gli_iterator():
+    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
+    
+        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
+    """
+    def __init__(self,file):
+        # take file as IO stream
+        self.file = file
+        self.yaml_generator = yaml.load_all(file)
+        self.current_dict = {}
+        self.current_class4gl_input = class4gl_input()
+        separator = self.file.readline() # this is just dummy
+        self.header = file.readline()
+        if self.header != '# CLASS4GL record; format version: 0.1\n':
+            raise NotImplementedError("Wrong format version: '"+self.header+"'")
+    def __iter__(self):
+        return self
+    def __next__(self):
+        self.current_dict = self.yaml_generator.__next__()
+        self.current_class4gl_input.load_yaml_dict(self.current_dict)
+        return self.current_class4gl_input
+
+
+
+#get_cape and lift_parcel are adapted from the SkewT package
+    
+class gl_dia(object):
+    def get_lifted_index(self,timestep=-1):
+        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
+    
+#from SkewT
+#def get_lcl(startp,startt,startdp,nsteps=101):
+#    from numpy import interp
+#    #--------------------------------------------------------------------
+#    # Lift a parcel dry adiabatically from startp to LCL.
+#    # Init temp is startt in K, Init dew point is stwrtdp,
+#    # pressure levels are in Pa    
+#    #--------------------------------------------------------------------
+#
+#    assert startdp<=startt
+#
+#    if startdp==startt:
+#        return np.array([startp]),np.array([startt]),np.array([startdp]),
+#
+#    # Pres=linspace(startp,60000.,nsteps)
+#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
+#
+#    # Lift the dry parcel
+#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
+#    # Mixing ratio isopleth
+#    starte=VaporPressure(startdp)
+#    startw=MixRatio(starte,startp)
+#    e=Pres*startw/(.622+startw)
+#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
+#
+#    # Solve for the intersection of these lines (LCL).
+#    # interp requires the x argument (argument 2)
+#    # to be ascending in order!
+#    P_lcl=interp(0.,T_iso-T_dry,Pres)
+#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
+#
+#    # # presdry=linspace(startp,P_lcl)
+#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
+#
+#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
+#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
+#
+#    return P_lcl,T_lcl
+
+
+
+def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
+
+
+
+#from class
+def get_lcl(startp,startt,startqv):
+        # Find lifting condensation level iteratively
+    lcl = 20.
+    RHlcl = 0.5
+    
+    itmax = 30
+    it = 0
+    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHd
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self):
+       self.status = 'init'
+       self.found = False
+       self.DT = None
+       self.current = None
+       #self.mode = 'b'
+       self.profile_type = 'wyoming'  
+       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+         
+    def set_STNM(self,STNM):
+        self.__init__()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+        self.current = self.sounding_series.find('h2')
+        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+        
+        # go through other files and find first sounding when year is not specified
+        self.iFN=self.iFN+1
+        while keepsearching:
+            self.FN = self.FILES[self.iFN]
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            self.iFN=self.iFN+1
+            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+        self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        if self.found and get_atm:
+            self.get_values_air_input()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        #print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                if get_atm:
+                    self.get_values_air_input()
+                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        if self.found and get_atm:
+            self.get_values_air_input()
+       
+
+
+    def get_values_air_input(self,latitude=None,longitude=None):
+
+        # for iDT,DT in enumerate(DTS):
+        
+            #websource = urllib.request.urlopen(webpage)
+        #soup = BeautifulSoup(open(webpage), "html.parser")
+        
+       
+        #workaround for ...last line has 
 which results in stringlike first column
+        string = self.current.find_next('pre').text
+        string = string.split('\n')[:-1]
+        string =  '\n'.join(string)
+        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
+        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
+        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
+        
+        #string =  soup.pre.next_sibling.next_sibling
+        
+        string = self.current.find_next('pre').find_next('pre').text
+
+        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
+        #PARAMS.insert(0,'date',DT)
+
+        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
+        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
+        
+        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
+        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
+        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
+        HAGL = HGHT - np.float(PARAMS['Station elevation'])
+        ONE_COLUMN.insert(0,'HAGL',HAGL)
+
+        
+        
+        
+        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
+        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
+        ONE_COLUMN.insert(0,'QABS',QABS)
+        
+        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
+
+        #mixed layer potential temperature
+        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
+
+        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
+        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
+        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
+
+        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
+        BLHV = np.max((BLHV,10.))
+        BLHVu = np.max((BLHVu,10.))
+        BLHVd = np.max((BLHVd,10.))
+        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
+
+        #security values for mixed-layer jump values dthetav, dtheta and dq
+        
+        # fit new profiles taking the above-estimated mixed-layer height
+        ONE_COLUMNNEW = []
+        for BLH in [BLHV,BLHVu,BLHVd]:
+            ONE_COLUMNNEW.append(pd.DataFrame())
+            
+            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+            
+            listHAGLNEW = list(HAGLNEW)
+            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+                
+                # get index of lowest valid observation. This seems to vary
+                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+                if len(idxvalid) > 0:
+                    #print('idxvalid',idxvalid)
+                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+                    else:
+                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
+                else:
+                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+                    #print(col,meanabl)
+               
+                
+                # if col == 'PRES':
+                #     meanabl =  
+            
+                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+                #THTVM = np.nanmean(THTV[HAGL <= BLH])
+                #print("new_pro_h",new_pro_h)
+                # calculate jump ath the top of the mixed layer
+                if col in ['THTA','THTV',]:
+                    #for moisture
+                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+                    if len(listHAGLNEW) > 4:
+                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+                        dtheta = np.max((0.1,dtheta_pre))
+                        #meanabl = meanabl - (dtheta - dtheta_pre)
+                        #print('dtheta_pre',dtheta_pre)
+                        #print('dtheta',dtheta)
+                        #print('meanabl',meanabl)
+                        #stop
+                        
+                    else:
+                        dtheta = np.nan
+                else:
+                    if len(listHAGLNEW) > 4:
+                        #for moisture (it can have both negative and positive slope)
+                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+                    else:
+                        dtheta = np.nan
+                #print('dtheta',dtheta)
+                
+                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+            
+                
+                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+                
+            #QABSM = np.nanmean(QABS[HAGL <= BLH])
+            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+            
+        # we just make a copy of the fields, so that it can be read correctly by CLASS 
+        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+            
+            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+        
+            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+
+
+        # assign fields adopted by CLASS
+        if self.mode == 'o': #original 
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'b':
+            PARAMS.insert(0,'h',   np.float(BLHV))
+        elif self.mode == 'u':
+            PARAMS.insert(0,'h',   BLHVu)
+        elif self.mode == 'd':
+            PARAMS.insert(0,'h',   BLHVd)
+        else:
+            PARAMS.insert(0,'h',   BLHV)
+            
+
+        try:
+            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+        except:
+            print("could not convert latitude coordinate")
+            PARAMS.insert(0,'latitude', np.nan)
+            PARAMS.insert(0,'lat', np.nan)
+        try:
+            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+            PARAMS.insert(0,'lon', 0.)
+        except:
+            print("could not convert longitude coordinate")
+            PARAMS.insert(0,'longitude', np.nan)
+            PARAMS.insert(0,'lon', 0.)
+
+        if latitude is not None:
+            print('overwriting latitude with specified value')
+            PARAMS['latitude'] = np.float(latitude)
+            PARAMS['lat'] = np.float(latitude)
+        if longitude is not None:
+            print('overwriting longitude with specified value')
+            PARAMS['longitude'] = np.float(longitude)
+        try:
+            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            # This is the nearest datetime when sun is up (for class)
+            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+            # apply the same time shift for UTC datetime
+            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+            
+        except:
+            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+
+        
+
+        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+        # as we are forcing lon equal to zero this is is expressed in local suntime
+        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+
+           
+        ONE_COLUMNb = ONE_COLUMNNEW[0]
+        ONE_COLUMNu = ONE_COLUMNNEW[1]
+        ONE_COLUMNd = ONE_COLUMNNEW[2]
+        
+
+        THTVM = np.nanmean(THTV[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+        
+        QABSM = np.nanmean(QABS[HAGL <= BLHV])
+        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
+        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
+        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
+
+        BLHVe = abs(BLHV - BLHVu)
+        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
+
+        #PARAMS.insert(0,'dq',0.)
+        
+        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
+        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+        
+        if self.mode == 'o': #original 
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
+        elif self.mode == 'b': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNb
+            BLCOLUMN = ONE_COLUMNb
+        elif self.mode == 'u': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNu
+            BLCOLUMN = ONE_COLUMNu
+        elif self.mode == 'd': # best BLH
+            USE_ONECOLUMN = ONE_COLUMNd
+            BLCOLUMN = ONE_COLUMNd
+        else:
+            USE_ONECOLUMN = ONE_COLUMN
+            BLCOLUMN = ONE_COLUMNb
+
+        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
+        # print(BLCOLUMN['HAGL'][lt6000])
+        # print(BLCOLUMN['HAGL'][lt2500])
+        # 
+        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+
+        #print(BLCOLUMN['HAGL'][lt2500])
+        PARAMS.insert(0,'OK',
+                      ((BLHVe < 200.) and 
+                       ( len(np.where(lt6000)[0]) > 5) and
+                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+                      )
+                     )
+
+        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+        
+        
+        PARAMS = PARAMS.T
+
+        
+        self.PARAMS = PARAMS
+        self.ONE_COLUMN = USE_ONECOLUMN
+        # if self.mode == 'o': #original 
+        #     self.ONE_COLUMN = ONE_COLUMN
+        # elif self.mode == 'b': # best BLH
+        #     self.ONE_COLUMN = ONE_COLUMNb
+        # elif self.mode == 'u':# upper BLH
+        #     self.ONE_COLUMN = ONE_COLUMNu
+        # elif self.mode == 'd': # lower BLH
+        #     self.ONE_COLUMN=ONE_COLUMNd
+        # else:
+        #     self.ONE_COLUMN = ONE_COLUMN
+
diff --git a/lib/data_global.py b/lib/data_global.py
new file mode 100644
index 0000000..9c3d9b5
--- /dev/null
+++ b/lib/data_global.py
@@ -0,0 +1,936 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: Hendrik Wouters
+
+Purpose: provides class routines for ground and atmosphere conditions used for
+the CLASS miced-layer model
+
+Usage:
+    from data_global import data_global
+    from class4gl import class4gl_input
+    from data_soundings import wyoming
+
+    # create a data_global object and load initial data pages
+    globaldata = data_global()
+    globaldata.load_datasets()
+    # create a class4gl_input object
+    c4gli = class4gl_input()
+    # Initialize it with profile data. We need to do this first. Actually this
+    # will set the coordinate parameters (datetime, latitude, longitude) in
+    # class4gl_input.pars.__dict__, which is required to read point data from
+    # the data_global object.
+
+    # open a Wyoming stream for a specific station
+    wy_strm = wyoming(STNM=91376)
+    # load the first profile
+    wy_strm.find_first()
+    # load the profile data into the class4gl_input object
+    c4gli.get_profile_wyoming(wy_strm)
+    
+    # and finally, read the global input data for this profile
+    c4gli.get_global_input(globaldata)
+
+
+"""
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+#import pynacolada as pcd
+import pandas as pd
+import xarray as xr
+import os
+import glob
+import sys
+import errno
+import warnings
+import logging
+
+
+#formatter = logging.Formatter()
+logging.basicConfig(format='%(asctime)s - \
+                               %(name)s - \
+                               %(levelname)s - \
+                               %(message)s')
+
+class book(object):
+    """ this is a class for a dataset spread over multiple files. It has a
+    similar purpose  open_mfdataset, but only 1 file (called current 'page')
+    one is loaded at a time. This saves precious memory.  """
+    def __init__(self,fn,concat_dim = None,debug_level=None):
+        self.logger = logging.getLogger('book')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        # filenames are expanded as a list and sorted by filename
+        self.pages = glob.glob(fn); self.pages.sort()
+        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
+        if len(self.pages) == 0:
+            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
+        self.ipage = -1; self.page = None
+        self.renames = {} # each time when opening a file, a renaming should be done.
+        self.set_page(0)
+
+        # we consider that the outer dimension is the one we concatenate
+        self.concat_dim = concat_dim
+        if self.concat_dim is None:
+            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
+
+    # this wraps the xarray sel-commmand
+    def sel(*args, **kwargs):
+        for dim in kwargs.keys():
+            if dim == self.concat_dim:
+                self.browse_page(**{dim: kwargs[dim]})
+        return page.sel(*args,**kwargs)
+
+
+    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
+    #def __getattr__(self,attr):
+    #    orig_attr = self.page.__getattribute__(attr)
+    #    if callable(orig_attr):
+    #        def hooked(*args, **kwargs):
+    #            for dim in kwargs.keys():
+    #                if dim == self.concat_dim:
+    #                    self.browse_page(**{dim: kwargs[dim]})
+    #
+    #            result = orig_attr(*args, **kwargs)
+    #            # prevent wrapped_class from becoming unwrapped
+    #            if result == self.page:
+    #                return self
+    #            self.post()
+    #            return result
+    #        return hooked
+    #    else:
+    #        return orig_attr
+
+    def set_renames(self,renames):
+        #first, we convert back to original names, and afterwards, we apply the update of the renames.
+        reverse_renames = dict((v,k) for k,v in self.renames.items())
+        self.renames = renames
+        if self.page is not None:
+            self.page = self.page.rename(reverse_renames)
+            self.page = self.page.rename(self.renames)
+
+    def set_page(self,ipage,page=None):
+        """ this sets the right page according to ipage:
+                - We do not switch the page if we are already at the right one
+                - we set the correct renamings (level -> lev, latitude -> lat,
+                etc.)
+                - The dataset is also squeezed.
+        """
+
+        if ((ipage != self.ipage) or (page is not None)):
+
+            if self.page is not None:
+                self.page.close()
+
+            self.ipage = ipage
+            if page is not None:
+                self.page = page
+            else:
+                if self.ipage == -1:
+                   self.page = None
+                else:
+                    #try:
+
+                    self.logger.info("Switching to page "+str(self.ipage)+': '\
+                                     +self.pages[self.ipage])
+                    self.page = xr.open_dataset(self.pages[self.ipage])
+
+
+            # do some final corrections to the dataset to make them uniform
+            if self.page is not None:
+               if 'latitude' in self.page.dims:
+#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
+               if 'level' in self.page.dims:
+                   self.page = self.page.rename({'level':'lev'})
+
+               self.page = self.page.rename(self.renames)
+               self.page = self.page.squeeze(drop=True)
+
+    def browse_page(self,rewind=2,**args):
+
+        # at the moment, this is only tested with files that are stacked according to the time dimension.
+        dims = args.keys()
+
+
+        if self.ipage == -1:
+            self.set_page(0)
+
+        found = False
+        iipage = 0
+        startipage = self.ipage - rewind
+        while (iipage < len(self.pages)) and not found:
+            ipage = (iipage+startipage) % len(self.pages)
+            for dim in args.keys():
+                this_file = True
+
+                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
+                if 'dims' not in self.__dict__:
+                    self.dims = {}
+                if dim not in self.dims.keys():
+                    self.dims[dim] = [None]*len(self.pages)
+
+                if self.dims[dim][ipage] is None:
+                    self.logger.info('Loading coordinates of dimension "'+dim+\
+                                     '" of page "' +str(ipage)+'".')
+                    self.set_page(ipage)
+                    # print(ipage)
+                    # print(dim)
+                    # print(dim,self.page[dim].values)
+                    self.dims[dim][ipage] = self.page[dim].values
+
+                # determine current time range of the current page
+                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
+                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
+
+                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
+                    this_file = False
+
+            if this_file:
+                found = True
+                self.set_page(ipage)
+            else:
+
+                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
+                #    iipage = len(self.pages) # we stop searching
+
+                iipage += 1
+
+        if not found:
+            self.logger.info("Page not found. Setting to page -1")
+            #iipage = len(self.pages) # we stop searching further
+            self.set_page(-1)
+
+        if self.ipage != -1:
+            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
+        else:
+            self.logger.debug("I'm now at page "+ str(self.ipage))
+
+
+class data_global(object):
+    def __init__(self,sources= {
+        # # old gleam
+        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
+        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
+        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
+        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
+        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
+        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
+        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
+        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
+        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
+        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
+        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
+        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
+        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
+        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
+        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
+        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
+        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
+        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
+        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
+        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
+        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
+        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
+        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
+        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
+        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
+        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
+        },debug_level=None):
+        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
+        self.sources = sources
+        self.datarefs = {}
+        self.datasets = {}
+        self.datetime = dt.datetime(1981,1,1)
+
+        self.logger = logging.getLogger('data_global')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+        self.debug_level = debug_level
+
+        warnings.warn('omitting pressure field p and advection')
+
+    def in_library(self,fn):
+        if fn not in self.library.keys():
+            return False
+        else:
+            print("Warning: "+fn+" is already in the library.")
+            return True
+
+    def add_to_library(self,fn):
+        if not self.in_library(fn):
+            print("opening: "+fn)
+            self.library[fn] = \
+                book(fn,concat_dim='time',debug_level=self.debug_level)
+
+            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
+            #if 'latitude' in self.library[fn].variables:
+            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
+
+
+    # default procedure for loading datasets into the globaldata library
+    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
+        if type(varssource) is str:
+            varssource = [varssource]
+        if type(varsdest) is str:
+            varsdest = [varsdest]
+
+        self.add_to_library(input_fn)
+
+        if varssource is None:
+            varssource = []
+            for var in self.sources[input_fn].variables:
+                avoid = \
+                ['lat','lon','latitude','longitude','time','lev','level']
+                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
+                    varssource.append(var)
+
+        if varsdest is None:
+            varsdest = varssource
+
+        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        for ivar,vardest in enumerate(varsdest):
+            varsource = varssource[ivar]
+            print('setting '+vardest+' as '+varsource+' from '+input_fn)
+
+            if vardest in self.datarefs.keys():
+                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
+            #self.add_to_library(fn,varsource,vardest)
+            if vardest != varsource:
+                libkey = input_fn+'.'+varsource+'.'+vardest
+                if libkey not in self.library.keys():
+                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
+                    self.library[libkey] = book(input_fn,\
+                                                debug_level=self.debug_level)
+                    self.library[libkey].set_renames({varsource: vardest})
+
+                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+            else:
+                self.datarefs[vardest] = input_fn
+                self.datasets[vardest] =self.library[self.datarefs[vardest]]
+
+            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
+            #     print('Warning: '+ vardest "not in " + input_fn)
+
+
+
+    def load_datasets(self,sources = None,recalc=0):
+
+        if sources is None:
+            sources = self.sources
+        for key in sources.keys():
+            #datakey,vardest,*args = key.split(':')
+            datakey,vardest = key.split(':')
+            #print(datakey)
+
+            fnvarsource = sources[key].split(':')
+            if len(fnvarsource) > 2:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource,fnargs = fnvarsource
+                fnargs = [fnargs]
+            elif len(fnvarsource) > 1:
+                #fn,varsource,*fnargs = fnvarsource
+                fn,varsource = fnvarsource
+                fnargs = []
+            else:
+                fn = sources[key]
+                varsource = vardest
+            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
+
+    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
+            # the default way of loading a 2d dataset
+            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
+                self.load_dataset_default(fn,varsource,vardest)
+            elif datakey == 'IGBPDIS':
+                if vardest == 'alpha':
+                    ltypes = ['W','B','H','TC']
+                    for ltype in ltypes:
+                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
+                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
+
+
+                    # landfr = {}
+                    # for ltype in ['W','B','H','TC']:
+                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
+
+
+
+                    keytemp = 'alpha'
+                    fnkeytemp = fn+':IGBPDIS:alpha'
+                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
+                        self.library[fnkeytemp]  = book(fnkeytemp,
+                                                        debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+                    else:
+                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
+                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
+                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
+                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
+                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
+                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
+                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
+
+                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+
+                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
+                        for ltype in ltypes:
+                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
+
+                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
+                        print('writing file to: '+fnkeytemp)
+                        os.system('rm '+fnkeytemp)
+                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
+                        self.library[fnkeytemp].close()
+
+
+                        self.library[fnkeytemp]  = \
+                            book(fnkeytemp,debug_level=self.debug_level)
+                        self.datasets[keytemp] = self.library[fnkeytemp]
+                        self.datarefs[keytemp] = fnkeytemp
+
+
+                else:
+                    self.load_dataset_default(fn,varsource,vardest)
+
+
+            elif datakey == 'GLAS':
+                self.load_dataset_default(fn,varsource,vardest)
+                if vardest == 'z0m':
+                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
+                elif vardest == 'z0h':
+                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
+            elif datakey == 'DSMW':
+
+
+                # Procedure of the thermal properties:
+                # 1. determine soil texture from DSMW/10.
+                # 2. soil type with look-up table (according to DWD/EXTPAR)
+                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
+                #    with parameter look-up table from Noilhan and Planton (1989).
+                #    Note: The look-up table is inspired on DWD/COSMO
+
+                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
+
+
+
+                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
+                self.load_dataset_default(fn,'DSMW')
+                print('calculating texture')
+                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
+                TEMP  = {}
+                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
+                TEMP3 = {}
+                for SPKEY in SPKEYS:
+
+
+                    keytemp = SPKEY+'_values'
+                    fnoutkeytemp = fnout+':DSMW:'+keytemp
+                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                    else:
+                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
+                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
+                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
+                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+                        # for faster computation, we need to get it to memory out of Dask.
+                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
+                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
+
+                # yes, I know I only check the last file.
+                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
+                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
+                        print('idx',idx,SPKEY)
+                        SEL = (TEMP2 == idx)
+                    #     print(idx,len(TEMP3))
+                        for SPKEY in SPKEYS:
+                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
+
+                    for SPKEY in SPKEYS:
+                        keytemp = SPKEY+'_values'
+                        fnoutkeytemp = fnout+':DSMW:'+keytemp
+                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
+                        os.system('rm '+fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
+                        self.datasets[SPKEY+'_values'].close()
+
+
+                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
+                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
+
+
+                keytemp = 'texture'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+                else:
+                    self.library[fn+':DSMW:texture'] = xr.Dataset()
+                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
+                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
+                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
+                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+
+                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
+
+                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
+                    zundef[zundef < 0] = np.nan
+                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
+                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
+
+                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+
+
+                    self.library[fnoutkeytemp]  = \
+                        book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
+                    self.datarefs[keytemp] =fn+':DSMW:texture'
+
+
+                print('calculating texture type')
+
+
+
+                keytemp = 'itex'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+                else:
+                    self.library[fnoutkeytemp] = xr.Dataset()
+                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+                    X = self.datasets['texture'].page['texture'].values*100
+                    X[pd.isnull(X)] = -9
+
+
+                    self.datasets[keytemp][keytemp].values = X
+
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
+                    self.datasets['itex'].close()
+
+
+                    self.library[fnoutkeytemp] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
+                    self.datarefs[keytemp] =fn+':DSMW:itex'
+
+
+                keytemp = 'isoil'
+                fnoutkeytemp=fnout+':DSMW:'+keytemp
+                isoil_reprocessed = False
+                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+                else:
+                    isoil_reprocessed = True
+                    print('calculating soil type')
+                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                    ITEX = self.datasets['itex'].page['itex'].values
+                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
+                    LOOKUP = [
+                              [-10 ,9],# ocean
+                              [0 ,7],# fine textured, clay (soil type 7)
+                              [20,6],# medium to fine textured, loamy clay (soil type 6)
+                              [40,5],# medium textured, loam (soil type 5)
+                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                              [80,3],# coarse textured, sand (soil type 3)
+                              [100,9],# coarse textured, sand (soil type 3)
+                            ]
+                    for iitex,iisoil in LOOKUP:
+                        ISOIL[ITEX > iitex] = iisoil
+                        print('iitex,iisoil',iitex,iisoil)
+
+
+                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+                    LOOKUP = [
+                              [9001, 1 ], # ice, glacier (soil type 1)
+                              [9002, 2 ], # rock, lithosols (soil type 2)
+                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                              [9,    9 ], # undefined (ocean)
+                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                              [9000, 9 ], # undefined (inland lake)
+                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                            ]
+                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
+
+                    CODE_VALUES[ITEX == 901200] = 9012
+                    for icode,iisoil in LOOKUP:
+                        ISOIL[CODE_VALUES == icode] = iisoil
+
+                    self.datasets['isoil']['isoil'].values = ISOIL
+                    os.system('rm '+fnoutkeytemp)
+                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
+                    self.datasets[keytemp].close()
+                    print('saved inbetween file to: '+fnoutkeytemp)
+
+                    self.library[fn+':DSMW:isoil'] = \
+                            book(fnoutkeytemp,debug_level=self.debug_level)
+                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                    self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+                #adopted from data_soil.f90 (COSMO5.0)
+                SP_LOOKUP = {
+                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
+                  # (by index)                                           loam                    loam                                water      ice
+                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+                  # Important note: For peat, the unknown values below are set equal to that of loam
+                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
+                  #error in table 2 of NP89: values need to be multiplied by e-6
+                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
+
+                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
+                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
+                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
+                }
+
+
+                # isoil_reprocessed = False
+                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
+
+                #     self.library[fn+':DSMW:isoil'] = \
+                #             book(fnoutkeytemp,debug_level=self.debug_level)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+                # else:
+                #     isoil_reprocessed = True
+                #     print('calculating soil type')
+                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
+                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
+                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
+                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
+
+
+
+
+                # this should become cleaner in future but let's hard code it for now.
+                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
+                print('calculating soil parameter')
+                DATATEMPSPKEY = {}
+                if (recalc < 1) and (isoil_reprocessed == False): 
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        keytemp = SPKEY
+                        fnoutkeytemp=fnout+':DSMW:'+keytemp
+                        self.library[fn+':DSMW:'+SPKEY] =\
+                                book(fnoutkeytemp,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
+                        self.datarefs[SPKEY] =fnoutkeytemp
+                else:
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+
+                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
+                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
+                    ISOIL = self.datasets['isoil'].page['isoil'].values
+                    print(np.where(ISOIL>0.))
+                    for i in range(11):
+                        SELECT = (ISOIL == i)
+                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+
+                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
+                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
+
+                        os.system('rm '+fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
+                        self.datasets[SPKEY].close()
+                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
+
+                        self.library[fn+':DSMW:'+SPKEY] = \
+                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
+                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
+                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
+
+
+            else:
+                self.load_dataset_default(fn,varsource,vardest)
+
+
+
+
+
+
+#
+#                 # only print the last parameter value in the plot
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'cala'
+#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#                 #inputs.append(cp.deepcopy(class_settings))
+#                 #var = 'crhoc'
+#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
+#                 #valnew = class_settings.__dict__[var]
+#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#     key = "CERES"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         CERES_start_date = dt.datetime(2000,3,1)
+#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+#
+#         var = 'cc'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#         print(class_settings.lat,class_settings.lon)
+#
+#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
+#
+#         input_nc.close()
+#
+
+
+#     key = "GIMMS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+#         print("Reading Leag Area Index from "+input_fn)
+#         var = 'LAI'
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+#
+#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+#
+#         if np.isnan(tarray[idatetime]):
+#             print("interpolating GIMMS cveg nan value")
+#
+#             mask = np.isnan(tarray)
+#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+#             else:
+#                 print("Warning. Could not interpolate GIMMS cveg nan value")
+#
+#         class_settings.__dict__[var] = tarray[idatetime]
+#
+#         input_nc.close()
+#
+#     key = "IGBPDIS_ALPHA"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         var = 'alpha'
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+#         print("Reading albedo from "+input_fn)
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#
+#         landfr = {}
+#         for ltype in ['W','B','H','TC']:
+#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+#
+#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+#
+#         alpha=0.
+#         for ltype in landfr.keys():
+#             alpha += landfr[ltype]*aweights[ltype]
+#
+#
+#         class_settings.__dict__[var] = alpha
+#         input_nc.close()
+#
+#
+#     key = "ERAINT_ST"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         print("Reading soil temperature from "+input_fn)
+#
+#         var = 'Tsoil'
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+#
+#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+#         var = 'T2'
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+#
+#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+#
+#
+#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+#
+#
+#         input_nc.close()
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #var = 'T2'
+#     #valold = class_settings.__dict__[var]
+#     #
+#     #class_settings.__dict__[var] = 305.
+#     #class_settings.__dict__['Tsoil'] = 302.
+#     #valnew = class_settings.__dict__[var]
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     #inputs.append(cp.deepcopy(class_settings))
+#     #
+#     #var = 'Lambda'
+#     #valold = class_settings.__dict__[var]
+#
+#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
+#     ## I need to ask Chiel.
+#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+#     #
+#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
+#     #class_settings.__dict__[var] = valnew
+#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+#
+#
+#
+#     key = "GLAS"
+#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+#
+#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+#         print("Reading canopy height for determining roughness length from "+input_fn)
+#         var = 'z0m'
+#
+#
+#         #plt.plot
+#
+#         input_nc = nc4.Dataset(input_fn,'r')
+#
+#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+#
+#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+#
+#         lowerlimit = 0.01
+#         if testval < lowerlimit:
+#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+#             class_settings.__dict__[var] = lowerlimit
+#         else:
+#             class_settings.__dict__[var] = testval
+#
+#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+#
+#
+#         input_nc.close()
+
+
+
+
+
diff --git a/lib/interface_functions.py b/lib/interface_functions.py
new file mode 100644
index 0000000..3e483f3
--- /dev/null
+++ b/lib/interface_functions.py
@@ -0,0 +1,506 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+#from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+#'_afternoon.yaml'
+def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+    filename = yaml_file.name
+    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+    #yaml_file = open(filename)
+
+    #print('going to next observation',filename)
+    yaml_file.seek(index_start)
+
+    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+
+    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer.write(buf)
+    filebuffer.close()
+    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+
+    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    print(command)
+    os.system(command)
+    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    record_dict = json.load(jsonstream)
+    jsonstream.close()
+    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+
+
+    if mode =='mod':
+        modelout = class4gl()
+        modelout.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        return modelout
+    elif mode == 'ini':
+
+ 
+        # datetimes are incorrectly converted to strings. We need to convert them
+        # again to datetimes
+        for key,value in record_dict['pars'].items():
+            # we don't want the key with columns that have none values
+            if value is not None: 
+                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+               # elif (type(value) == str):
+                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
+            if (value == 0.9e19) or (value == '.9e19'):
+                record_dict['pars'][key] = np.nan
+        for key in record_dict.keys():
+            #print(key)
+            if key in ['air_ap','air_balloon',]:
+                #NNprint('check')
+                for datakey,datavalue in record_dict[key].items():
+                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+
+        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+
+        c4gli = class4gl_input()
+        print(c4gli.logger,'hello')
+        c4gli.load_yaml_dict(record_dict)
+        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+
+class stations(object):
+    def __init__(self,path,suffix='ini',refetch_stations=False):
+
+        self.path = path
+
+        self.file = self.path+'/stations_list.csv'
+        if (os.path.isfile(self.file)) and (not refetch_stations):
+            self.table = pd.read_csv(self.file)
+        else:
+            self.table = self.get_stations(suffix=suffix)
+            self.table.to_csv(self.file)
+        
+        self.table = self.table.set_index('STNID')
+        #print(self.table)
+
+    def get_stations(self,suffix):
+        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        if len(stations_list_files) == 0:
+            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        stations_list_files.sort()
+        print(stations_list_files)
+        if len(stations_list_files) == 0:
+            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
+        stations_list = []
+        for stations_list_file in stations_list_files:
+            thisfile = open(stations_list_file,'r')
+            yamlgen = yaml.load_all(thisfile)
+            try:
+                first_record  = yamlgen.__next__()
+            except:
+                first_record = None
+            if first_record is not None:
+                stations_list.append({})
+                for column in ['STNID','latitude','longitude']:
+                    #print(first_record['pars'].keys())
+                    stations_list[-1][column] = first_record['pars'][column]
+                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
+            yamlgen.close()
+            thisfile.close()
+    
+        print(stations_list)
+        return pd.DataFrame(stations_list)
+
+class stations_iterator(object):
+    def __init__(self,stations):
+        self.stations = stations
+        self.ix = -1 
+    def __iter__(self):
+        return self
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.stations.table)) 
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_row(self,row):
+        self.ix = row
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+    def set_STNID(self,STNID):
+        self.ix = np.where((self.stations.table.index == STNID))[0][0]
+        print(self.ix)
+        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
+
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+    def close():
+        del(self.ix)
+
+class records_iterator(object):
+    def __init__(self,records):
+            
+        self.records = records
+        self.ix = -1 
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self,jump=1):
+        self.ix = (self.ix+jump) 
+        if self.ix >= len(self.records.index):
+            raise StopIteration
+        self.ix = np.mod(self.ix,len(self.records))
+        return self.records.index[self.ix], self.records.iloc[self.ix]
+    def __prev__(self):
+        return self.__next__(self,jump=-1)
+
+
+# #'_afternoon.yaml'
+# def get_record_yaml(yaml_file,index_start,index_end):
+#     filename = yaml_file.name
+#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
+#     #yaml_file = open(filename)
+# 
+#     #print('going to next observation',filename)
+#     yaml_file.seek(index_start)
+# 
+#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
+# 
+#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+#     filebuffer.write(buf)
+#     filebuffer.close()
+#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+#     
+#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+# 
+#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+#     print(command)
+#     os.system(command)
+#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
+#     record_dict = json.load(jsonstream)
+#     jsonstream.close()
+#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+#  
+#     # datetimes are incorrectly converted to strings. We need to convert them
+#     # again to datetimes
+#     for key,value in record_dict['pars'].items():
+#         # we don't want the key with columns that have none values
+#         if value is not None: 
+#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
+#            # elif (type(value) == str):
+#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+#                 
+#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
+# 
+#         if (value == 0.9e19) or (value == '.9e19'):
+#             record_dict['pars'][key] = np.nan
+#     for key in record_dict.keys():
+#         print(key)
+#         if key in ['air_ap','air_balloon',]:
+#             print('check')
+#             for datakey,datavalue in record_dict[key].items():
+#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
+# 
+#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
+# 
+#     c4gli = class4gl_input()
+#     c4gli.load_yaml_dict(record_dict)
+#     return c4gli
+
+
+
+
+
+
+        # self.frames['stats']['records_current_station_index'] = \
+        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+        #      == \
+        #      self.frames['stats']['current_station'].name)
+
+        # # create the value table of the records of the current station
+        # tab_suffixes = \
+        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        # for tab_suffix in tab_suffixes:
+        #     self.frames['stats']['records_current_station'+tab_suffix] = \
+        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+# class records_selection(object):
+#     def __init__
+
+# class records(object):
+#     def __init__(self,stations,path_obs,path_mod):
+#         self.stations = stations
+#         self.path_obs = path_obs
+#         self.path_mod = path_mod
+# 
+#         self.ini =       self.get_records(self.path_mod,'ini')
+#         self.mod =       self.get_records(self.path_mod,'mod')
+#         #self.morning =   self.get_records(self.path_obs,'morning')
+#         self.afternoon = self.get_records(self.path_obs,'afternoon')
+# 
+#         
+#         self.afternoon.index = self.afternoon.ldatetime.dt.date
+#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
+# 
+#         self.index = self.ini.index
+#         self.mod.index = self.index
+#         self.afternoon.index = self.index
+# 
+# 
+#         #self.records_iterator = records_current_station_mod.iterrows()
+
+
+
+def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
+
+    records = pd.DataFrame()
+    for STNID,station in stations.iterrows():
+        dictfnchunks = []
+        if getchunk is 'all':
+
+            # we try the old single-chunk filename format first (usually for
+            # original profile pairs)
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(fn):
+                chunk = 0
+                dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+            # otherwise, we use the new multi-chunk filename format
+            else:
+                chunk = 0
+                end_of_chunks = False
+                while not end_of_chunks:
+                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(fn):
+                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    else:
+                        end_of_chunks = True
+                    chunk += 1
+
+            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
+            # yamlfilenames = glob.glob(globyamlfilenames)
+            # yamlfilenames.sort()
+        else:
+            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
+            
+        if len(dictfnchunks) > 0:
+            for dictfnchunk in dictfnchunks:
+                yamlfilename = dictfnchunk['fn']
+                chunk = dictfnchunk['chunk']
+                print(chunk)
+
+                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                generate_pkl = False
+                if not os.path.isfile(pklfilename): 
+                    print('pkl file does not exist. I generate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                elif not (os.path.getmtime(yamlfilename) <  \
+                    os.path.getmtime(pklfilename)):
+                    print('pkl file older than yaml file, so I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+
+                if refetch_records:
+                    print('refetch_records flag is True. I regenerate "'+\
+                          pklfilename+'" from "'+yamlfilename+'"...')
+                    generate_pkl = True
+                if not generate_pkl:
+                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                   # irecord = 0
+                else:
+                    with open(yamlfilename) as yaml_file:
+
+                        dictout = {}
+
+                        next_record_found = False
+                        end_of_file = False
+                        while (not next_record_found) and (not end_of_file):
+                            linebuffer = yaml_file.readline()
+                            next_record_found = (linebuffer == '---\n')
+                            end_of_file = (linebuffer == '')
+                        next_tell = yaml_file.tell()
+                        
+                        while not end_of_file:
+
+                            print(' next record:',next_tell)
+                            current_tell = next_tell
+                            next_record_found = False
+                            yaml_file.seek(current_tell)
+                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            linebuffer = ''
+                            while ( (not next_record_found) and (not end_of_file)):
+                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                linebuffer = yaml_file.readline()
+                                next_record_found = (linebuffer == '---\n')
+                                end_of_file = (linebuffer == '')
+                            filebuffer.close()
+                            
+                            next_tell = yaml_file.tell()
+                            index_start = current_tell
+                            index_end = next_tell
+
+                            
+                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            print(command)
+                            
+                            os.system(command)
+                            #jsonoutput = subprocess.check_output(command,shell=True) 
+                            #print(jsonoutput)
+                            #jsonstream = io.StringIO(jsonoutput)
+                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            record = json.load(jsonstream)
+                            dictouttemp = {}
+                            for key,value in record['pars'].items():
+                                # we don't want the key with columns that have none values
+                                if value is not None: 
+                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                   if (type(value) in regular_numeric_types):
+                                        dictouttemp[key] = value
+                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                            recordindex = record['index']
+                            dictouttemp['chunk'] = chunk
+                            dictouttemp['index_start'] = index_start
+                            dictouttemp['index_end'] = index_end
+                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            for key,value in dictouttemp.items():
+                                if key not in dictout.keys():
+                                    dictout[key] = {}
+                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                            print(' obs record registered')
+                            jsonstream.close()
+                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                    records_station = pd.DataFrame.from_dict(dictout)
+                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
+                    print('writing table file ('+pklfilename+') for station '\
+                          +str(STNID))
+                    records_station.to_pickle(pklfilename)
+                    # else:
+                    #     os.system('rm '+pklfilename)
+                    records = pd.concat([records,records_station])
+    return records
+
+def stdrel(mod,obs,columns):
+    stdrel = pd.DataFrame(columns = columns)
+    for column in columns:
+        stdrel[column] = \
+                (mod.groupby('STNID')[column].transform('mean') -
+                 obs.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') + \
+                (mod[column] -
+                 mod.groupby('STNID')[column].transform('mean')) /\
+                obs.groupby('STNID')[column].transform('std') 
+    return stdrel
+
+def pct(obs,columns):
+    pct = pd.DataFrame(columns=columns)
+    for column in columns:
+        #print(column)
+        pct[column] = ""
+        pct[column] = obs[column].rank(pct=True)
+    return pct
+
+def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (obs_afternoon.ldatetime - \
+                               obs_morning.ldatetime).dt.seconds*3600.
+    return stats
+
diff --git a/lib/interface_multi.py b/lib/interface_multi.py
new file mode 100644
index 0000000..83148e5
--- /dev/null
+++ b/lib/interface_multi.py
@@ -0,0 +1,2061 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+import os
+import xarray as xr
+import sys
+from contextlib import suppress
+from time import sleep
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl,units
+from interface_functions import *
+# from data_soundings import wyoming
+import yaml
+import glob
+import pandas as pd
+import json
+import io
+import subprocess
+import pytz
+from scipy.stats import mstats
+
+from matplotlib.colors import LinearSegmentedColormap
+cdictpres = {'blue': (\
+                   (0.,    0.,  0.),
+                   (0.25,  0.25, 0.25),
+                   (0.5,  .70, 0.70),
+                   (0.75, 1.0, 1.0),
+                   (1,     1.,  1.),
+                   ),
+       'green': (\
+                   (0. ,   0., 0.0),
+                   (0.25,  0.50, 0.50),
+                   (0.5,  .70, 0.70),
+                   (0.75,  0.50, 0.50),
+                   (1  ,    0,  0.),
+                   ),
+       'red':  (\
+                  (0 ,  1.0, 1.0),
+                  (0.25 ,  1.0, 1.0),
+                   (0.5,  .70, 0.70),
+                  (0.75 , 0.25, 0.25),
+                  (1,    0., 0.),
+                  )}
+
+statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+os.system('module load Ruby')
+
+class c4gl_interface_soundings(object):
+    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+        """ creates an interactive interface for analysing class4gl experiments
+
+        INPUT:
+            path_exp : path of the experiment output
+            path_obs : path of the observations 
+            globaldata: global data that is being shown on the map
+            refetch_stations: do we need to build the list of the stations again?
+        OUTPUT:
+            the procedure returns an interface object with interactive plots
+
+        """
+        
+        # set the ground
+        self.globaldata = globaldata
+
+ 
+        self.path_exp = path_exp
+        self.path_obs = path_obs
+        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
+
+        # # get the list of stations
+        # stationsfile = self.path_exp+'/stations_list.csv'
+        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
+        #     stations = pd.read_csv(stationsfile)
+        # else:
+        #     stations = get_stations(self.path_exp)
+        #     stations.to_csv(stationsfile)
+
+        # stations = stations.set_index('STNID')
+
+        self.frames = {}
+
+        self.frames['stats'] = {}
+        self.frames['worldmap'] = {}
+                
+        self.frames['profiles'] = {}
+        self.frames['profiles'] = {}
+        self.frames['profiles']['DT'] = None
+        self.frames['profiles']['STNID'] = None
+
+        #self.frames['worldmap']['stationsfile'] = stationsfile
+        self.frames['worldmap']['stations'] = stations(self.path_exp, \
+                                                       suffix='ini',\
+                                                       refetch_stations=refetch_stations)
+
+        # Initially, the stats frame inherets the values/iterators of
+        # worldmap
+        for key in self.frames['worldmap'].keys():
+            self.frames['stats'][key] = self.frames['worldmap'][key]
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_ini'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='ini',\
+                                           refetch_records=refetch_records
+                                           )
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_mod'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_exp,\
+                                           subset='mod',\
+                                           refetch_records=refetch_records
+                                           )
+
+        # get its records and load it into the stats frame
+        self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                        get_records(self.frames['stats']['stations'].table,\
+                                           self.path_obs,\
+                                           subset='afternoon',\
+                                           refetch_records=refetch_records
+                                           )
+
+        self.frames['stats']['records_all_stations_mod'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['records_all_stations_ini']['dates'] = \
+            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+
+
+        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+
+        self.frames['stats']['records_all_stations_obs_afternoon'] = \
+            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+
+        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_ini'].index 
+
+        self.frames['stats']['viewkeys'] = ['h','theta','q']
+        print('Calculating table statistics')
+        self.frames['stats']['records_all_stations_mod_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_mod'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_obs_afternoon'],\
+                           self.frames['stats']['records_all_stations_ini'],\
+                           self.frames['stats']['viewkeys']\
+                          )
+
+        self.frames['stats']['inputkeys'] = inputkeys
+        
+        # self.frames['stats']['inputkeys'] = \
+        #     [ key for key in \
+        #       self.globaldata.datasets.keys() \
+        #       if key in \
+        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
+
+
+        # get units from the class4gl units database
+        self.units = dict(units)
+        # for those that don't have a definition yet, we just ask a question
+        # mark
+        for var in self.frames['stats']['inputkeys']:
+            self.units[var] = '?'
+
+        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
+        self.frames['stats']['records_all_stations_ini_pct'] = \
+                  pct(self.frames['stats']['records_all_stations_ini'], \
+                      columns = self.frames['stats']['inputkeys'])
+
+        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
+        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+        #     mod['
+
+        # 
+        # 
+        # \
+        #        self.frames['stats']['records_all_stations_mod'], \
+
+
+
+        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        #               columns = [ 'd'+key+'dt' for key in \
+        #                           self.frames['stats']['viewkeys']], \
+        #              )
+
+        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
+        #               obs = self.frames['stats']['records_all_stations_ini'], \
+        #               columns = self.frames['stats']['viewkeys'], \
+        #              )
+        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+        
+        print('filtering pathological data')
+        # some observational sounding still seem problematic, which needs to be
+        # investigated. In the meantime, we filter them
+        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+        # we filter ALL data frames!!!
+        for key in self.frames['stats'].keys():
+            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+               (self.frames['stats'][key].index.names == indextype):
+                self.frames['stats'][key] = self.frames['stats'][key][valid]
+        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+
+
+        print("filtering stations from interface that have no records")
+        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
+            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                    == STNID).sum() == 0):
+                print("dropping", STNID)
+                self.frames['worldmap']['stations'].table = \
+                        self.frames['worldmap']['stations'].table.drop(STNID)
+                    
+        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+        
+        # TO TEST: should be removed, since it's is also done just below
+        self.frames['stats']['stations_iterator'] = \
+            self.frames['worldmap']['stations_iterator'] 
+
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
+        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
+        self.next_station()
+
+        # self.goto_datetime_worldmap(
+        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+        #     'after')
+    def sel_station(self,STNID=None,rownumber=None):
+
+        if (STNID is not None) and (rownumber is not None):
+            raise ValueError('Please provide either STNID or rownumber, not both.')
+
+        if (STNID is None) and (rownumber is None):
+            raise ValueError('Please provide either STNID or rownumber.')
+            
+        if STNID is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
+            print(
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+            )
+            self.update_station()
+        elif rownumber is not None:
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
+            self.update_station()
+
+
+
+    def next_station(self,event=None,jump=1):
+        with suppress(StopIteration):
+            self.frames['worldmap']['STNID'],\
+            self.frames['worldmap']['current_station'] \
+                = self.frames['worldmap']['stations_iterator'].__next__(jump)
+            # self.frames['worldmap']['stations_iterator'].close()
+            # del(self.frames['worldmap']['stations_iterator'])
+            # self.frames['worldmap']['stations_iterator'] = \
+            #                 selfself.frames['worldmap']['stations'].iterrows()
+            # self.frames['worldmap']['STNID'],\
+            # self.frames['worldmap']['current_station'] \
+            #     = self.frames['worldmap']['stations_iterator'].__next__()
+
+        self.update_station()
+
+    def prev_station(self,event=None):
+        self.next_station(jump = -1,event=event)
+    def update_station(self):
+        for key in ['STNID','current_station','stations_iterator']: 
+            self.frames['stats'][key] = self.frames['worldmap'][key] 
+
+
+
+        # generate index of the current station
+        self.frames['stats']['records_current_station_index'] = \
+            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+             == \
+             self.frames['stats']['current_station'].name)
+
+        # create the value table of the records of the current station
+        tab_suffixes = \
+                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+        for tab_suffix in tab_suffixes:
+            self.frames['stats']['records_current_station'+tab_suffix] = \
+                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+        # go to first record of current station
+        self.frames['stats']['records_iterator'] = \
+                        records_iterator(self.frames['stats']['records_current_station_mod'])
+        (self.frames['stats']['STNID'] , \
+        self.frames['stats']['current_record_chunk'] , \
+        self.frames['stats']['current_record_index']) , \
+        self.frames['stats']['current_record_mod'] = \
+                        self.frames['stats']['records_iterator'].__next__()
+
+        for key in self.frames['stats'].keys():
+            self.frames['profiles'][key] = self.frames['stats'][key]
+
+        STNID = self.frames['profiles']['STNID']
+        chunk = self.frames['profiles']['current_record_chunk']
+        if 'current_station_file_ini' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_ini'].close()
+        self.frames['profiles']['current_station_file_ini'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+        if 'current_station_file_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_mod'].close()
+        self.frames['profiles']['current_station_file_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_afternoon'].close()
+        self.frames['profiles']['current_station_file_afternoon'] = \
+            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+        self.frames['profiles']['records_iterator'] = \
+                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+        (self.frames['profiles']['STNID'] , \
+        self.frames['profiles']['current_record_chunk'] , \
+        self.frames['profiles']['current_record_index']) , \
+        self.frames['profiles']['current_record_mod'] = \
+                        self.frames['profiles']['records_iterator'].__next__()
+
+
+        # for the profiles we make a distinct record iterator, so that the
+        # stats iterator can move independently
+
+        self.update_record()
+
+    def next_record(self,event=None,jump=1):
+        with suppress(StopIteration):
+            (self.frames['profiles']['STNID'] , \
+            self.frames['profiles']['current_record_chunk'] , \
+            self.frames['profiles']['current_record_index']) , \
+            self.frames['profiles']['current_record_mod'] = \
+                      self.frames['profiles']['records_iterator'].__next__(jump)
+        # except (StopIteration):
+        #     self.frames['profiles']['records_iterator'].close()
+        #     del( self.frames['profiles']['records_iterator'])
+        #     self.frames['profiles']['records_iterator'] = \
+        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #     (self.frames['profiles']['STNID'] , \
+        #     self.frames['profiles']['current_record_index']) , \
+        #     self.frames['profiles']['current_record_mod'] = \
+        #                     self.frames['profiles']['records_iterator'].__next__()
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        self.update_record()
+
+    def prev_record(self,event=None):
+        self.next_record(jump=-1,event=event)
+
+    def update_record(self):
+        self.frames['profiles']['current_record_ini'] =  \
+            self.frames['profiles']['records_current_station_ini'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'],\
+                  self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon'] =  \
+            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                  (self.frames['profiles']['STNID'] , \
+                  self.frames['profiles']['current_record_chunk'] , \
+                  self.frames['profiles']['current_record_index'])]
+
+        self.frames['profiles']['current_record_mod_stats'] = \
+                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                    self.frames['profiles']['STNID'], \
+                    self.frames['profiles']['current_record_chunk'], \
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+        self.frames['profiles']['current_record_ini_pct'] = \
+                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
+                    self.frames['profiles']['STNID'],\
+                    self.frames['profiles']['current_record_chunk'],\
+                    self.frames['profiles']['current_record_index'])]
+
+        for key in self.frames['profiles'].keys():
+            self.frames['stats'][key] = self.frames['profiles'][key]
+        # frame
+        # note that the current station, record is the same as the stats frame for initialization
+
+        # select first 
+        #self.frames['profiles']['current_record_index'], \
+        #self.frames['profiles']['record_yaml_mod'] = \
+        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
+        #                   self.frames['stats']['current_record_index'])
+        self.frames['profiles']['record_yaml_mod'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_mod'], \
+               self.frames['profiles']['current_record_mod'].index_start,
+               self.frames['profiles']['current_record_mod'].index_end,
+               mode='mod')
+                                
+        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_ini'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_ini'], \
+               record_ini.index_start,
+               record_ini.index_end,
+                mode='ini')
+
+        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                       (self.frames['stats']['STNID'] , \
+                        self.frames['stats']['current_record_chunk'] , \
+                        self.frames['stats']['current_record_index'])]
+
+        self.frames['profiles']['record_yaml_obs_afternoon'] = \
+           get_record_yaml(
+               self.frames['profiles']['current_station_file_afternoon'], \
+               record_afternoon.index_start,
+               record_afternoon.index_end,
+                mode='ini')
+
+
+        key = self.frames['worldmap']['inputkey']
+        # only redraw the map if the current world map has a time
+        # dimension
+        if 'time' in self.globaldata.datasets[key].page[key].dims:
+            self.goto_datetime_worldmap(
+                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                'after')
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap',
+                                                  'profiles'])
+        else:
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only=['stats_lightupdate',
+                                                  'worldmap_stations',
+                                                  'profiles'])
+
+    def abline(self,slope, intercept,axis):
+        """Plot a line from slope and intercept"""
+        #axis = plt.gca()
+        x_vals = np.array(axis.get_xlim())
+        y_vals = intercept + slope * x_vals
+        axis.plot(x_vals, y_vals, 'k--')
+
+    def plot(self):
+        import pylab as pl
+        from matplotlib.widgets import Button
+        import matplotlib.pyplot as plt
+        import matplotlib as mpl
+        '''
+        Definition of the axes for the sounding table stats
+        '''
+        
+        fig = pl.figure(figsize=(14,9))
+        axes = {} #axes
+        btns = {} #buttons
+
+        # frames, which sets attributes for a group of axes, buttens, 
+        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            label = 'stats_'+str(key)
+            axes[label] = fig.add_subplot(\
+                            len(self.frames['stats']['viewkeys']),\
+                            5,\
+                            5*ikey+1,label=label)
+            # Actually, the axes should be a part of the frame!
+            #self.frames['stats']['axes'] = axes[
+
+            # pointer to the axes' point data
+            axes[label].data = {}
+
+            # pointer to the axes' color fields
+            axes[label].fields = {}
+
+
+        fig.tight_layout()
+        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
+
+        label ='stats_colorbar'
+        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
+        axes[label].fields = {}
+
+        from matplotlib.colors import LinearSegmentedColormap
+        cdictpres = {'blue': (\
+                           (0.,    0.,  0.),
+                           (0.25,  0.25, 0.25),
+                           (0.5,  .70, 0.70),
+                           (0.75, 1.0, 1.0),
+                           (1,     1.,  1.),
+                           ),
+               'green': (\
+                           (0. ,   0., 0.0),
+                           (0.25,  0.50, 0.50),
+                           (0.5,  .70, 0.70),
+                           (0.75,  0.50, 0.50),
+                           (1  ,    0,  0.),
+                           ),
+               'red':  (\
+                          (0 ,  1.0, 1.0),
+                          (0.25 ,  1.0, 1.0),
+                           (0.5,  .70, 0.70),
+                          (0.75 , 0.25, 0.25),
+                          (1,    0., 0.),
+                          )}
+        
+        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
+
+
+        label = 'times'
+               
+        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+
+
+        label = 'worldmap'
+               
+        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
+        # add pointers to the data of the axes
+        axes[label].data = {}
+        # add pointers to color fields (for maps and colorbars) in the axes
+        axes[label].fields = {}
+        axes[label].lat = None
+        axes[label].lon = None
+
+        label = 'worldmap_colorbar'
+        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+        axes[label].fields = {}
+
+        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
+        label = 'worldmap_stations'
+        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label].data = {}
+
+        fig.canvas.mpl_connect('pick_event', self.on_pick)
+        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
+
+
+        """ buttons definitions """
+        
+        label = 'bprev_dataset'
+        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous dataset')
+        btns[label].on_clicked(self.prev_dataset)
+
+        label = 'bnext_dataset'
+        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next dataset')
+        btns[label].on_clicked(self.next_dataset)
+
+        label = 'bprev_datetime'
+        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous datetime')
+        btns[label].on_clicked(self.prev_datetime)
+
+        label = 'bnext_datetime'
+        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next datetime')
+        btns[label].on_clicked(self.next_datetime)
+
+
+        label = 'bprev_station'
+        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous station')
+        btns[label].on_clicked(self.prev_station)
+
+        label = 'bnext_station'
+        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next station')
+        btns[label].on_clicked(self.next_station)
+
+        label = 'bprev_record'
+        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Previous record')
+        btns[label].on_clicked(self.prev_record)
+
+        label = 'bnext_record'
+        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        btns[label] = Button(axes[label], 'Next record')
+        btns[label].on_clicked(self.next_record)
+
+
+        # self.nstatsview = nstatsview
+        # self.statsviewcmap = statsviewcmap
+        self.fig = fig
+        self.axes = axes
+        self.btns = btns
+        self.tbox = {}
+        # self.hover_active = False
+
+        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
+        #                                transform=plt.gcf().transFigure)
+
+        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
+                                          transform=plt.gcf().transFigure)
+
+        label = 'air_ap:theta'
+        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
+
+        label = 'air_ap:q'
+        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
+
+        label = 'out:h'
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+
+        label = 'out:theta'
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+
+        label = 'out:q'
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+
+        label = 'SEB'
+        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
+
+
+        self.hover_active = False
+        self.fig = fig
+        self.fig.show()
+        self.fig.canvas.draw()
+        self.refresh_plot_interface()
+
+
+    # def scan_stations(self):
+    #     blabla
+        
+
+
+    # def get_records(current_file):
+    #     records = pd.DataFrame()
+
+    #     # initial position
+    #     next_record_found = False
+    #     while(not next_record_found):
+    #         next_record_found = (current_file.readline() == '---\n')
+    #     next_tell = current_file.tell() 
+    #     end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #     while not end_of_file:
+    #         current_tell = next_tell
+    #         next_record_found = False
+    #         current_file.seek(current_tell)
+    #         while ( (not next_record_found) and (not end_of_file)):
+    #             current_line = current_file.readline()
+    #             next_record_found = (currentline == '---\n')
+    #             end_of_file = (currentline == '') # an empty line means we are at the end
+
+    #         # we store the position of the next record
+    #         next_tell = current_file.tell() 
+    #         
+    #         # we get the current record. Unfortunately we need to reset the
+    #         # yaml record generator first.
+    #         current_yamlgen.close()
+    #         current_yamlgen = yaml.load_all(current_file)
+    #         current_file.seek(current_tell)
+    #         current_record_mod = current_yamlgen.__next__()
+    #     current_yamlgen.close()
+
+    #     return records
+
+       #      next_record_found = False
+       #      while(not record):
+       #          next_record_found = (self.current_file.readline() == '---\n')
+       #      self.current_tell0 = self.current_file.tell() 
+
+       #  
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell0 = self.current_file.tell() 
+
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell1 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell0)
+       #  self.r0 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell1)
+       #  next_record_found = False
+       #  while ( (not next_record_found) and (not end_of_file):
+       #      current_line = self.current_file.readline()
+       #      next_record_found = (currentline == '---\n')
+       #      end_of_file = (currentline == '') # an empty line means we are at the end
+
+       #  self.current_tell2 = self.current_file.tell() 
+
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell1)
+       #  self.r1 = self.current_yamlgen.__next__()
+
+       #  self.current_file.seek(self.current_tell2)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell3 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell2)
+       #  self.r2 = self.current_yamlgen.__next__()
+
+       #  # go to position of next record in file
+       #  self.current_file.seek(self.current_tell3)
+       #  next_record_found = False
+       #  while(not next_record_found):
+       #      next_record_found = (self.current_file.readline() == '---\n')
+       #  self.current_tell4 = self.current_file.tell() 
+
+       #  self.current_yamlgen.close()
+       #  self.current_yamlgen = yaml.load_all(self.current_file)
+       #  self.current_file.seek(self.current_tell3)
+       #  self.r3 = self.current_yamlgen.__next__()
+ 
+       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
+
+    def goto_datetime_worldmap(self,DT,shift=None):
+        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                self.frames['worldmap']['iDT'] += 1
+            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                self.frames['worldmap']['iDT'] -= 1 
+            # for gleam, we take the values of the previous day
+            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                self.frames['worldmap']['iDT'] -= 2 
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+        #else:
+        #    self.frames['worldmap'].pop('DT')
+
+    def next_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def prev_datetime(self,event=None):
+        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+            # for now we don't go to different files, so we cannot go to
+            # another file 
+            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
+            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            if "fig" in self.__dict__.keys():
+                self.refresh_plot_interface(only='worldmap') 
+
+    def next_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset(self,event=None):
+        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
+        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
+        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+
+
+    def sel_dataset(self,inputkey):
+        self.frames['worldmap']['inputkey'] = inputkey
+        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
+        self.goto_datetime_worldmap(
+            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+            'after')# get nearest datetime of the current dataset to the profile
+        if "fig" in self.__dict__.keys():
+            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+       
+    # def prev_station(self,event=None):
+    #     self.istation = (self.istation - 1) % self.stations.shape[0]
+    #     self.update_station()
+
+
+
+
+    #def update_datetime(self):
+    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
+    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
+    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
+    #        print(self.worldmapfocus['DT'])
+    #        self.refresh_plot_interface(only='worldmap')
+
+    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
+
+        #print('r1')
+        for argkey in args.keys():
+            self.__dict__[arg] = args[argkey]
+
+        axes = self.axes
+        tbox = self.tbox
+        frames = self.frames
+        fig = self.fig
+ 
+        if (only is None) or ('worldmap' in only):
+            globaldata = self.globaldata
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+            else:
+                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+            keystotranspose = ['lat','lon']
+            for key in dict(datasetxr.dims).keys():
+                if key not in keystotranspose:
+                    keystotranspose.append(key)
+
+            datasetxr = datasetxr.transpose(*keystotranspose)
+            datasetxr = datasetxr.sortby('lat',ascending=False)
+
+            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+            lonleft = lonleft - 360.
+            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+            label = 'worldmap'
+            axes[label].clear()
+            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+
+        if (only is None) or ('worldmap' in only):
+            #if 'axmap' not in self.__dict__ :
+            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
+            #else:
+
+            #stations = self.stations
+
+
+            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
+            #     resolution = 'l', 
+            # area_thresh = 0.1,
+            #     llcrnrlon=-180., llcrnrlat=-90.0,
+            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
+            # 
+            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
+            # self.gmap.drawcountries(color='white',linewidth=0.3)
+            # #self.gmap.fillcontinents(color = 'gray')
+            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
+            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
+            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
+            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
+            # #self.ax5.shadedrelief()
+
+           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
+
+
+            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+
+            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
+            if 'lev' in field.dims:
+                field = field.isel(lev=-1)
+
+            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+            axes[label].axis('off')
+
+            from matplotlib import cm
+            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+            
+            
+            title=frames['worldmap']['inputkey']
+            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            axes[label].set_title(title)
+
+            label ='worldmap_colorbar'
+            axes[label].clear()
+            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+
+
+            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+            # x,y = self.gmap(lons,lats)
+            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+
+        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+
+            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            store_xlim = {}
+            store_ylim = {}
+            for ikey, key in enumerate(statskeys_out):
+                if (only is not None) and ('stats_lightupdate' in only):
+                    store_xlim[key] = axes['stats_'+key].get_xlim()
+                    store_ylim[key] = axes['stats_'+key].get_ylim()
+                self.axes['stats_'+key].clear()    
+
+            label = 'times'
+            self.axes[label].clear()
+
+            key = 'dthetadt'
+            x = self.frames['stats']['records_all_stations_ini']['datetime']
+            #print(x)
+            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            #print(y)
+            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            #print(z)
+
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            self.axes[label].data[label] = self.axes[label].scatter(x.values,
+                                                                    y.values,
+                                                                    c=z.values,
+                                                                    cmap=self.statsviewcmap,
+                                                                    s=2,
+                                                                    vmin=0.,
+                                                                    vmax=1.,
+                                                                    alpha=alpha_cloud_pixels)
+
+            
+            x = self.frames['stats']['records_current_station_ini']['datetime']
+            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+
+            x = self.frames['profiles']['records_current_station_ini']['datetime']
+            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
+            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
+
+            for ikey, key in enumerate(statskeys_out):
+
+                # show data of all stations
+                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                qvalmax = x.quantile(0.999)
+                qvalmin = x.quantile(0.001)
+                print('applying extra filter over extreme values for plotting stats')
+                selx = (x >= qvalmin) & (x < qvalmax)
+                sely = (x >= qvalmin) & (x < qvalmax)
+                x = x[selx & sely]
+                y = y[selx & sely]
+                z = z[selx & sely]
+                self.axes['stats_'+key].data['stats_'+key] = \
+                       self.axes['stats_'+key].scatter(x,y, c=z,\
+                                cmap=self.statsviewcmap,\
+                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
+
+                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
+
+                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
+                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
+                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
+                                cmap=self.statsviewcmap,\
+                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
+
+                if len(x) > 1:
+                    fit = np.polyfit(x, y, deg=1)
+                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
+                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
+
+                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
+                y = self.frames['stats']['current_record_mod_stats'][key]
+                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
+
+                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
+                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
+                    axes['stats_'+key].annotate(text, \
+                                               xy=(x,y),\
+                                               xytext=(0.05,0.05),\
+                                               textcoords='axes fraction',\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               color='white',\
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                # self.axes['stats_'+key].data[key+'_current_record'] = \
+                #        self.axes['stats_'+key].scatter(x,y, c=z,\
+                #                 cmap=self.statsviewcmap,\
+                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
+
+                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
+                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
+                # # highlight data for curent station
+                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+
+                if ikey == len(statskeys_out)-1:
+                    self.axes['stats_'+key].set_xlabel('external')
+                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
+                axes['stats_'+key].set_ylabel('model')
+
+
+                if (only is not None) and ('stats_lightupdate' in only):
+                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
+                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
+                else:
+                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
+                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
+                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
+                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
+                self.abline(1,0,axis=self.axes['stats_'+key])
+
+        if (only is None) or ('stats_colorbar' in only):
+            label ='stats_colorbar'
+            axes[label].clear()
+            import matplotlib as mpl
+            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
+            self.axes[label].fields[label] = \
+             mpl.colorbar.ColorbarBase(self.axes[label],\
+                        orientation='horizontal',\
+                        label="percentile of "+self.frames['worldmap']['inputkey'],
+                        alpha=1.,
+                                cmap=self.statsviewcmap,\
+                                       norm=norm
+                         )
+
+        #print('r1')
+        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
+            #print('r2')
+            label = 'worldmap_stations'
+            axes[label].clear()
+            
+            stations = self.frames['worldmap']['stations'].table
+            globaldata = self.globaldata
+            
+            key = label
+
+            #print('r3')
+            if (stations is not None):
+                xlist = []
+                ylist = []
+                #print('r4')
+                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
+            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    xlist.append(x)
+                    ylist.append(y)
+                #picker is needed to make it clickable (pick_event)
+                axes[label].data[label] = axes[label].scatter(xlist,ylist,
+                                                              c='r', s=15,
+                                                              picker = 15,
+                                                              label=key,
+                                                              edgecolor='k',
+                                                              linewidth=0.8)
+
+            # cb.set_label('Wilting point [kg kg-3]')
+                #print('r5')
+
+                
+            #     xseries = []
+            #     yseries = []
+            #     for iSTN,STN in stations.iterrows():
+            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
+            #         xseries.append(x)                    
+            #         yseries.append(y)
+            #         
+            #         
+            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
+                    
+                if ('current_station' in frames['worldmap']):
+                    #print('r5')
+                    STN = frames['stats']['current_station']
+                    STNID = frames['stats']['STNID']
+                    #print('r5')
+
+                    x,y = len(axes['worldmap'].lon)* \
+                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
+                          len(axes['worldmap'].lat)* \
+                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
+                    #print('r6')
+                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
+                    #                          == \
+                    #                          self.frames['worldmap']['STNID'])\
+                    #                         & \
+                    #                         (self.seltablestats['DT'] \
+                    #                          == self.axes['statsview0].focus['DT']) \
+                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
+                    #print('r7')
+                    text = 'STNID: '+ format(STNID,'10.0f') + \
+                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
+                            ', LON: '+format(STN['longitude'],'3.3f')+ \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+
+                            #+', VAL: '+format(VAL,'.3e')
+
+                    axes[label].scatter(x,y, c='r', s=30,\
+                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
+                    #print('r8')
+            
+                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
+                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
+                    #colorstation = max((min((1.,colorstation)),0.))
+                    colorstation =0.2
+                    from matplotlib import cm
+                    axes[label].annotate(text,
+                                         xy=(x,y),
+                                         xytext=(0.05,0.05),
+                                         textcoords='axes fraction', 
+                                         bbox=dict(boxstyle="round",
+                                         fc = cm.viridis(colorstation)),
+                                         arrowprops=dict(arrowstyle="->",
+                                                         linewidth=1.1),
+                                         color='white' if colorstation < 0.5 else 'black')
+                    #print('r9')
+
+                    # #pos = sc.get_offsets()[ind["ind"][0]]
+                    # 
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label].data[label+'statannotate'].set_text(text)
+                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
+                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
+            #print('r9')
+            axes[label].axis('off')
+            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
+            axes[label].set_ylim((len(axes['worldmap'].lat),0))
+            #print('r10')
+
+        if (only is None) or ('profiles' in only): 
+            #print('r11')
+
+            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
+            # # self.update_station(goto_first_sounding=False)
+            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
+            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
+            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
+            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
+
+            label = 'air_ap:theta'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # +\
+                # ' -> '+ \
+                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+            
+            
+            
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+            #print('r12')
+
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
+            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
+            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            #print('r13')
+            # 
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r14')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+
+            #print('r15')
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+                          
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r16')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r17')
+            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            print(hmax)
+            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            if valid_mod:
+
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="mod "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+
+            #print('r18')
+            axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('theta [K]')
+
+            label = 'air_ap:q'
+            axes[label].clear()
+
+            tbox['datetime'].set_text(\
+                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+            # 
+
+            #print('r19')
+            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
+            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # 
+            if valid_mod:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            else:
+                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
+                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+            # 
+            #print('r20')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+            #print('r21')
+
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            zco = range(zidxmax)
+
+
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                             label="obs "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+            zco = range(zidxmax)
+
+            #print('r23')
+            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                             label="fit "+\
+                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                             +'LT')
+
+            #print('r24')
+            if valid_mod:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zco = range(zidxmax)
+                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                                 label="fit ")#+\
+                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #+'LT')
+            #print('r25')
+            #axes[label].legend()
+
+            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
+            #axes[label].set_ylabel('height [m]')
+            axes[label].set_xlabel('q [kg/kg]')
+
+            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
+
+            # #pl.subplots_adjust(right=0.6)
+
+            # label = 'q_pro'
+            # axes[label].clear()
+
+            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
+            # 
+            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
+            # 
+            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
+
+            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
+            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
+            # 
+            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
+            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
+            # 
+            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
+            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
+            # #pl.subplots_adjust(right=0.6)
+            # axes[label].set_xlabel('specific humidity [kg/kg]')
+ 
+
+            #print('r26')
+            time = self.frames['profiles']['record_yaml_mod'].out.time
+            for ilabel,label in enumerate(['h','theta','q']):
+                axes["out:"+label].clear()
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].set_ylabel(label)
+                if ilabel == 2:
+                    axes["out:"+label].set_xlabel('local sun time [h]')
+                
+            #print('r27')
+            label = 'SEB'
+            axes[label].clear()
+            
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
+            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
+            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
+                
+            #print('r28')
+            
+            axes[label].legend()
+            
+            #         for ax in self.fig_timeseries_axes:
+#             ax.clear()
+#         
+#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
+#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
+#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
+#         #print(self.morning_sounding.c4gl.out.Swin)
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
+#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
+#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
+#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
+#         self.fig_timeseries_axes[3].legend()
+#         self.fig.canvas.draw()
+            
+
+
+
+
+
+
+        #self.ready()
+        #print('r29')
+        fig.canvas.draw()
+        #fig.show()
+
+        self.axes = axes
+        self.tbox = tbox
+        self.fig = fig
+
+    def on_pick(self,event):
+        #print("HELLO")
+        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
+        #self.axes['theta_pro'].clear()
+        #self.axes['q_pro'].clear()
+        
+
+        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
+        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
+        keys_to_axes = {}
+        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
+            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
+
+        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
+        keys_to_axes['worldmap'] = 'worldmap'
+        
+        axes = self.axes
+        #nstatsview = self.nstatsview
+        #statsviewcmap = self.statsviewcmap
+        stations = self.frames['worldmap']['stations'].table
+
+
+        #print("p1")
+        current = event
+        artist = event.artist
+        
+        selkey = artist.get_label()
+        
+        #print(keys_to_axes)
+        
+        label = keys_to_axes[selkey]
+        #print("HELLO",selkey,label)
+
+        # # Get to know in which axes we are
+        # label = None
+        # for axeskey in axes.keys():
+        #     if event.inaxes == axes[axeskey]:
+        #         label = axeskey
+        #         
+
+        # cont, pos = None, None
+        
+        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
+        ind = event.ind
+        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
+        d = axes[label].collections[0]
+        #d.set_offset_position('data')
+        xy = d.get_offsets()
+        x, y =  xy[:,0],xy[:,1]
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+
+        #print("p2")
+        if len(ind) > 0:
+            #print("p3")
+            pos = x[ind[0]], y[ind[0]]
+
+            #if label[:-1] == 'statsview':
+            #    #seltablestatsstdrel = self.seltablestatsstdrel
+            #    #seltablestatspct = self.seltablestatspct
+
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    
+            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
+            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+            #    
+            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
+            #el
+            if (label == 'worldmap') or (label == 'worldmap_stations'):
+                self.hover_active = False
+                if (self.frames['worldmap']['STNID'] !=
+                    self.frames['profiles']['STNID']):
+                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
+                # so we just need to perform update_station
+                    self.update_station()
+            elif (label[:5] == 'stats'):
+
+                self.hover_active = False
+                if (self.frames['stats']['STNID'] !=
+                self.frames['profiles']['STNID']) or \
+                   (self.frames['stats']['current_record_chunk'] != 
+                    self.frames['profiles']['current_record_chunk']) or \
+                   (self.frames['stats']['current_record_index'] != 
+                    self.frames['profiles']['current_record_index']):
+
+
+
+                    for key in ['STNID','current_station','stations_iterator']: 
+                        self.frames['worldmap'][key] = self.frames['stats'][key] 
+
+                    for key in self.frames['stats'].keys():
+                        self.frames['profiles'][key] = self.frames['stats'][key]
+
+                    STNID = self.frames['profiles']['STNID']
+                    chunk = self.frames['profiles']['current_record_chunk']
+                    if 'current_station_file_ini' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_ini'].close()
+                    self.frames['profiles']['current_station_file_ini'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+                    if 'current_station_file_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_mod'].close()
+                    self.frames['profiles']['current_station_file_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_afternoon'].close()
+                    self.frames['profiles']['current_station_file_afternoon'] = \
+                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+
+                    # go to hovered record of current station
+                    self.frames['profiles']['records_iterator'] = \
+                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # ... and go to the record of the profile window (last one that
+                    # was picked by the user)
+                    found = False
+                    EOF = False
+                    while (not found) and (not EOF):
+                        try:
+                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
+                            #print("hello*")
+                            #print(self.frames['profiles']['current_record_index'])
+                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
+                               (index == self.frames['profiles']['current_record_index']) and \
+                               (STNID == self.frames['profiles']['STNID']):
+                                #print('found!')
+                                found = True
+                        except StopIteration:
+                            EOF = True
+                    if found:
+                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_chunk'] = chunk
+                        self.frames['stats']['current_record_index'] = index
+                    # # for the profiles we make a distinct record iterator, so that the
+                    # # stats iterator can move independently
+                    # self.frames['profiles']['records_iterator'] = \
+                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    # (self.frames['profiles']['STNID'] , \
+                    # self.frames['profiles']['current_record_index']) , \
+                    # self.frames['profiles']['current_record_mod'] = \
+                    #                 self.frames['profiles']['records_iterator'].__next__()
+
+
+                    # for the profiles we make a distinct record iterator, so that the
+                    # stats iterator can move independently
+
+                    self.update_record()
+
+
+
+    def on_plot_hover(self,event):
+        axes = self.axes
+        #print('h1')
+
+        # Get to know in which axes we are
+        label = None
+        for axeskey in axes.keys():
+            if event.inaxes == axes[axeskey]:
+                label = axeskey
+                
+        #print('h2')
+
+        cont, pos = None, None
+        #print (label)
+        
+        if label is not None:
+            if  ('data' in axes[label].__dict__.keys()) and \
+                (label in axes[label].data.keys()) and \
+                (axes[label].data[label] is not None):
+                
+                #print('h3')
+                cont, ind =  axes[label].data[label].contains(event)
+                selkey = axes[label].data[label].get_label()
+                if len(ind["ind"]) > 0:
+                    #print('h4')
+                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
+                    #print('pos',pos,selkey)
+
+
+                    #if label[:-1] == 'statsview':
+                    #    seltablestatsstdrel = self.seltablestatsstdrel
+                    #    seltablestatspct = self.seltablestatspct
+
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
+                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
+                    #    self.hover_active = True
+                    #    
+                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
+                    #    
+                    #el
+                    #print(label[:5])
+                    if (label[:5] == 'stats') or (label == 'times'):
+                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        
+
+                        if label[:5] == 'stats':
+                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            (self.frames['stats']['STNID'] ,
+                             self.frames['stats']['current_record_chunk'], 
+                             self.frames['stats']['current_record_index']) = \
+                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        # elif label[:5] == 'stats':
+                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                        #     (self.frames['stats']['STNID'] ,
+                        #      self.frames['stats']['current_record_chunk'], 
+                        #      self.frames['stats']['current_record_index']) = \
+                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+
+
+                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+                        
+                        # # TO TEST: should be removed, since it's is also done just below
+                        # self.frames['stats']['stations_iterator'] = \
+                        #     self.frames['worldmap']['stations_iterator'] 
+                
+                
+                        # self.goto_datetime_worldmap(
+                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
+                        #     'after')
+
+
+                        # scrolling to the right station
+                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                        EOF = False
+                        found = False
+                        while (not found and not EOF):
+                            if (STNID == self.frames['stats']['STNID']):
+                                   found = True 
+                            if not found:
+                                try:
+                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
+                                except (StopIteration):
+                                    EOF = True
+                        if found:
+                        #    self.frames['stats']['STNID'] = STNID
+                            self.frames['stats']['current_station'] =  station
+
+                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
+                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
+
+
+                        # generate index of the current station
+                        self.frames['stats']['records_current_station_index'] = \
+                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                             == self.frames['stats']['STNID'])
+
+
+                        tab_suffixes = \
+                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            self.frames['stats']['records_current_station'+tab_suffix] = \
+                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+
+                        # go to hovered record of current station
+                        self.frames['stats']['records_iterator'] = \
+                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+
+
+                        # ... and go to the record of the profile window (last one that
+                        # was picked by the user)
+                        found = False
+                        EOF = False
+                        while (not found) and (not EOF):
+                            try:
+                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                                #print("hello*")
+                                #print(self.frames['profiles']['current_record_index'])
+                                if (index == self.frames['stats']['current_record_index']) and \
+                                   (chunk == self.frames['stats']['current_record_chunk']) and \
+                                   (STNID == self.frames['stats']['STNID']):
+                                    #print('found!')
+                                    found = True
+                            except StopIteration:
+                                EOF = True
+                        if found:
+                            #print('h5')
+                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_chunk'] = chunk
+                            self.frames['stats']['current_record_index'] = index
+
+                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
+                        tab_suffixes = \
+                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        for tab_suffix in tab_suffixes:
+                            #print(tab_suffix)
+                            #print(self.frames['stats']['records_current_station'+tab_suffix])
+                            self.frames['stats']['current_record'+tab_suffix] =  \
+                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                      (self.frames['stats']['STNID'] , \
+                                       self.frames['stats']['current_record_chunk'] , \
+                                       self.frames['stats']['current_record_index'])]
+
+
+                        self.hover_active = True
+                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                        # print('h13')
+                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
+                        #     self.goto_datetime_worldmap(
+                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
+                        #         'after')
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap',
+                        #                                           'profiles'])
+                        # else:
+                        #     if "fig" in self.__dict__.keys():
+                        #         self.refresh_plot_interface(only=['stats_lightupdate',
+                        #                                           'worldmap_stations',
+                        #                                           'profiles'])
+
+
+
+                    elif label in ['worldmap_stations','worldmap']:
+                        #print('h5')
+
+                        if (self.axes['worldmap'].lat is not None) and \
+                           (self.axes['worldmap'].lon is not None):
+
+
+                            #self.loading()
+                            self.fig.canvas.draw()
+                            self.fig.show()
+
+
+                            # get position of 
+                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
+                                                                 self.axes['worldmap'].lat[0]) + \
+                                           self.axes['worldmap'].lat[0],4)
+                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
+                                                                 self.axes['worldmap'].lon[0]) + \
+                                           self.axes['worldmap'].lon[0],4)
+                        
+                            stations = self.frames['worldmap']['stations'].table
+                            #print('h7')
+                        
+                            #reset stations iterator:
+                            # if 'stations_iterator' in self.frames['worldmap'].keys():
+                            #     self.frames['worldmap']['stations_iterator'].close()
+                            #     del(self.frames['worldmap']['stations_iterator'])
+                            # if 'stations_iterator' in self.frames['stats'].keys():
+                            #     self.frames['stats']['stations_iterator'].close()
+                            #     del(self.frames['stats']['stations_iterator'])
+                            self.frames['worldmap']['stations_iterator'] =\
+                               stations_iterator(self.frames['worldmap']['stations'])
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                            EOF = False
+                            found = False
+                            while (not found and not EOF):
+                                #print('h8',station.latitude,latmap)
+                                #print('h8',station.longitude,lonmap)
+                                if (round(station.latitude,3) == round(latmap,3)) and \
+                                    (round(station.longitude,3) == round(lonmap,3)):
+                                       found = True 
+                                if not found:
+                                    try:
+                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                                    except (StopIteration):
+                                        EOF = True
+                            if found:
+                                self.frames['worldmap']['STNID'] = STNID
+                                self.frames['worldmap']['current_station'] = \
+                                        station
+                        
+                            self.frames['stats']['stations_iterator'] = \
+                                self.frames['worldmap']['stations_iterator'] 
+                            #print('h8')
+                            # inherit station position for the stats frame...
+                            for key in self.frames['worldmap'].keys():
+                                self.frames['stats'][key] = self.frames['worldmap'][key]
+                                
+                            ## fetch records of current station...
+                            #self.frames['stats']['records_current_station_mod'] =\
+                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                            # ... and their indices
+                            self.frames['stats']['records_current_station_index'] = \
+                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                                     == \
+                                     self.frames['stats']['current_station'].name)
+
+
+                            tab_suffixes = \
+                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['records_current_station'+tab_suffix] = \
+                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+
+                            # ... create a record iterator ...
+                            #self.frames['stats']['records_iterator'].close()
+                            del(self.frames['stats']['records_iterator'])
+                            self.frames['stats']['records_iterator'] = \
+                                self.frames['stats']['records_current_station_mod'].iterrows()
+
+
+
+                        
+                            #print('h9')
+                            # ... and go to to the first record of the current station
+                            (self.frames['stats']['STNID'] , \
+                             self.frames['stats']['current_record_chunk'] , \
+                             self.frames['stats']['current_record_index']) , \
+                            self.frames['stats']['current_record_mod'] = \
+                                self.frames['stats']['records_iterator'].__next__()
+                        
+
+
+
+                            #print('h10')
+                            # cash the current record
+                            tab_suffixes = \
+                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                            for tab_suffix in tab_suffixes:
+                                self.frames['stats']['current_record'+tab_suffix] =  \
+                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                                          (self.frames['stats']['STNID'] , \
+                                           self.frames['stats']['current_record_chunk'] , \
+                                           self.frames['stats']['current_record_index'])]
+
+                            #print('h11')
+                            
+                            self.hover_active = True
+                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
+                            #print('h13')
+
+                        
+
+            #if (stations is not None):
+            #    for iSTN,STN in stations.iterrows():
+            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
+            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
+            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
+            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
+
+        # self.fig.show()
+ 
+        # we are hovering on nothing, so we are going back to the position of
+        # the profile sounding
+        if pos is None:
+            if self.hover_active == True:
+                #print('h1*')
+                
+                #self.loading()
+                # to do: reset stations iterators
+
+                # get station and record index from the current profile
+                for key in ['STNID', 'current_station']:
+                    self.frames['stats'][key] = self.frames['profiles'][key]
+
+                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
+                self.frames['stats']['current_station'] = \
+                        self.frames['profiles']['current_station']
+                #print('h3a*')
+                self.frames['stats']['records_current_station_mod'] = \
+                        self.frames['profiles']['records_current_station_mod']
+                #print('h3b*')
+
+                # the next lines recreate the records iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+
+                # reset stations iterator...
+                #self.frames['stats']['records_iterator'].close()
+                del(self.frames['stats']['records_iterator'])
+                self.frames['stats']['records_iterator'] = \
+                    self.frames['stats']['records_current_station_mod'].iterrows()
+                #print('h4*')
+
+                # ... and go to the record of the profile window (last one that
+                # was picked by the user)
+                found = False
+                EOF = False
+                while (not found) and (not EOF):
+                    try:
+                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
+                        #print("hello*")
+                        #print(self.frames['profiles']['current_record_index'])
+                        #print(self.frames['profiles']['STNID'])
+                        #print(STNID,index)
+                        if (index == self.frames['profiles']['current_record_index']) and \
+                            (chunk == self.frames['profiles']['current_record_chunk']) and \
+                            (STNID == self.frames['profiles']['STNID']):
+                            #print('found!')
+                            found = True
+                    except StopIteration:
+                        EOF = True
+                if found:
+                    #print('h5*')
+                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_chunk'] = chunk
+                    self.frames['stats']['current_record_index'] = index
+
+                #print('h6*')
+
+
+
+                # # fetch records of current station...
+                # self.frames['stats']['records_current_station_mod'] =\
+                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+
+                # ... and their indices
+                self.frames['stats']['records_current_station_index'] = \
+                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
+                         == \
+                         self.frames['stats']['current_station'].name)
+
+
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['records_current_station'+tab_suffix] = \
+                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
+
+                
+
+                # cash the records of the current stations
+                tab_suffixes = \
+                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                for tab_suffix in tab_suffixes:
+                    self.frames['stats']['current_record'+tab_suffix] =  \
+                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
+                              (self.frames['stats']['STNID'] , \
+                               self.frames['stats']['current_record_chunk'] , \
+                               self.frames['stats']['current_record_index'])]
+
+
+                # the next lines recreate the stations iterator. Probably it's
+                # better to just copy the profile iterator and its position to
+                # the worldmap/stats 
+                #print('h7*')
+
+                # reset the stations iterators
+                for framekey in ['stats','worldmap']:
+                    ##print(framekey)
+                    if 'stations_iterator' in self.frames[framekey]:
+                        #self.frames[framekey]['stations_iterator'].close()
+                        del(self.frames[framekey]['stations_iterator'])
+
+                self.frames['worldmap']['current_station'] = \
+                        self.frames['profiles']['current_station']
+
+                #recreate the stations iterator for the worldmap...
+                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
+
+                # ... and go the position of the profile
+                #print('h8*')
+                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                EOF = False
+                found = False
+                while (not found and not EOF):
+                    if STNID == self.frames['profiles']['STNID'] :
+                        found = True 
+                    if not found:
+                        try:
+                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
+                        except (StopIteration):
+                            EOF = True
+                if found:
+                    self.frames['worldmap']['current_station'] = station
+                    self.frames['worldmap']['STNID'] = STNID
+                #print('h9*')
+                self.frames['stats']['stations_iterator'] = \
+                    self.frames['worldmap']['stations_iterator'] 
+
+                # the stats window now inherits the current station from the
+                # worldmap
+                for key in ['STNID','current_station','stations_iterator']: 
+                    self.frames['stats'][key] = self.frames['worldmap'][key] 
+                #print('h10*')
+
+                # # we now only need inherit station position and go to first record
+                # for key in self.frames['worldmap'].keys():
+                #     self.frames['stats'][key] = self.frames['worldmap'][key]
+
+                # self.frames['stats']['records_current_station'] =\
+                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
+
+                # #print(self.frames['stats']['records_current_station'])
+                # self.frames['stats']['records_iterator'] = \
+                #                 self.frames['stats']['records_current_station'].iterrows()
+                # (self.frames['stats']['STNID'] , \
+                # self.frames['stats']['current_record_index']) , \
+                # self.frames['stats']['current_record_mod'] = \
+                #                 self.frames['stats']['records_iterator'].__next__()
+                
+
+
+
+
+
+
+                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
+                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
+                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
+                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
+                self.hover_active = False
+                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
+    # def loading(self):
+    #     self.tbox['loading'].set_text('Loading...')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+    #     sleep(0.1)
+    # def ready(self):
+    #     self.tbox['loading'].set_text('Ready')
+    #     self.fig.canvas.draw()
+    #     self.fig.show()
+
+
+
diff --git a/lib/model.py b/lib/model.py
new file mode 100644
index 0000000..8760411
--- /dev/null
+++ b/lib/model.py
@@ -0,0 +1,2214 @@
+# 
+# CLASS
+# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
+# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
+# Copyright (c) 2011-2015 Chiel van Heerwaarden
+# Copyright (c) 2011-2015 Bart van Stratum
+# Copyright (c) 2011-2015 Kees van den Dries
+# 
+# This file is part of CLASS
+# 
+# CLASS is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published bygamma
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+# 
+# CLASS is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+# 
+# You should have received a copy of the GNU General Public License
+# along with CLASS.  If not, see .
+#
+
+import copy as cp
+import numpy as np
+import sys
+import warnings
+import pandas as pd
+from ribtol_hw import zeta_hs2 , funcsche
+import logging
+#from SkewT.thermodynamics import Density
+#import ribtol
+
+grav = 9.81
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+
+def qsat(T,p):
+    return 0.622 * esat(T) / p
+
+
+def ribtol(Rib, zsl, z0m, z0h): 
+    Rib = np.float64(Rib)
+    zsl = np.float64(zsl)
+    z0m = np.float64(z0m)
+    z0h = np.float64(z0h)
+    #print(Rib,zsl,z0m,z0h)
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    #print(Rib,zsl,z0m,z0h)
+    while (abs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+        #print(L,fx/fxdif)
+        if(abs(L) > 1e12):
+            break
+
+    return L
+  
+def psim(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psim = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+  
+def psih(zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * np.log( (1. + x*x) / 2.)
+        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+ 
+class model:
+    def __init__(self, model_input = None,debug_level=None):
+
+        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
+        """
+
+        self.logger = logging.getLogger('model')
+        if debug_level is not None:
+            self.logger.setLevel(debug_level)
+
+        """ initialize the different components of the model """ 
+
+        if model_input is not None:
+            # class4gl style input
+            if 'pars' in model_input.__dict__.keys():
+
+                # we make a reference to the full input first, so we can dump it
+                # afterwards
+                self.input_c4gl = model_input
+
+                # we copy the regular parameters first. We keep the classical input
+                # format as self.input so that we don't have to change the entire
+                # model code.
+                self.input = cp.deepcopy(model_input.pars)
+
+                # we copy other sections we are interested in, such as profile
+                # data, and store it also under input
+
+                # I know we mess up a bit the structure of the class4gl_input, but
+                # we will make it clean again at the time of dumping data
+
+                # So here, we copy the profile data into self.input
+                # 1. Air circulation data 
+                if 'sw_ac' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ac']:
+                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
+                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
+
+                    # correct pressure of levels according to surface pressure
+                    # error (so that interpolation is done in a consistent way)
+
+                    p_e = self.input.Ps - self.input.sp
+                    for irow in self.input.air_ac.index[::-1]:
+                       self.input.air_ac.p.iloc[irow] =\
+                        self.input.air_ac.p.iloc[irow] + p_e
+                       p_e = p_e -\
+                       (self.input.air_ac.p.iloc[irow]+p_e)/\
+                        self.input.air_ac.p.iloc[irow] *\
+                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+
+
+
+                # 2. Air circulation data 
+                if 'sw_ap' in self.input.__dict__.keys() \
+                   and self.input.__dict__['sw_ap']:
+                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
+
+            # standard class input
+            else:
+                self.input = cp.deepcopy(model_input)
+
+    def load_yaml_dict(self,yaml_dict):
+        for key,data in yaml_dict.items():
+            if key == 'pars':
+                for keydata,value in data.items():
+                    self.__dict__[keydata] = value
+            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
+                self.__dict__[key] = pd.DataFrame(data)
+            #elif key == 'sources':
+            #    self.__dict__[key] = data
+            elif key == 'out':
+                # lets convert it to a list of dictionaries
+                dictouttemp = pd.DataFrame(data).to_dict('list')
+            else: 
+                 warnings.warn("Key '"+key+"' is be implemented.")
+            #     self.__dict__[key] = data
+
+
+        self.tsteps = len(dictouttemp['h'])
+        self.out = model_output(self.tsteps)
+        for keydictouttemp in dictouttemp.keys():
+            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+
+
+  
+    def run(self):
+        # initialize model variables
+        self.init()
+  
+        # time integrate model 
+        #for self.t in range(self.tsteps):
+        while self.t < self.tsteps:
+          
+            # time integrate components
+            self.timestep()
+  
+        # delete unnecessary variables from memory
+        self.exitmodel()
+    
+    def init(self):
+        # assign variables from input data
+        # initialize constants
+        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
+        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+        self.rho        = 1.2                   # density of air [kg m-3]
+        self.k          = 0.4                   # Von Karman constant [-]
+        self.g          = 9.81                  # gravity acceleration [m s-2]
+        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+        self.bolz       = 5.67e-8               # Bolzman constant [-]
+        self.rhow       = 1000.                 # density of water [kg m-3]
+        self.S0         = 1368.                 # solar constant [W m-2]
+
+        # A-Gs constants and settings
+        # Plant type:       -C3-     -C4-
+        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
+        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
+        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
+        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
+        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
+        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
+        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
+        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
+        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
+        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
+        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
+        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
+        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
+
+        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
+        self.mair       =  28.9;                # molecular weight air [g mol -1]
+        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
+
+        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
+        self.wmax       =  0.55;                # upper reference value soil water [-]
+        self.wmin       =  0.005;               # lower reference value soil water [-]
+        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
+        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
+
+        # Read switches
+        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
+        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
+        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
+        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
+        self.sw_sl      = self.input.sw_sl      # surface layer switch
+        self.sw_rad     = self.input.sw_rad     # radiation switch
+        self.sw_ls      = self.input.sw_ls      # land surface switch
+        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
+        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
+
+        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
+        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
+        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
+  
+        # initialize mixed-layer
+        self.h          = self.input.h          # initial ABL height [m]
+        self.Ps         = self.input.Ps         # surface pressure [Pa]
+        self.sp         = self.input.sp         # This is also surface pressure
+                                                #but derived from the global data [Pa]
+        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
+        self.ws         = None                  # large-scale vertical velocity [m s-1]
+        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
+        self.we         = -1.                   # entrainment velocity [m s-1]
+       
+         # Temperature 
+        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
+        
+        
+        self.substep    = False
+        self.substeps   = 0
+
+
+
+        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
+        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
+        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
+        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
+        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
+ 
+        self.wstar      = 0.                    # convective velocity scale [m s-1]
+ 
+        # 2m diagnostic variables 
+        self.T2m        = None                  # 2m temperature [K]
+        self.q2m        = None                  # 2m specific humidity [kg kg-1]
+        self.e2m        = None                  # 2m vapor pressure [Pa]
+        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
+        self.u2m        = None                  # 2m u-wind [m s-1]
+        self.v2m        = None                  # 2m v-wind [m s-1]
+ 
+        # Surface variables 
+        self.thetasurf  = self.input.theta      # surface potential temperature [K]
+        self.thetavsurf = None                  # surface virtual potential temperature [K]
+        self.qsurf      = None                  # surface specific humidity [g kg-1]
+
+        # Mixed-layer top variables
+        self.P_h        = None                  # Mixed-layer top pressure [pa]
+        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
+        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
+        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
+        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
+        self.dz_h       = None                  # Transition layer thickness [-]
+        self.lcl        = None                  # Lifting condensation level [m]
+
+        # Virtual temperatures and fluxes
+        self.thetav     = None                  # initial mixed-layer potential temperature [K]
+        self.dthetav    = None                  # initial virtual temperature jump at h [K]
+        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
+        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
+       
+        
+        
+        
+        
+        
+        # Moisture 
+        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
+
+        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
+        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
+        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
+  
+        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
+        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
+        self.e          = None                  # mixed-layer vapor pressure [Pa]
+        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
+        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
+      
+        
+        
+        # CO2
+        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
+        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
+        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
+        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
+        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
+        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
+        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
+        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
+        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
+        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
+       
+        # Wind 
+        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
+        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
+        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = self.input.advu       # advection of u-wind [m s-2]
+        
+        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
+        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = self.input.advv       # advection of v-wind [m s-2]
+         
+  # BEGIN -- HW 20170606
+        # z-coordinate for vertical profiles of stratification above the mixed-layer height
+
+        if self.sw_ac:
+        # this is the data frame with the grided profile on the L60 grid
+        # (subsidence, and advection) 
+            self.air_ac      = self.input.air_ac  # full level air circulation
+                                                  # forcing
+            # self.air_ach     = self.input.air_ach # half level air circulation
+            #                                       # forcing
+            
+
+        if self.sw_ap:
+        # this is the data frame with the fitted profile (including HAGL,
+        # THTA,WSPD, SNDU,WNDV PRES ...)
+            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
+
+            # just for legacy reasons...
+            if 'z' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
+            if 'p' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
+
+            indexh = np.where(self.air_ap.z.values == self.h)
+            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
+                raise ValueError("Error input profile consistency: mixed- \
+                                 layer height needs to be equal to the second \
+                                 and third \
+                                 level of the vertical profile input!")
+            # initialize q from its profile when available
+            p_old = self.Ps
+            p_new = self.air_ap.p[indexh[0][0]]
+            
+            if ((p_old is not None) & (p_old != p_new)):
+                print("Warning: Ps input was provided ("+str(p_old)+\
+                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+                    +str(p_new)+"Pa).")
+                                    
+            self.Ps = p_new
+            # these variables/namings are more convenient to work with in the code
+            # we will update the original variables afterwards
+            #self.air_ap['q'] = self.air_ap.QABS/1000.
+
+            self.air_ap = \
+                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
+            # we require the temperature fields, since we need to consider
+            # advection
+            # if self.sw_ac:
+            #     #self.air_ap['theta'] = self.air_ap['t'] *
+
+            #     # we consider self.sp in case of air-circulation input (for
+            #     # consistence)
+            #     self.air_ap['t'] = \
+            #                 self.air_ap.theta *  \
+            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
+            # else:
+            # we consider self.Ps in case of balloon input only 
+            self.air_ap = self.air_ap.assign(t = lambda x: \
+                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
+
+            #self.air_ap['theta'] = self.air_ap.THTA
+            if 'u' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
+            if 'v' not in list(self.air_ap.columns):
+                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
+
+            for var in ['theta','q','u','v']:
+
+                
+                if self.air_ap[var][1] != self.air_ap[var][0]:
+                    raise ValueError("Error input profile consistency: two \
+                                     lowest profile levels for "+var+" should \
+                                     be equal.")
+                
+                # initialize the value from its profile when available
+                value_old = self.__dict__[var]
+                value_new = self.air_ap[var][indexh[0][0]]
+                
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided \
+                                     ("+str(value_old)+ "kg kg-1), \
+                                     but it is now overwritten by the first \
+                                     level (index 0) of air_ap]var\ which is \
+                                     different (" +str(value_new)+"K).")
+                                        
+                self.__dict__[var] = value_new
+
+                # make a profile of the stratification 
+                # please note that the stratification between z_pro[i] and
+                # z_pro[i+1] is given by air_ap.GTHT[i]
+
+                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
+                # np.gradient(self.z_pro)
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
+
+
+                self.__dict__['gamma'+var] = \
+                    self.air_ap['gamma'+var][np.where(self.h >= \
+                                                     self.air_ap.z)[0][-1]]
+
+
+
+        # the variable p_pro is just for diagnosis of lifted index
+            
+            
+
+            # input Ph is wrong, so we correct it according to hydrostatic equation
+            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+
+            #if self.sw_ac:
+                # note that we use sp as surface pressure, which is determined
+                # from era-interim instead of the observations. This is to
+                # avoid possible failure of the interpolation routine
+                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
+                #                          + \
+                #                          list(self.air_ap.p[3:]))
+
+            # else:
+                # in the other case, it is updated at the time of calculting
+                # the statistics 
+
+# END -- HW 20170606      
+        #print(self.air_ap)
+
+        if self.sw_ac and not self.sw_ap:
+            raise ValueError("air circulation switch only possible when air \
+                             profiles are given")
+        
+        if self.sw_ac:
+
+            # # # we comment this out, because subsidence is calculated
+            # according to advection
+            # #interpolate subsidence towards the air_ap height coordinate
+            # self.air_ap['w'] = np.interp(self.air_ap.p,\
+            #                               self.air_ac.p,\
+            #                               self.air_ac.w) 
+            # #subsidence at the mixed-layer top
+            # self.w = self.air_ap.w[1]
+        
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+                # in case we didn't find any points, we just take the lowest one.
+                # actually, this can happen if ERA-INTERIM pressure levels are
+                # inconsistent with 
+                if in_ml.sum() == 0:
+                    warnings.warn(" no circulation points in the mixed layer \
+                                  found. We just take the bottom one.")
+                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+
+                for var in ['t','q','u','v']:
+    
+                   # calculation of the advection variables for the mixed layer
+                   # we weight by the hydrostatic thickness of each layer and
+                   # divide by the total thickness
+                   self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+
+                   # calculation of the advection variables for the profile above
+                   # (lowest 3 values are not used by class)
+                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
+                   self.air_ap['adv'+var] = \
+                           np.interp(self.air_ap.p,\
+                                     self.air_ac.p,\
+                                     self.air_ac['adv'+var+'_x']) \
+                           + \
+                           np.interp(self.air_ap.p, \
+                                       self.air_ac.p, \
+                                       self.air_ac['adv'+var+'_y'])
+
+                # as an approximation, we consider that advection of theta in the
+                # mixed layer is equal to advection of t. This is a sufficient
+                # approximation since theta and t are very similar at the surface
+                # pressure.
+                self.__dict__['advtheta'] = self.__dict__['advt']
+
+
+            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
+            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
+            # # # CALCULATING THE ADVECTION PROFILES
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # self.wrho = np.interp(self.P_h,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) 
+            # self.ws   = self.air_ap.w.iloc[1]
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
+                self.air_ap = self.air_ap.assign(R = 0.)
+                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                     self.Rv*self.air_ap.q)
+                self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+                
+                self.air_ap = self.air_ap.assign(w = 0.)
+                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+                #print('hello w ini')
+
+                # Note: in case of sw_ac is False, we update it from prescribed
+                # divergence
+                self.ws   = self.air_ap.w[1]
+
+                # self.ws   = self.wrho/self.rho
+                # self.ws   = self.wrho/(self.P_h/ \
+                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
+                #                         self.theta) # this should be T!!!
+
+                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+                #                         + \
+                #                         self.air_ac['divU_y'][in_ml])* \
+                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                #             self.air_ac['delpdgrav'][in_ml].sum() \
+        
+
+        # Tendencies 
+        self.htend      = None                  # tendency of CBL [m s-1]
+        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
+        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
+        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
+        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
+        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
+        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
+        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
+        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
+        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
+        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
+        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
+  
+        # initialize surface layer
+        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
+        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
+        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
+        self.z0m        = self.input.z0m        # roughness length for momentum [m]
+        self.z0h        = self.input.z0h        # roughness length for scalars [m]
+        self.Cm         = 1e12                  # drag coefficient for momentum [-]
+        self.Cs         = 1e12                  # drag coefficient for scalars [-]
+        self.L          = None                  # Obukhov length [m]
+        self.Rib        = None                  # bulk Richardson number [-]
+        self.ra         = None                  # aerodynamic resistance [s m-1]
+  
+        # initialize radiation
+        self.lat        = self.input.lat        # latitude [deg]
+        #self.fc         = self.input.fc         # coriolis parameter [s-1]
+        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
+        self.lon        = self.input.lon        # longitude [deg]
+        self.doy        = self.input.doy        # day of the year [-]
+        self.tstart     = self.input.tstart     # time of the day [-]
+        self.cc         = self.input.cc         # cloud cover fraction [-]
+        self.Swin       = None                  # incoming short wave radiation [W m-2]
+        self.Swout      = None                  # outgoing short wave radiation [W m-2]
+        self.Lwin       = None                  # incoming long wave radiation [W m-2]
+        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
+        self.Q          = self.input.Q          # net radiation [W m-2]
+        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
+  
+        # initialize land surface
+        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
+        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
+        self.T2         = self.input.T2         # temperature deeper soil layer [K]
+                           
+        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
+        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
+        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
+        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
+                           
+        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
+        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
+        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
+                           
+        self.C1sat      = self.input.C1sat      
+        self.C2ref      = self.input.C2ref      
+
+        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
+        
+        self.LAI        = self.input.LAI        # leaf area index [-]
+        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
+        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = self.input.alpha      # surface albedo [-]
+  
+        self.rs         = 1.e6                  # resistance transpiration [s m-1]
+        self.rssoil     = 1.e6                  # resistance soil [s m-1]
+                           
+        self.Ts         = self.input.Ts         # surface temperature [K]
+                           
+        self.cveg       = self.input.cveg       # vegetation fraction [-]
+        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
+        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
+        self.cliq       = None                  # wet fraction [-]
+                          
+        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
+  
+        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
+        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
+        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
+  
+        self.H          = None                  # sensible heat flux [W m-2]
+        self.LE         = None                  # evapotranspiration [W m-2]
+        self.LEliq      = None                  # open water evaporation [W m-2]
+        self.LEveg      = None                  # transpiration [W m-2]
+        self.LEsoil     = None                  # soil evaporation [W m-2]
+        self.LEpot      = None                  # potential evaporation [W m-2]
+        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
+        self.G          = None                  # ground heat flux [W m-2]
+
+        # initialize A-Gs surface scheme
+        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
+
+        # initialize cumulus parameterization
+        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
+        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
+        self.ac         = 0.                    # Cloud core fraction [-]
+        self.M          = 0.                    # Cloud core mass flux [m s-1] 
+        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
+  
+        # initialize time variables
+        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
+        self.dt     = self.input.dt
+        self.dtcur      = self.dt
+        self.firsttime = True
+        self.t      = 0
+ 
+        # Some sanity checks for valid input
+        if (self.c_beta is None): 
+            self.c_beta = 0                     # Zero curvature; linear response
+        assert(self.c_beta >= 0 or self.c_beta <= 1)
+
+        # initialize output
+        self.out = model_output(self.tsteps)
+ 
+        self.statistics()
+  
+        # calculate initial diagnostic variables
+        if(self.sw_rad):
+            self.run_radiation()
+ 
+        if(self.sw_sl):
+            for i in range(10): 
+                self.run_surface_layer()
+  
+        if(self.sw_ls):
+            self.run_land_surface()
+
+        if(self.sw_cu):
+            self.run_mixed_layer()
+            self.run_cumulus()
+        
+        if(self.sw_ml):
+            self.run_mixed_layer()
+
+    def timestep(self):
+
+        self.dtmax = +np.inf
+        self.logger.debug('before stats') 
+        self.statistics()
+
+        # run radiation model
+        self.logger.debug('before rad') 
+        if(self.sw_rad):
+            self.run_radiation()
+  
+        # run surface layer model
+        if(self.sw_sl):
+            self.logger.debug('before surface layer') 
+            self.run_surface_layer()
+        
+        # run land surface model
+        if(self.sw_ls):
+            self.logger.debug('before land surface') 
+            self.run_land_surface()
+ 
+        # run cumulus parameterization
+        if(self.sw_cu):
+            self.logger.debug('before cumulus') 
+            self.run_cumulus()
+   
+        self.logger.debug('before mixed layer') 
+        # run mixed-layer model
+        if(self.sw_ml):
+            self.run_mixed_layer()
+        self.logger.debug('after mixed layer') 
+ 
+        #get first profile data point above mixed layer
+        if self.sw_ap:
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                # here we correct for the fact that the upper profile also
+                # shifts in the vertical.
+
+                diffhtend = self.htend - self.air_ap.w[zidx_first]
+                if diffhtend > 0:
+                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            else:
+                if self.htend > 0:
+                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
+                    self.dtmax= min(dtmax_new,self.dtmax)
+            #print(self.h,zidx_first,self.ws,self.air_ap.z)
+
+        
+        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
+        self.logger.debug('before store') 
+        self.substep =  (self.dtcur > self.dtmax)
+        if self.substep:
+            dtnext = self.dtcur - self.dtmax
+            self.dtcur = self.dtmax
+
+        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
+
+        # HW: this will be done multiple times in case of a substep is needed
+        # store output before time integration
+        if self.firsttime:
+            self.store()
+  
+        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
+        # time integrate land surface model
+        if(self.sw_ls):
+            self.integrate_land_surface()
+        self.logger.debug('before integrate mixed layer') 
+        # time integrate mixed-layer model
+        if(self.sw_ml):
+            self.integrate_mixed_layer() 
+        self.logger.debug('after integrate mixed layer') 
+        if self.substep:
+            self.dtcur = dtnext
+            self.firsttime = False
+            self.substeps += 1
+        else:
+            self.dtcur = self.dt
+            self.t += 1 
+            self.firsttime = True
+            self.substeps = 0
+        self.logger.debug('going to next step')
+        
+        
+  
+    def statistics(self):
+        # Calculate virtual temperatures 
+        self.thetav   = self.theta  + 0.61 * self.theta * self.q
+        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
+        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
+        # Mixed-layer top properties
+        self.P_h    = self.Ps - self.rho * self.g * self.h
+        # else:
+            # in the other case, it is updated at the time that the profile is
+            # updated (and at the initialization
+
+        self.T_h    = self.theta - self.g/self.cp * self.h
+
+        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
+        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
+
+        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
+
+        # Find lifting condensation level iteratively
+        if(self.t == 0):
+            self.lcl = self.h
+            RHlcl = 0.5
+        else:
+            RHlcl = 0.9998 
+
+        itmax = 30
+        it = 0
+        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
+            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
+            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
+        else:
+            self.q2_h   = 0.
+            self.CO22_h = 0.
+
+        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
+        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
+        self.M      = self.ac * self.wstar
+        self.wqM    = self.M * self.q2_h**0.5
+
+        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
+        if(self.dCO2 < 0):
+            self.wCO2M  = self.M * self.CO22_h**0.5
+        else:
+            self.wCO2M  = 0.
+
+    def run_mixed_layer(self):
+        if(not self.sw_sl):
+            # decompose ustar along the wind components
+            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
+            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
+
+
+
+        # calculate large-scale vertical velocity (subsidence)
+        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
+            self.ws = -self.divU * self.h
+        # else:
+        #     in case the air circulation switch is turned on, subsidence is
+        #     calculated from the circulate profile at the initialization and
+        #     in the integrate_mixed_layer routine
+              
+        # calculate compensation to fix the free troposphere in case of subsidence 
+        if(self.sw_fixft):
+            w_th_ft  = self.gammatheta * self.ws
+            w_q_ft   = self.gammaq     * self.ws
+            w_CO2_ft = self.gammaCO2   * self.ws 
+        else:
+            w_th_ft  = 0.
+            w_q_ft   = 0.
+            w_CO2_ft = 0. 
+      
+        # calculate mixed-layer growth due to cloud top radiative divergence
+        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
+       
+        # calculate convective velocity scale w* 
+        if(self.wthetav > 0.):
+            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
+        else:
+            self.wstar  = 1e-6;
+      
+        # Virtual heat entrainment flux 
+        self.wthetave    = -self.beta * self.wthetav 
+        
+        # compute mixed-layer tendencies
+        if(self.sw_shearwe):
+            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
+        else:
+            self.we    = -self.wthetave / self.dthetav
+        # Don't allow boundary layer shrinking if wtheta < 0 
+        if(self.we < 0):
+            self.we = 0.
+
+        # Calculate entrainment fluxes
+        self.wthetae     = -self.we * self.dtheta
+        self.wqe         = -self.we * self.dq
+        self.wCO2e       = -self.we * self.dCO2
+        
+        htend_pre       = self.we + self.ws + self.wf - self.M
+        
+        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+        
+ 
+        #print('thetatend_pre',thetatend_pre)
+        
+        #preliminary boundary-layer top chenage
+        #htend_pre = self.we + self.ws + self.wf - self.M
+        #preliminary change in temperature jump
+        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
+                          thetatend_pre + w_th_ft
+        
+        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
+        l_entrainment = True
+
+        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
+            l_entrainment = False
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! temperature jump is at the lower limit \
+                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+        elif dtheta_pre < 0.1:
+            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
+            l_entrainment = True
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          " Warning! Potential temperature jump at mixed- \
+                          layer height would become too low limiting timestep \
+                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+            self.dtmax = min(self.dtmax,dtmax_new)
+            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "next subtimestep, entrainment will be disabled")
+            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
+
+
+
+        # when entrainment is disabled, we just use the simplified formulation
+        # as in Wouters et al., 2013 (section 2.2.1)
+
+        self.dthetatend = l_entrainment*dthetatend_pre + \
+                        (1.-l_entrainment)*0.
+        self.thetatend = l_entrainment*thetatend_pre + \
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = l_entrainment*htend_pre + \
+                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
+        #stop
+
+
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+
+
+        # self.qtend = l_entrainment*qtend_pre + \
+        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
+        # self.CO2tend = l_entrainment*CO2tend_pre + \
+        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+
+
+        #     # part of the timestep for which the temperature mixed-layer jump
+        #     # was changing, and for which entrainment took place. For the other
+        #     # part, we don't assume entrainment anymore, and we use the
+        #     # simplified formulation  of Wouters et al., 2013
+
+        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
+        #   
+        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
+        #                      self.dthetatend + w_th_ft) + \
+        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
+        #     self.htend = fac*self.htend + \
+        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
+        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
+        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
+
+        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+
+        # else:
+        #     #self.htend = htend_pre
+        #     self.dthetatend = dthetatend_pre
+        #     self.thetatend = thetatend_pre
+        
+        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+     
+        # assume u + du = ug, so ug - u = du
+        if(self.sw_wind):
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+  
+            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
+        
+        # tendency of the transition layer thickness
+        if(self.ac > 0 or self.lcl - self.h < 300):
+            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
+        else:
+            self.dztend = 0.
+
+   
+    def integrate_mixed_layer(self):
+        # set values previous time step
+        h0      = self.h
+        
+        theta0  = self.theta
+        dtheta0 = self.dtheta
+        q0      = self.q
+        dq0     = self.dq
+        CO20    = self.CO2
+        dCO20   = self.dCO2
+        
+        u0      = self.u
+        du0     = self.du
+        v0      = self.v
+        dv0     = self.dv
+
+        dz0     = self.dz_h
+  
+        # integrate mixed-layer equations
+        
+            
+
+# END -- HW 20170606        
+        self.h        = h0      + self.dtcur * self.htend
+        # print(self.h,self.htend)
+        # stop
+        self.theta    = theta0  + self.dtcur * self.thetatend
+        #print(dtheta0,self.dtcur,self.dthetatend)
+        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+        self.q        = q0      + self.dtcur * self.qtend
+        self.dq       = dq0     + self.dtcur * self.dqtend
+        self.CO2      = CO20    + self.dtcur * self.CO2tend
+        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
+        self.dz_h     = dz0     + self.dtcur * self.dztend
+            
+        # Limit dz to minimal value
+        dz0 = 50
+        if(self.dz_h < dz0):
+            self.dz_h = dz0 
+  
+        if(self.sw_wind):
+            self.u        = u0      + self.dtcur * self.utend
+            self.du       = du0     + self.dtcur * self.dutend
+            self.v        = v0      + self.dtcur * self.vtend
+            self.dv       = dv0     + self.dtcur * self.dvtend
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+            for var in ['t','q','u','v']:
+                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
+
+            # take into account advection for the whole profile
+                
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
+
+            var = 'z'
+            #print(self.air_ap[var])
+                #     print(self.air_ap['adv'+var])
+
+
+
+
+            #moving the profile vertically according to the vertical wind
+                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
+
+
+            # air_apvarold = pd.Series(np.array(self.air_ap.z))
+            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
+            # stop
+
+
+                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
+                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
+
+            #As t is updated, we also need to recalculate theta (and R)
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+
+            # air_aptheta_old = pd.Series(self.air_ap['theta'])
+            self.air_ap['theta'] = \
+                        self.air_ap.t * \
+                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
+            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
+                                         self.dtcur * self.air_ap.w[zidx_first:]
+
+#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
+#            print(self.t, self.dtcur,self.dt,self.htend)
+
+            # # the pressure levels of the profiles are recalculated according to
+            # # there new height (after subsidence)
+            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
+            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
+            #         * self.dtcur *  self.air_ap.w[zidx_first:]
+
+            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
+                    self.dtcur * self.air_ap.wp[zidx_first:]
+
+            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
+        # note that theta and q itself are updatet by class itself
+
+    
+        if self.sw_ap:
+            # Just for model consistency preservation purposes, we set the
+            # theta variables of the mixed-layer to nan values, since the
+            # mixed-layer values should overwritte by the mixed-layer
+            # calculations of class.
+            self.air_ap['theta'][0:3] = np.nan 
+            self.air_ap['p'][0:3] = np.nan 
+            self.air_ap['q'][0:3] = np.nan 
+            self.air_ap['u'][0:3] = np.nan 
+            self.air_ap['v'][0:3] = np.nan 
+            self.air_ap['t'][0:3] = np.nan 
+            self.air_ap['z'][0:3] = np.nan 
+
+            # Update the vertical profiles: 
+            #   - new mixed layer properties( h, theta, q ...)
+            #   - any data points below the new ixed-layer height are removed
+
+            # Three data points at the bottom that describe the mixed-layer
+            # properties
+            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
+                                           # columns as air_ap
+            # air_ap_head['z'].iloc[0] = 2.
+            # air_ap_head['z'].iloc[1] = self.__dict__['h']
+            # air_ap_head['z'].iloc[2] = self.__dict__['h']
+            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
+                        [2.,self.__dict__['h'],self.__dict__['h']]
+            for var in ['theta','q','u','v']:
+
+                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
+                        [self.__dict__[var], \
+                         self.__dict__[var], \
+                         self.__dict__[var] + self.__dict__['d'+var]]
+                
+            #print(self.air_ap)
+
+            # This is the remaining profile considering the remaining
+            # datapoints above the mixed layer height
+            air_ap_tail = self.air_ap.iloc[3:]
+            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
+
+            # print('h',self.h)
+            # # only select samples monotonically increasing with height
+            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            # air_ap_tail = pd.DataFrame()
+            # theta_low = self.theta
+            # z_low =     self.h
+            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            # for ibottom in range(1,len(air_ap_tail_orig)):
+            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
+            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+            # make theta increase strong enough to avoid numerical
+            # instability
+            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+            air_ap_tail = pd.DataFrame()
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+            theta_low = self.theta
+            z_low =     self.h
+            ibottom = 0
+            itop = 0
+            # print(air_ap_tail_orig)
+            # stop
+
+            # HW: this is the lower limit that we use for gammatheta, which is
+            # there to avoid model crashes. Besides on this limit, the upper
+            # air profile is modified in a way that is still conserves total
+            # quantities of moisture and temperature. The limit is set by trial
+            # and error. The numerics behind the crash should be investigated
+            # so that a cleaner solution can be provided.
+            gammatheta_lower_limit = 0.002
+            while ((itop in range(0,1)) or (itop != ibottom)):
+                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+                if (
+                    #(z_mean > (z_low+0.2)) and \
+                    #(theta_mean > (theta_low+0.02) ) and \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                  (itop >= (len(air_ap_tail_orig)-1)) \
+                   :
+
+                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                    ibottom = itop+1
+                    theta_low = air_ap_tail.theta.iloc[-1]
+                    z_low =     air_ap_tail.z.iloc[-1]
+    
+
+                itop +=1
+                # elif  (itop > len(air_ap_tail_orig)-10):
+                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+                #print(itop,ibottom)
+
+            if itop > 1:
+                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
+                          "Warning! Temperature profile was too steep. \
+                                  Modifying profile: "+ \
+                                  str(itop - 1)+ " measurements were dropped \
+                                  and replaced with its average \
+                                  Modifying profile. \
+                                  mean with next profile point(s).") 
+
+
+            self.air_ap = pd.concat((air_ap_head,\
+                                     air_ap_tail,\
+                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
+                                                                      axis=1)
+
+            if  self.sw_ac:
+                qvalues = \
+                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
+
+                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
+                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
+                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
+                self.P_h    = self.Ps - self.rho * self.g * self.h
+                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
+                        [self.Ps,  self.P_h, self.P_h-0.1]
+
+                self.air_ap.t = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
+
+
+        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
+
+
+
+
+        # else:
+            # in the other case, it is updated at the time the statistics are
+            # calculated 
+
+        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
+
+
+            self.P_h    = self.Ps - self.rho * self.g * self.h
+            in_ml = (self.air_ac.p >= self.P_h)
+
+            if in_ml.sum() == 0:
+                warnings.warn(" no circulation points in the mixed layer \
+                              found. We just take the bottom one.")
+                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
+            for var in ['t','q','u','v']:
+
+                # calculation of the advection variables for the mixed-layer
+                # these will be used for the next timestep
+                # Warning: w is excluded for now.
+
+                self.__dict__['adv'+var] = \
+                        ((self.air_ac['adv'+var+'_x'][in_ml] \
+                         + \
+                         self.air_ac['adv'+var+'_y'][in_ml])* \
+                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                        self.air_ac['delpdgrav'][in_ml].sum()
+
+                # calculation of the advection variables for the profile above
+                # the mixed layer (also for the next timestep)
+                self.air_ap['adv'+var] = \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p,\
+                                              self.air_ac['adv'+var+'_x']) \
+                                    + \
+                                    np.interp(self.air_ap.p,\
+                                              self.air_ac.p, \
+                                              self.air_ac['adv'+var+'_y'])
+                # if var == 't':
+                #     print(self.air_ap['adv'+var])
+                #     stop
+
+            # as an approximation, we consider that advection of theta in the
+            # mixed layer is equal to advection of t. This is a sufficient
+            # approximation since theta and t are very similar at the surface
+            # pressure.
+
+            self.__dict__['advtheta'] = self.__dict__['advt']
+
+        if (self.sw_ac is not None) and ('w' in self.sw_ac):
+            # update the vertical wind profile
+            self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                          self.air_ac.p, \
+                                          self.air_ac['wp'])
+            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
+                                                 self.Rv*self.air_ap.q)
+            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+            
+            air_apwold = self.air_ap['w']
+            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
+            #print('hello w upd')
+
+            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
+            # # interpolate subsidence x density
+            # self.air_ap['wrho'] = \
+            #            np.interp(self.air_ap.p,\
+            #                      self.air_ach.p,\
+            #                      self.air_ach['wrho']) \
+            #     
+            # self.air_ap['w'] = \
+            #     self.air_ap['wrho']/(self.air_ap.p/ \
+            #                          (self.Rd*(1.-self.air_ap.q) + \
+            #                           self.Rv*self.air_ap.q)* \
+            #                          self.air_ap.TEMP)
+            # # self.wrho = np.interp(self.P_h,\
+            # #                      self.air_ach.p,\
+            # #                      self.air_ach['wrho']) \
+
+
+
+            # Also update the vertical wind at the mixed-layer height
+            # (subsidence)
+            self.ws   = self.air_ap.w[1]
+        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
+
+            ## Finally, we update he 
+            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
+            #                        + \
+            #                        self.air_ac['divU_y'][in_ml])* \
+            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+            #            self.air_ac['delpdgrav'][in_ml].sum() 
+            
+
+        if self.sw_ap:
+            for var in ['theta','q','u','v']:
+
+                # update of the slope (gamma) for the different variables, for
+                # the next timestep!
+
+                # there is an warning message that tells about dividing through
+                # zero, which we ignore
+
+                with np.errstate(divide='ignore'):
+                    gammavar = list(np.array(self.air_ap[var][1:].values - \
+                                             self.air_ap[var][:-1].values) \
+                                    / np.array(self.air_ap['z'][1:].values - \
+                                               self.air_ap['z'][:-1].values))
+
+                    # add last element twice (since we have one element less)
+                gammavar.append(gammavar[-1])
+                gammavar = np.array(gammavar)
+                self.air_ap['gamma'+var] = gammavar
+
+                # Based on the above, update the gamma value at the mixed-layer
+                # top
+                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
+                                                                     self.air_ap.z)[0][-1]]
+
+            
+    def run_radiation(self):
+        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
+        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
+        sinlea = max(sinlea, 0.0001)
+        
+        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
+  
+        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
+  
+        self.Swin  = self.S0 * Tr * sinlea
+        self.Swout = self.alpha * self.S0 * Tr * sinlea
+        
+        
+        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
+        self.Lwout = self.bolz * self.Ts ** 4.
+          
+        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
+        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
+  
+    def run_surface_layer(self):
+        # HW: I had to raise the minimum wind speed to make the simulation with
+        # the non-iterative solution stable (this solution was a wild guess, so I don't
+        # know the exact problem of the instability in case of very low wind
+        # speeds yet)
+        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        # version of 20180730 where there are still some runs crashing. Maybe
+        # an upper limit should be set on the monin-obukhov length instead of
+        # a lower limmit on the wind speed?
+        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
+
+        
+        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
+        qsatsurf       = qsat(self.thetasurf, self.Ps)
+        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
+        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
+
+        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
+  
+        zsl       = 0.1 * self.h
+        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
+        
+
+
+        if self.sw_lit:
+            self.Rib  = min(self.Rib, 0.2)
+            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
+            self.zeta  = zsl/self.L
+            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
+            
+        
+            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
+            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
+            
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+        
+     
+            # diagnostic meteorological variables
+            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
+            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
+            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
+            
+            # diagnostic meteorological variables
+        else:
+            
+            ## circumventing any iteration with Wouters et al., 2012
+            self.zslz0m = np.max((zsl/self.z0m,10.))
+            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
+            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
+            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
+            self.L = zsl/self.zeta
+            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
+        
+            self.Cm = self.k**2.0/funm/funm
+            self.Cs = self.k**2.0/funm/funh
+            
+            self.ustar = np.sqrt(self.Cm) * ueff
+            self.uw    = - self.Cm * ueff * self.u
+            self.vw    = - self.Cm * ueff * self.v
+            
+            # extrapolation from mixed layer (instead of from surface) to 2meter
+            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
+            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
+            self.u2m    =                - self.uw     / self.ustar / self.k * funm
+            self.v2m    =                - self.vw     / self.ustar / self.k * funm
+        
+        
+        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
+        self.e2m    = self.q2m * self.Ps / 0.622
+     
+    def ribtol(self, Rib, zsl, z0m, z0h): 
+        if(Rib > 0.):
+            L    = 1.
+            L0   = 2.
+        else:
+            L  = -1.
+            L0 = -2.
+        #print(Rib,zsl,z0m,z0h)
+        
+        while (abs(L - L0) > 0.001):
+            L0      = L
+            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
+            Lstart  = L - 0.001*L
+            Lend    = L + 0.001*L
+            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
+                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
+                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+            L       = L - fx / fxdif
+            #print(L)
+            if(abs(L) > 1e12):
+                break
+
+        return L
+      
+    def psim(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
+            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psim = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+        return psim
+      
+    def psih(self, zeta):
+        if(zeta <= 0):
+            x     = (1. - 16. * zeta)**(0.25)
+            psih  = 2. * np.log( (1. + x*x) / 2.)
+            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
+            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
+        else:
+            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+        return psih
+ 
+    def jarvis_stewart(self):
+        # calculate surface resistances using Jarvis-Stewart model
+        if(self.sw_rad):
+            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
+        else:
+            f1 = 1.
+  
+        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
+            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
+        else:
+            f2 = 1.e8
+ 
+        # Limit f2 in case w2 > wfc, where f2 < 1
+        f2 = max(f2, 1.);
+ 
+        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
+        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
+  
+        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
+
+    def factorial(self,k):
+        factorial = 1
+        for n in range(2,k+1):
+            factorial = factorial * float(n)
+        return factorial;
+
+    def E1(self,x):
+        E1sum = 0
+        for k in range(1,100):
+            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
+        return -0.57721566490153286060 - np.log(x) - E1sum
+ 
+    def ags(self):
+        # Select index for plant type
+        if(self.c3c4 == 'c3'):
+            c = 0
+        elif(self.c3c4 == 'c4'):
+            c = 1
+        else:
+            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
+
+        # calculate CO2 compensation concentration
+        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
+
+        # calculate mesophyll conductance
+        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
+                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
+        gm            = gm / 1000. # conversion from mm s-1 to m s-1
+  
+        # calculate CO2 concentration inside the leaf (ci)
+        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
+        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
+  
+        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
+        D0            = (self.f0[c] - fmin) / self.ad[c]
+  
+        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
+        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
+        ci            = cfrac * (co2abs - CO2comp) + CO2comp
+  
+        # calculate maximal gross primary production in high light conditions (Ag)
+        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
+  
+        # calculate effect of soil moisture stress on gross assimilation rate
+        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
+  
+        # calculate stress function
+        if (self.c_beta == 0):
+            fstr = betaw;
+        else:
+            # Following Combe et al (2016)
+            if (self.c_beta < 0.25):
+                P = 6.4 * self.c_beta
+            elif (self.c_beta < 0.50):
+                P = 7.6 * self.c_beta - 0.3
+            else:
+                P = 2**(3.66 * self.c_beta + 0.34) - 1
+            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
+  
+        # calculate gross assimilation rate (Am)
+        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
+        Rdark        = (1. / 9.) * Am
+        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
+  
+        # calculate  light use efficiency
+        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
+  
+        # calculate gross primary productivity
+        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
+  
+        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
+        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
+        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
+  
+        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
+        a1           = 1. / (1. - self.f0[c])
+        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
+  
+        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
+  
+        # calculate surface resistance for moisture and carbon dioxide
+        self.rs      = 1. / (1.6 * gcco2)
+        rsCO2        = 1. / gcco2
+  
+        # calculate net flux of CO2 into the plant (An)
+        An           = -(co2abs - ci) / (self.ra + rsCO2)
+  
+        # CO2 soil surface flux
+        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
+        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
+  
+        # CO2 flux
+        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
+        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
+        self.wCO2    = self.wCO2A + self.wCO2R
+ 
+    def run_land_surface(self):
+        # compute ra
+        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
+        #print('ueff',self.u,self.v,self.wstar)
+
+        if(self.sw_sl):
+          self.ra = (self.Cs * ueff)**-1.
+        else:
+          self.ra = ueff / max(1.e-3, self.ustar)**2.
+
+        #print('ra',self.ra,self.ustar,ueff)
+
+        # first calculate essential thermodynamic variables
+        self.esat    = esat(self.theta)
+        self.qsat    = qsat(self.theta, self.Ps)
+        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
+        self.dqsatdT = 0.622 * desatdT / self.Ps
+        self.e       = self.q * self.Ps / 0.622
+
+        if(self.ls_type == 'js'): 
+            self.jarvis_stewart() 
+        elif(self.ls_type == 'ags'):
+            self.ags()
+        else:
+            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
+
+        # recompute f2 using wg instead of w2
+        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
+          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
+        else:
+          f2        = 1.e8
+        self.rssoil = self.rssoilmin * f2 
+ 
+        Wlmx = self.LAI * self.Wmax
+        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
+        self.cliq = min(1., self.Wl / Wlmx) 
+     
+        # calculate skin temperature implictly
+        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
+            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
+            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
+            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
+            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
+
+        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        #print('Ts',self.rs)
+
+        esatsurf      = esat(self.Ts)
+        self.qsatsurf = qsat(self.Ts, self.Ps)
+
+        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
+  
+        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
+  
+        self.LE     = self.LEsoil + self.LEveg + self.LEliq
+        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
+        #print('H',self.ra,self.Ts,self.theta)
+        self.G      = self.Lambda * (self.Ts - self.Tsoil)
+        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
+        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
+        
+        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
+  
+        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
+   
+        d1          = 0.1
+        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
+        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
+        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
+        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
+  
+        # calculate kinematic heat fluxes
+        self.wtheta   = self.H  / (self.rho * self.cp)
+        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
+        self.wq       = self.LE / (self.rho * self.Lv)
+ 
+    def integrate_land_surface(self):
+        # integrate soil equations
+        Tsoil0        = self.Tsoil
+        wg0           = self.wg
+        Wl0           = self.Wl
+  
+        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
+        self.wg       = wg0     + self.dtcur * self.wgtend
+        self.Wl       = Wl0     + self.dtcur * self.Wltend
+  
+    # store model output
+    def store(self):
+        t                      = self.t
+        
+        self.out.time[t]          = t * self.dt / 3600. + self.tstart
+
+        # in case we are at the end of the simulation, we store the vertical
+        # profiles to the output
+        
+        # if t == (len(self.out.time) - 1):
+        #     self.out.air_ac = self.air_ac
+        #     self.out.air_ap = self.air_ap
+
+        
+        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
+        #  for key in self.out.__dict__.keys():
+        #      if key in self.__dict__:
+        #          self.out.__dict__[key][t]  = self.__dict__[key]
+        
+        self.out.h[t]          = self.h
+        
+        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
+        
+        self.out.gammatheta[t] = self.gammatheta
+        self.out.gammau[t]     = self.gammau
+        self.out.gammav[t]     = self.gammav
+        self.out.gammaq[t]     = self.gammaq
+        self.out.theta[t]      = self.theta
+        self.out.thetav[t]     = self.thetav
+        self.out.dtheta[t]     = self.dtheta
+        self.out.dthetav[t]    = self.dthetav
+        self.out.wtheta[t]     = self.wtheta
+        self.out.wthetav[t]    = self.wthetav
+        self.out.wthetae[t]    = self.wthetae
+        self.out.wthetave[t]   = self.wthetave
+        
+        self.out.q[t]          = self.q
+        self.out.dq[t]         = self.dq
+        self.out.wq[t]         = self.wq
+        self.out.wqe[t]        = self.wqe
+        self.out.wqM[t]        = self.wqM
+      
+        self.out.qsat[t]       = self.qsat
+        self.out.e[t]          = self.e
+        self.out.esat[t]       = self.esat
+      
+        fac = (self.rho*self.mco2)/self.mair
+        self.out.CO2[t]        = self.CO2
+        self.out.dCO2[t]       = self.dCO2
+        self.out.wCO2[t]       = self.wCO2  * fac
+        self.out.wCO2e[t]      = self.wCO2e * fac
+        self.out.wCO2R[t]      = self.wCO2R * fac
+        self.out.wCO2A[t]      = self.wCO2A * fac
+
+        self.out.u[t]          = self.u
+        self.out.du[t]         = self.du
+        self.out.uw[t]         = self.uw
+        
+        self.out.v[t]          = self.v
+        self.out.dv[t]         = self.dv
+        self.out.vw[t]         = self.vw
+        
+        self.out.T2m[t]        = self.T2m
+        self.out.q2m[t]        = self.q2m
+        self.out.u2m[t]        = self.u2m
+        self.out.v2m[t]        = self.v2m
+        self.out.e2m[t]        = self.e2m
+        self.out.esat2m[t]     = self.esat2m
+
+
+        self.out.Tsoil[t]      = self.Tsoil
+        self.out.T2[t]         = self.T2
+        self.out.Ts[t]         = self.Ts
+        self.out.wg[t]         = self.wg
+        
+        self.out.thetasurf[t]  = self.thetasurf
+        self.out.thetavsurf[t] = self.thetavsurf
+        self.out.qsurf[t]      = self.qsurf
+        self.out.ustar[t]      = self.ustar
+        self.out.Cm[t]         = self.Cm
+        self.out.Cs[t]         = self.Cs
+        self.out.L[t]          = self.L
+        self.out.Rib[t]        = self.Rib
+  
+        self.out.Swin[t]       = self.Swin
+        self.out.Swout[t]      = self.Swout
+        self.out.Lwin[t]       = self.Lwin
+        self.out.Lwout[t]      = self.Lwout
+        self.out.Q[t]          = self.Q
+  
+        self.out.ra[t]         = self.ra
+        self.out.rs[t]         = self.rs
+        self.out.H[t]          = self.H
+        self.out.LE[t]         = self.LE
+        self.out.LEliq[t]      = self.LEliq
+        self.out.LEveg[t]      = self.LEveg
+        self.out.LEsoil[t]     = self.LEsoil
+        self.out.LEpot[t]      = self.LEpot
+        self.out.LEref[t]      = self.LEref
+        self.out.G[t]          = self.G
+
+        self.out.zlcl[t]       = self.lcl
+        self.out.RH_h[t]       = self.RH_h
+
+        self.out.ac[t]         = self.ac
+        self.out.M[t]          = self.M
+        self.out.dz[t]         = self.dz_h
+        self.out.substeps[t]   = self.substeps
+  
+    # delete class variables to facilitate analysis in ipython
+    def exitmodel(self):
+        del(self.Lv)
+        del(self.cp)
+        del(self.rho)
+        del(self.k)
+        del(self.g)
+        del(self.Rd)
+        del(self.Rv)
+        del(self.bolz)
+        del(self.S0)
+        del(self.rhow)
+  
+        del(self.t)
+        del(self.dt)
+        del(self.tsteps)
+         
+        del(self.h)          
+        del(self.Ps)        
+        del(self.fc)        
+        del(self.ws)
+        del(self.we)
+        
+        del(self.theta)
+        del(self.dtheta)
+        del(self.gammatheta)
+        del(self.advtheta)
+        del(self.beta)
+        del(self.wtheta)
+    
+        del(self.T2m)
+        del(self.q2m)
+        del(self.e2m)
+        del(self.esat2m)
+        del(self.u2m)
+        del(self.v2m)
+        
+        del(self.thetasurf)
+        del(self.qsatsurf)
+        del(self.thetav)
+        del(self.dthetav)
+        del(self.thetavsurf)
+        del(self.qsurf)
+        del(self.wthetav)
+        
+        del(self.q)
+        del(self.qsat)
+        del(self.dqsatdT)
+        del(self.e)
+        del(self.esat)
+        del(self.dq)
+        del(self.gammaq)
+        del(self.advq)
+        del(self.wq)
+        
+        del(self.u)
+        del(self.du)
+        del(self.gammau)
+        del(self.advu)
+        
+        del(self.v)
+        del(self.dv)
+        del(self.gammav)
+        del(self.advv)
+  
+        del(self.htend)
+        del(self.thetatend)
+        del(self.dthetatend)
+        del(self.qtend)
+        del(self.dqtend)
+        del(self.utend)
+        del(self.dutend)
+        del(self.vtend)
+        del(self.dvtend)
+     
+        del(self.Tsoiltend) 
+        del(self.wgtend)  
+        del(self.Wltend) 
+  
+        del(self.ustar)
+        del(self.uw)
+        del(self.vw)
+        del(self.z0m)
+        del(self.z0h)        
+        del(self.Cm)         
+        del(self.Cs)
+        del(self.L)
+        del(self.Rib)
+        del(self.ra)
+  
+        del(self.lat)
+        del(self.lon)
+        del(self.doy)
+        del(self.tstart)
+   
+        del(self.Swin)
+        del(self.Swout)
+        del(self.Lwin)
+        del(self.Lwout)
+        del(self.cc)
+  
+        del(self.wg)
+        del(self.w2)
+        del(self.cveg)
+        del(self.cliq)
+        del(self.Tsoil)
+        del(self.T2)
+        del(self.a)
+        del(self.b)
+        del(self.p)
+        del(self.CGsat)
+  
+        del(self.wsat)
+        del(self.wfc)
+        del(self.wwilt)
+  
+        del(self.C1sat)
+        del(self.C2ref)
+  
+        del(self.LAI)
+        del(self.rs)
+        del(self.rssoil)
+        del(self.rsmin)
+        del(self.rssoilmin)
+        del(self.alpha)
+        del(self.gD)
+  
+        del(self.Ts)
+  
+        del(self.Wmax)
+        del(self.Wl)
+  
+        del(self.Lambda)
+        
+        del(self.Q)
+        del(self.H)
+        del(self.LE)
+        del(self.LEliq)
+        del(self.LEveg)
+        del(self.LEsoil)
+        del(self.LEpot)
+        del(self.LEref)
+        del(self.G)
+  
+        del(self.sw_ls)
+        del(self.sw_rad)
+        del(self.sw_sl)
+        del(self.sw_wind)
+        del(self.sw_shearwe)
+
+# class for storing mixed-layer model output data
+class model_output:
+    def __init__(self, tsteps):
+        self.time          = np.zeros(tsteps)    # time [s]
+
+        # mixed-layer variables
+        self.h          = np.zeros(tsteps)    # ABL height [m]
+        
+        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
+        self.gammau     = np.zeros(tsteps)
+        self.gammav     = np.zeros(tsteps)
+        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
+        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
+        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
+        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
+        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
+        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
+        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
+        
+        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
+        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
+        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
+        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
+        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
+
+        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
+        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
+        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
+
+        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
+        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
+        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
+        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
+        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
+        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
+        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
+        
+        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
+        
+        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
+        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
+
+        # diagnostic meteorological variables
+        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
+        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
+        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
+        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
+        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
+        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
+
+        # ground variables
+        self.Tsoil       = np.zeros(tsteps)
+        self.T2          = np.zeros(tsteps)
+        self.Ts          = np.zeros(tsteps)
+        self.wg          = np.zeros(tsteps)
+
+        # surface-layer variables
+        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
+        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
+        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
+        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
+        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
+        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
+        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
+        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
+        self.L          = np.zeros(tsteps)    # Obukhov length [m]
+        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
+
+        # radiation variables
+        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
+        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
+        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
+        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
+        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
+
+        # land surface variables
+        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
+        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
+        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
+        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
+        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
+        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
+        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
+        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
+        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
+        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
+
+        # Mixed-layer top variables
+        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
+        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
+
+        # cumulus variables
+        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
+        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
+        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
+        
+        
+        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
+
+# class for storing mixed-layer model input data
+class model_input:
+    def __init__(self):
+
+        # # comment not valid
+        # we comment out the initialization, because there is a problem when
+        # inheriting values from one the another class4gl_iput. We also expect
+        # that the user specifies all the required parmameters (if not, an error
+        # is raised). 
+
+        # general model variables
+        self.runtime    = None  # duration of model run [s]
+        self.dt         = None  # time step [s]
+
+        # mixed-layer variables
+        self.sw_ml      = None  # mixed-layer model switch
+        self.sw_shearwe = None  # Shear growth ABL switch
+        self.sw_fixft   = None  # Fix the free-troposphere switch
+        self.h          = None  # initial ABL height [m]
+        self.Ps         = None  # surface pressure [Pa]
+        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
+        self.fc         = None  # Coriolis parameter [s-1]
+        
+        self.theta      = None  # initial mixed-layer potential temperature [K]
+        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
+
+        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
+
+        self.dtheta     = None  # initial temperature jump at h [K]
+        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advtheta   = None  # advection of heat [K s-1]
+        self.beta       = None  # entrainment ratio for virtual heat [-]
+        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
+        
+        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
+        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
+        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
+
+        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
+        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+        self.advq       = None  # advection of moisture [kg kg-1 s-1]
+        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
+
+        self.CO2        = None  # initial mixed-layer potential temperature [K]
+        self.dCO2       = None  # initial temperature jump at h [K]
+        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
+        self.advCO2     = None  # advection of heat [K s-1]
+        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
+        
+        self.sw_wind    = None  # prognostic wind switch
+        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.du         = None  # initial u-wind jump at h [m s-1]
+        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
+        self.advu       = None  # advection of u-wind [m s-2]
+
+        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
+        self.dv         = None  # initial u-wind jump at h [m s-1]
+        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
+        self.advv       = None  # advection of v-wind [m s-2]
+
+        # surface layer variables
+        self.sw_sl      = None  # surface layer switch
+        self.ustar      = None  # surface friction velocity [m s-1]
+        self.z0m        = None  # roughness length for momentum [m]
+        self.z0h        = None  # roughness length for scalars [m]
+        self.Cm         = None  # drag coefficient for momentum [-]
+        self.Cs         = None  # drag coefficient for scalars [-]
+        self.L          = None  # Obukhov length [-]
+        self.Rib        = None  # bulk Richardson number [-]
+
+        # radiation parameters
+        self.sw_rad     = None  # radiation switch
+        self.lat        = None  # latitude [deg]
+        self.lon        = None  # longitude [deg]
+        self.doy        = None  # day of the year [-]
+        self.tstart     = None  # time of the day [h UTC]
+        self.cc         = None  # cloud cover fraction [-]
+        self.Q          = None  # net radiation [W m-2] 
+        self.dFz        = None  # cloud top radiative divergence [W m-2] 
+
+        # land surface parameters
+        self.sw_ls      = None  # land surface switch
+        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
+        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
+        self.Tsoil      = None  # temperature top soil layer [K]
+        self.T2         = None  # temperature deeper soil layer [K]
+        
+        self.a          = None  # Clapp and Hornberger retention curve parameter a
+        self.b          = None  # Clapp and Hornberger retention curve parameter b
+        self.p          = None  # Clapp and Hornberger retention curve parameter p 
+        self.CGsat      = None  # saturated soil conductivity for heat
+        
+        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
+        self.wfc        = None  # volumetric water content field capacity [-]
+        self.wwilt      = None  # volumetric water content wilting point [-]
+        
+        self.C1sat      = None 
+        self.C2ref      = None
+
+        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
+        
+        self.LAI        = None  # leaf area index [-]
+        self.gD         = None  # correction factor transpiration for VPD [-]
+        self.rsmin      = None  # minimum resistance transpiration [s m-1]
+        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
+        self.alpha      = None  # surface albedo [-]
+        
+        self.Ts         = None  # initial surface temperature [K]
+        
+        self.cveg       = None  # vegetation fraction [-]
+        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
+        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
+        
+        self.Lambda     = None  # thermal diffusivity skin layer [-]
+
+        # A-Gs parameters
+        self.c3c4       = None  # Plant type ('c3' or 'c4')
+
+        # Cumulus parameters
+        self.sw_cu      = None  # Cumulus parameterization switch
+        self.dz_h       = None  # Transition layer thickness [m]
+        
+# BEGIN -- HW 20171027
+        # self.cala       = None      # soil heat conductivity [W/(K*m)]
+        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
+# END -- HW 20171027
diff --git a/lib/ribtol/Makefile b/lib/ribtol/Makefile
new file mode 100644
index 0000000..e23e3e1
--- /dev/null
+++ b/lib/ribtol/Makefile
@@ -0,0 +1,8 @@
+ribtol.so : ribtol.o
+	g++ -O3 -shared -Wl -z -def -o ribtol.so -lpython2.6 -lboost_python ribtol.o
+
+ribtol.o : ribtol.cpp
+	g++ -c -O3 -fPIC ribtol.cpp -I/usr/include/python2.6
+
+clean : 
+	rm -rf ribtol.o ribtol.so
diff --git a/lib/ribtol/MakefileMac b/lib/ribtol/MakefileMac
new file mode 100644
index 0000000..bf34ea8
--- /dev/null
+++ b/lib/ribtol/MakefileMac
@@ -0,0 +1,9 @@
+# Note: boost-python needs to be installed: brew install boost-python -with-python3 -without-python
+ribtol.so : ribtol.o
+	clang++ -O3 -shared -o ribtol.so -L/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib -lpython3.6m -L/usr/local/lib -lboost_python3-mt -lpython ribtol.o
+
+ribtol.o : ribtol.cpp
+	clang++ -c -O3 -fPIC ribtol.cpp -I/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/include/python3.6m -I/usr/local/include
+
+clean : 
+	rm -rf ribtol.o ribtol.so
diff --git a/lib/ribtol/ribtol.cpp b/lib/ribtol/ribtol.cpp
new file mode 100644
index 0000000..148b0d3
--- /dev/null
+++ b/lib/ribtol/ribtol.cpp
@@ -0,0 +1,81 @@
+// fast conversion of bulk Richardson number to Obukhov length
+
+#include 
+#include 
+#include 
+using namespace std;
+
+inline double psim(double zeta)
+{
+  double psim;
+  double x;
+  if(zeta <= 0.)
+  {
+    //x     = (1. - 16. * zeta) ** (0.25)
+    //psim  = 3.14159265 / 2. - 2. * arctan(x) + log( (1.+x) ** 2. * (1. + x ** 2.) / 8.)
+    x    = pow(1. + pow(3.6 * abs(zeta),2./3.), -0.5);
+    psim = 3. * log( (1. + 1. / x) / 2.);
+  }
+  else
+  {
+    psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35;
+  }
+  return psim;
+}
+    
+inline double psih(double zeta)
+{
+  double psih;
+  double x;
+  if(zeta <= 0.)
+  {
+    // x     = (1. - 16. * zeta) ** (0.25)
+    // psih  = 2. * log( (1. + x ** 2.) / 2. )
+    x     = pow(1. + pow(7.9 * abs(zeta), (2./3.)), -0.5);
+    psih  = 3. * log( (1. + 1. / x) / 2.);
+  }
+  else
+  {
+    psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - pow(1. + (2./3.) * zeta, 1.5) - (10./3.) / 0.35 + 1.;
+  }
+  return psih;
+}
+
+
+double ribtol(double Rib, double zsl, double z0m, double z0h)
+{
+  double L, L0;
+  double Lstart, Lend;
+  double fx, fxdif;
+
+  if(Rib > 0.)
+  {
+    L    = 1.;
+    L0   = 2.;
+  }
+  else
+  {
+    L  = -1.;
+    L0 = -2.;
+  }
+    
+  while (abs(L - L0) > 0.001)
+  {
+    L0      = L;
+    fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / pow(log(zsl / z0m) - psim(zsl / L) + psim(z0m / L), 2.);
+    Lstart  = L - 0.001 * L;
+    Lend    = L + 0.001 * L;
+    fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / pow(log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart), 2.)) - (-zsl / Lend * (log(zsl / z0h) - psih(zsl / Lend) + psih(z0h / Lend)) / pow(log(zsl / z0m) - psim(zsl / Lend) + psim(z0m / Lend), 2.)) ) / (Lstart - Lend);
+    L       = L - fx / fxdif;
+  }
+  
+  return L;
+
+}
+
+BOOST_PYTHON_MODULE(ribtol)
+{
+    using namespace boost::python;
+    def("ribtol", ribtol);
+}
+
diff --git a/lib/ribtol/ribtol.pyx b/lib/ribtol/ribtol.pyx
new file mode 100644
index 0000000..e11a147
--- /dev/null
+++ b/lib/ribtol/ribtol.pyx
@@ -0,0 +1,48 @@
+#cython: boundscheck=False
+#cython: wraparound=False
+
+from libc.math cimport atan, log, exp, fabs
+
+cdef double psim(double zeta):
+    cdef double x, psim
+
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psim  = 3.14159265 / 2. - 2. * atan(x) + log((1. + x)**2. * (1. + x**2.) / 8.)
+    else:
+        psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
+    return psim
+      
+cdef double psih(double zeta):
+    if(zeta <= 0):
+        x     = (1. - 16. * zeta)**(0.25)
+        psih  = 2. * log( (1. + x*x) / 2.)
+    else:
+        psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
+    return psih
+
+def ribtol(double Rib, double zsl, double z0m, double z0h): 
+    cdef double L, L0, fx, Lstart, Lend, fxdif
+
+    if(Rib > 0.):
+        L    = 1.
+        L0   = 2.
+    else:
+        L  = -1.
+        L0 = -2.
+    
+    while (fabs(L - L0) > 0.001):
+        L0      = L
+        fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
+        Lstart  = L - 0.001*L
+        Lend    = L + 0.001*L
+        fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
+                                      (log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
+                  - (-zsl /  Lend   * (log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
+                                      (log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
+        L       = L - fx / fxdif
+
+        if(fabs(L) > 1e15):
+            break
+
+    return L
diff --git a/lib/ribtol/ribtol_hw.py b/lib/ribtol/ribtol_hw.py
new file mode 100644
index 0000000..1946cc8
--- /dev/null
+++ b/lib/ribtol/ribtol_hw.py
@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jan 12 10:46:20 2018
+
+@author: vsc42247
+"""
+
+
+
+# purpose of calc_cm_ch: calculate momentum and thermal turbulent diffusion coefficients of the surface layer with a non-iterative procedure (Wouters et al., 2012)
+
+# input:
+
+# zrib = bulk Richardson number = (g/T)* DT * z/(ua^2)
+#   with:
+#     g = 9.81 m/s2 the gravitational acceleration
+#     z = height (in meters) of the surface layer under consideration 
+#     T = (reference) temperature (in Kelvin) at height z 
+#     DT = (T - T_s) = temperature (in Kelvin) gradient between the surface and height z 
+#     u_a^2 = u^2 +  v^2 is the squared horizontal absolute wind speed 
+# zzz0m = ratio z/z0 between the height z and the momentum roughness length z0m
+# zkbm = ln(z0m/z0h), with z0m, z0h the momentum and thermal roughness length, respectively.
+
+# output: diffusion coefficients (CM and CH) which cna be used to determine surface-layer turbulent transport
+# u'w' = - CM ua^2.
+# w'T' = - CH ua DT 
+
+
+# Reference:
+# Wouters, H., De Ridder, K., and Lipzig, N. P. M.: Comprehensive
+# Parametrization of Surface-Layer Transfer Coefficients for Use
+# in Atmospheric Numerical Models, Bound.-Lay. Meteorol., 145,
+# 539–550, doi:10.1007/s10546-012-9744-3, 2012.
+
+import numpy as np
+
+def calc_cm_ch (zeta,zzz0m,zkbm):
+    krm = 0.4
+
+    #ZETA = zeta_hs2(zrib,zzz0m,zkbm)
+    FUNM,FUNH = funcsche(ZETA,zzz0m,zkbm)
+    CM = krm**2.0/FUNM/FUNM
+    CH = krm**2.0/FUNM/FUNH
+
+    # FUNMn,FUNHn = funcsche(0.,zzz0m,zkbm)
+    # CMn = krm**2.0/FUNMn/FUNMn
+    # CHn = krm**2.0/FUNMn/FUNHn
+
+    # print ZETA,FUNM,FUNH
+    # print 'CMCMN',CM/CMn
+    # print 'CHCHN',CH/CHn
+
+    return CM,CH
+
+
+def zeta_hs2(RiB,zzz0m,kBmin1):
+    #print(RiB,zzz0m,kBmin1)
+    mum=2.59
+    muh=0.95
+    nu=0.5
+    lam=1.5
+
+    betah = 5.0
+
+    zzz0h = zzz0m*np.exp(kBmin1)
+    zzzs = zzz0m*0.06 # to be changed!! r. 101 nog bekijken!!
+
+    L0M = np.log(zzz0m)
+    L0H = np.log(zzz0h)
+    facM = np.log(1.+lam/mum/zzzs)*np.exp(-mum*zzzs)/lam
+    facH = np.log(1.+lam/muh/zzzs)*np.exp(-muh*zzzs)/lam
+    L0Ms = L0M + facM 
+    L0Hs = L0H + facH
+
+    if RiB < 0.:
+        p = np.log(1.-RiB)
+        Q = -0.486 +0.219*p - 0.0331*p**2-4.93*np.exp(-L0H) - 3.65/L0H +\
+            0.38*p/L0H+ 14.8/L0H/L0H-0.946*p/L0H/L0H-10.0/L0H**3+ \
+            0.392*L0M/L0H-0.084*p*L0M/L0H+0.368*L0M/L0H/L0H
+        # print 'p: ',p
+        # print 'Q: ',Q
+        zeta = (1. + p*Q)* L0Ms**2/L0Hs * RiB
+    else:
+        betam = 4.76+7.03/zzz0m +0.24*zzz0m/zzz0h # to be changed
+        # betam = 5.0 + 1.59*10.**(-5.)*(np.exp(13.0-L0M)-1.0) \
+        #         +0.24*(np.exp(-kBmin1)-1.0) # to be changed!!
+        # print('betam',betam)
+        lL0M = np.log(L0M)
+        S0Ms = 1.-1./zzz0m + (1.+nu/mum/zzzs)*facM
+        S0Hs = 1.-1./zzz0h + (1.+nu/muh/zzzs)*facH
+        zetat = -0.316-0.515*np.exp(-L0H) + 25.8 *np.exp(-2.*L0H) + 4.36/L0H \
+                -6.39/L0H/L0H+0.834*lL0M - 0.0267*lL0M**2
+        # print('zetat',zetat)
+        RiBt = zetat *(L0Hs+ S0Hs*betah*zetat)/(L0Ms+S0Ms*betam*zetat)**2 
+        # print('RiBt',RiBt)
+
+        if (RiB > RiBt):
+            D = (L0Ms+S0Ms*betam*zetat)**3/\
+                (L0Ms*L0Hs+zetat*(2.*S0Hs * betah * L0Ms - S0Ms*betam*L0Hs))
+            zeta = zetat + D*(RiB-RiBt)
+        else:
+            r = RiB - S0Hs*betah/(S0Ms*betam)**2
+            B = S0Ms*betam*L0Hs- 2.*S0Hs*betah*L0Ms
+            C = 4.*(S0Ms*betam)**2 * L0Ms *(S0Hs*betah*L0Ms-S0Ms*betam*L0Hs)
+            zeta = - L0Ms / S0Ms/betam - B*C/(4.*(S0Ms*betam)**3 *(B**2+abs(C*r)))
+            if r != 0:
+                zeta = zeta + (B-np.sqrt(B**2+C*r) + B*C*r/(2.*(B**2+abs(C*r))))/(2.*(S0Ms*betam)**3*r)
+    # print('zeta',zeta)
+    return zeta
+
+def funcsche(zeta,zzz0,kBmin1):
+
+
+    mum=2.5
+    muh=0.9
+    nu=0.5
+    lam=1.5
+    
+    p2=3.141592/2.
+    
+    lnzzz0=np.log(zzz0)
+    zzzs=zzz0*0.06
+    zetamcorr=(1.+nu/(mum*zzzs))*zeta
+    zetam0=zeta/zzz0
+    zetahcorr=(1.+nu/(muh*zzzs))*zeta
+    zetah0=zeta/(zzz0*np.exp(kBmin1))
+    
+    if (zeta <= 0.):
+    
+        gamma=15.2
+        alfam=0.25
+        xx=(1.-gamma*zeta)**alfam
+        psim=2.*np.log((1.+xx)/2.)+np.log((1.+xx**2.)/2.)-2.*np.arctan(xx)+p2
+        xx0=(1.-gamma*zetam0)**alfam
+        psim0=2.*np.log((1.+xx0)/2.)+np.log((1.+xx0**2.)/2.)-2.*np.arctan(xx0)+p2
+        phimcorr=(1.-gamma*zetamcorr)**(-alfam)
+        
+        alfah=0.5
+        yy=(1.-gamma*zeta)**alfah
+        psih=2.*np.log((1.+yy)/2.)
+        yy0=(1.-gamma*zetah0)**alfah
+        psih0=2.*np.log((1.+yy0)/2.)
+        phihcorr=(1.-gamma*zetahcorr)**(-alfah)
+    else: 
+    
+        aa=6.1
+        bb=2.5
+        psim=-aa*np.log(zeta+(1.+zeta**bb)**(1./bb))
+        psim0=-aa*np.log(zetam0+(1.+zetam0**bb)**(1./bb))
+        phimcorr=1.+aa*(zetamcorr+zetamcorr**bb*(1.+zetamcorr**bb)**((1.-bb)/bb))/(zetamcorr+(1.+zetamcorr**bb)**(1./bb))
+        
+        cc=5.3
+        dd=1.1
+        psih=-cc*np.log(zeta+(1.+zeta**dd)**(1./dd))
+        psih0=-cc*np.log(zetah0+(1.+zetah0**dd)**(1./dd))
+        phihcorr=1.+cc*(zetahcorr+zetahcorr**dd*(1.+zetahcorr**dd)**((1.-dd)/dd))/(zetahcorr+(1.+zetahcorr**dd)**(1./dd))
+    
+    psistrm=phimcorr*(1./lam)*np.log(1.+lam/(mum*zzzs))*np.exp(-mum*zzzs)
+    psistrh=phihcorr*(1./lam)*np.log(1.+lam/(muh*zzzs))*np.exp(-muh*zzzs)
+    
+    funm=lnzzz0-psim+psim0 +psistrm
+    funh=lnzzz0+kBmin1-psih+psih0 +psistrh
+    return funm,funh
+
diff --git a/lib/ribtol/setup.py b/lib/ribtol/setup.py
new file mode 100644
index 0000000..bfb44db
--- /dev/null
+++ b/lib/ribtol/setup.py
@@ -0,0 +1,12 @@
+# build with "python setup.py build_ext --inplace"
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Build import cythonize
+import numpy as np
+import os
+
+os.environ["CC"] = "g++-7"
+
+setup(
+    ext_modules = cythonize((Extension("ribtol", sources=["ribtol.pyx"], include_dirs=[np.get_include()], ), ))
+)
diff --git a/setup.py b/setup.py
index bfb44db..a806fa0 100644
--- a/setup.py
+++ b/setup.py
@@ -1,12 +1,9 @@
-# build with "python setup.py build_ext --inplace"
 from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Build import cythonize
-import numpy as np
-import os
-
-os.environ["CC"] = "g++-7"
 
 setup(
-    ext_modules = cythonize((Extension("ribtol", sources=["ribtol.pyx"], include_dirs=[np.get_include()], ), ))
+        name='class4gl',
+        version='0.1dev',
+        packages=['lib','bin'],
+        license='GPLv3 licence',
+        long_description=open('README.md').read(),
 )
diff --git a/trash/data_ground.py b/trash/data_ground.py
new file mode 100644
index 0000000..d4e0b5a
--- /dev/null
+++ b/trash/data_ground.py
@@ -0,0 +1,393 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov  7 10:51:03 2017
+
+@author: vsc42247
+
+Purpose: Set surface conditions for the CLASS boundary-layer model
+"""
+
+
+import netCDF4 as nc4
+import numpy as np
+import datetime as dt
+#you can install with
+import pynacolada as pcd
+import pandas as pd
+
+def get_class4gl_ground(class_settings,**kwargs):   
+    
+    key = "IGBPDIS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+    
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
+        print('reading soil water saturation from '+input_fn)
+
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wsat'] = input_nc.variables['wsat'][ilon,ilat]
+        input_nc.close()
+
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc"
+        print('reading soil water field capacity from '+input_fn)
+    
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wfc'] = input_nc.variables['wfc'][ilon,ilat]
+        input_nc.close()
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc"
+        print('reading soil wilting point from '+input_fn)
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__['wwilt'] = input_nc.variables['wwp'][ilon,ilat]
+        input_nc.close()
+        
+    key = "GLEAM"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+        
+        #INPUT_gleam = gleam() 
+        #INPUT_gleam.path = "/kyukon/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/" 
+        
+        gleam_path = "/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/"
+        print('reading soil-water content for "+str(class_settings,datetime.year)+" from '+gleam_path)
+        
+        gleam_files = {}
+        
+        gleam_vars = ['SMroot','SMsurf']
+        
+        for VAR in gleam_vars:
+            gleam_files[VAR] = nc4.Dataset(gleam_path+'/'+str(class_settings.datetime.year)+'/'+VAR+'_'+str(class_settings.datetime.year)+'_GLEAM_v3.1a.nc','r')
+        
+
+        year = class_settings.datetime.year
+        day = class_settings.datetime.day
+        hour = class_settings.datetime.hour
+  
+        ilat = np.where(gleam_files['SMsurf'].variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(gleam_files['SMsurf'].variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        VAR = 'SMsurf'; class_settings.wg = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
+        VAR = 'SMroot'; class_settings.w2 = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
+        
+        for VAR in gleam_vars:
+            gleam_files[VAR].close()
+    
+    key = "MOD44B"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+    
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc"
+        print('initializing vegetation fraction from '+input_fn)
+        var = 'cveg'
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        class_settings.__dict__[var] = input_nc.variables['fv'][ilon,ilat]
+        input_nc.close()
+        
+    key = "DSMW"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
+         # Procedure of the thermal properties:
+         # 1. determine soil texture from DSMW
+         # 2. soil type with look-up table (according to DWD/EXTPAR)
+         # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987) 
+         #    with parameter look-up table from Noilhan and Planton (1989). 
+         #    Note: The look-up table is inspired on DWD/COSMO
+                 
+       
+        #preparing for soil thermal properties
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc"
+        
+        print("deriving soil thermal properties for the force-restore methodes from the soil texture file "+ input_fn)
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        DSMW = input_nc.variables['DSMW'][ilat,ilon]
+        
+        
+        #EXTPAR: zfine   = soil_texslo(soil_unit)%tex_fine
+        SP = {}; SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code']
+        for SPKEY in SPKEYS: 
+            SP[SPKEY] = np.array(input_nc.variables[SPKEY][DSMW])
+        input_nc.close()
+        
+        SP['texture'] = (0.5*SP['tex_medium']+1.0*SP['tex_coarse']) /(SP['tex_coarse']+SP['tex_medium']+SP['tex_fine'])
+        
+        if pd.isnull(SP['texture']):
+            print('Warning, texture is invalid> Setting to Ocean')
+            SP['itex'] = 9
+        
+        else:
+            SP['itex'] = int(SP['texture']*100)
+        
+        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+        SP['isoil'] = np.zeros_like(SP['itex'],dtype=np.int)
+        LOOKUP = [
+                  [0 ,7],# fine textured, clay (soil type 7)
+                  [20,6],# medium to fine textured, loamy clay (soil type 6)
+                  [40,5],# medium textured, loam (soil type 5)
+                  [60,4],# coarse to medium textured, sandy loam (soil type 4)
+                  [80,3],# coarse textured, sand (soil type 3)
+                ]
+        for iitex,iisoil in LOOKUP: 
+            SP['isoil'][SP['itex'] >= iitex ] = iisoil 
+        
+        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
+        LOOKUP = [
+                  [9001, 1 ], # ice, glacier (soil type 1) 
+                  [9002, 2 ], # rock, lithosols (soil type 2)
+                  [9003, 3 ], # salt, set soiltype to sand (soil type 3)
+                  [9004, 8 ], # histosol, e.g. peat (soil type 8)
+                  [9,    9 ], # undefined (ocean)
+                  [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
+                  [9000, 9 ], # undefined (inland lake)
+                  [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
+                  [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
+                ]
+        # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
+        for icode,iisoil in LOOKUP: 
+            SP['isoil'][SP['code'] == icode] = iisoil 
+        
+        #adopted from data_soil.f90 (COSMO5.0)
+        SP_LOOKUP = { 
+          # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea  
+          # (by index)                                           loam                    loam                                water      ice
+          'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
+          'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
+          'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
+          'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
+          'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
+          'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
+          'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
+          'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
+          'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
+          'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
+          'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
+          'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
+          'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
+          'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
+          'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
+          'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
+          'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
+          'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
+          'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
+          #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
+          'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , np.nan    , np.nan   ,  np.nan  ],
+          #error in table 2 of NP89: values need to be multiplied by e-6
+          'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
+          'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , np.nan    , np.nan   ,  np.nan  ],
+          'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , np.nan    , np.nan   ,  np.nan  ],
+          'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , np.nan    , np.nan   ,  np.nan  ],
+          'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , np.nan    , np.nan   ,  np.nan  ],
+        }
+        
+        for SPKEY in SP_LOOKUP.keys(): 
+            SP[SPKEY] = np.zeros_like(SP['isoil'],dtype=np.float)
+        
+        for i in range(11):
+            SELECT = (SP['isoil'] == i)
+            for SPKEY in SP_LOOKUP.keys(): 
+                SP[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
+        
+        for SPKEY in list(SP_LOOKUP.keys())[-6:]: 
+            var = SPKEY
+            class_settings.__dict__[var] = np.float(SP[SPKEY])
+            
+        # only print the last parameter value in the plot
+        
+        #inputs.append(cp.deepcopy(class_settings))
+        #var = 'cala'
+        #class_settings.__dict__[var] = np.float(SP['cala0'])
+        #valnew = class_settings.__dict__[var]
+        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+        
+        #inputs.append(cp.deepcopy(class_settings))
+        #var = 'crhoc'
+        #class_settings.__dict__[var] = np.float(SP['crhoc'])
+        #valnew = class_settings.__dict__[var]
+        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+        
+    key = "CERES"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
+        
+        CERES_start_date = dt.datetime(2000,3,1)
+        DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
+        DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
+        print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
+            
+        var = 'cc'
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        print(class_settings.lat,class_settings.lon)
+        
+        class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:(idatetime+class_settings.runtime),ilat,ilon])/100.
+   
+        input_nc.close()
+    
+    key = "GIMMS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
+       
+    
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
+        print("Reading Leag Area Index from "+input_fn)
+        var = 'LAI'
+        
+        #plt.plot
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
+        
+        print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
+        tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
+        
+        if np.isnan(tarray[idatetime]):
+            print("interpolating GIMMS cveg nan value")
+            
+            mask = np.isnan(tarray)
+            if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
+                tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
+            else:
+                print("Warning. Could not interpolate GIMMS cveg nan value")
+                
+        class_settings.__dict__[var] = tarray[idatetime]
+        
+        input_nc.close()
+ 
+    key = "IGBPDIS_ALPHA"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        var = 'alpha'
+        
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
+        print("Reading albedo from "+input_fn)
+    
+        input_nc = nc4.Dataset(input_fn,'r')
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        
+        landfr = {}
+        for ltype in ['W','B','H','TC']:   
+            landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
+        
+        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
+        
+        alpha=0.
+        for ltype in landfr.keys():
+            alpha += landfr[ltype]*aweights[ltype]
+        
+        
+        class_settings.__dict__[var] = alpha
+        input_nc.close()        
+        
+        
+    key = "ERAINT_ST"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
+        print("Reading soil temperature from "+input_fn)
+        
+        var = 'Tsoil'
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+        
+        
+        class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
+        
+        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
+        var = 'T2'
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
+        
+        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
+        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
+        
+        
+        class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
+        
+        
+        input_nc.close()
+        
+        
+    
+    #inputs.append(cp.deepcopy(class_settings))
+    #var = 'T2'
+    #valold = class_settings.__dict__[var]
+    #
+    #class_settings.__dict__[var] = 305.
+    #class_settings.__dict__['Tsoil'] = 302.
+    #valnew = class_settings.__dict__[var]
+    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+    
+    
+    
+    #inputs.append(cp.deepcopy(class_settings))
+    #
+    #var = 'Lambda'
+    #valold = class_settings.__dict__[var]
+    
+    ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book. 
+    ## I need to ask Chiel.
+    ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
+    #
+    #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg'] 
+    #class_settings.__dict__[var] = valnew
+    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
+    
+    
+    
+    key = "GLAS"
+    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
+       
+        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
+        print("Reading canopy height for determining roughness length from "+input_fn)
+        var = 'z0m'
+    
+        
+        #plt.plot
+        
+        input_nc = nc4.Dataset(input_fn,'r')
+        
+        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
+        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
+        
+        testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
+        
+        lowerlimit = 0.01
+        if testval < lowerlimit:
+            print('forest canopy height very very small. We take a value of '+str(lowerlimit))
+            class_settings.__dict__[var] = lowerlimit
+        else:
+            class_settings.__dict__[var] = testval
+        
+        class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
+        
+        
+        input_nc.close()
+        

From 54b4cc3e68a7ce66d52062af6d4d9a4f9b08ee4e Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 21 Aug 2018 21:54:14 +0200
Subject: [PATCH 008/129] restructure

---
 MANIFEST                                      |  14 +-
 build/lib/bin/__init__.py                     |   1 -
 build/lib/{lib => class4gl}/__init__.py       |   0
 build/lib/{lib => class4gl}/class4gl.py       |   0
 build/lib/{lib => class4gl}/data_air.py       |   0
 build/lib/{lib => class4gl}/data_global.py    |   0
 .../{lib => class4gl}/interface_functions.py  |   0
 .../lib/{lib => class4gl}/interface_multi.py  |   0
 build/lib/{lib => class4gl}/model.py          |   0
 class4gl/setup/batch_setup_global.py          |  42 +
 class4gl/setup/setup_bllast.py                | 719 +++++++++++++++++
 class4gl/setup/setup_global.py                | 310 ++++++++
 class4gl/setup/setup_goamazon.py              | 740 ++++++++++++++++++
 class4gl/setup/setup_humppa.py                | 732 +++++++++++++++++
 class4gl/setup/trash/setup_global_old.py      | 284 +++++++
 class4gl/simulations/batch_simulations.py     |  77 ++
 class4gl/simulations/runmodel.py              | 130 +++
 class4gl/simulations/simulations.py           | 260 ++++++
 class4gl/simulations/simulations_iter.py      | 364 +++++++++
 class4gl/simulations/simulations_iter_test.py | 367 +++++++++
 class4gl/simulations/trash/run_test.py        | 241 ++++++
 setup.py                                      |   2 +-
 22 files changed, 4274 insertions(+), 9 deletions(-)
 rename build/lib/{lib => class4gl}/__init__.py (100%)
 rename build/lib/{lib => class4gl}/class4gl.py (100%)
 rename build/lib/{lib => class4gl}/data_air.py (100%)
 rename build/lib/{lib => class4gl}/data_global.py (100%)
 rename build/lib/{lib => class4gl}/interface_functions.py (100%)
 rename build/lib/{lib => class4gl}/interface_multi.py (100%)
 rename build/lib/{lib => class4gl}/model.py (100%)
 create mode 100644 class4gl/setup/batch_setup_global.py
 create mode 100644 class4gl/setup/setup_bllast.py
 create mode 100644 class4gl/setup/setup_global.py
 create mode 100644 class4gl/setup/setup_goamazon.py
 create mode 100644 class4gl/setup/setup_humppa.py
 create mode 100644 class4gl/setup/trash/setup_global_old.py
 create mode 100644 class4gl/simulations/batch_simulations.py
 create mode 100644 class4gl/simulations/runmodel.py
 create mode 100644 class4gl/simulations/simulations.py
 create mode 100644 class4gl/simulations/simulations_iter.py
 create mode 100644 class4gl/simulations/simulations_iter_test.py
 create mode 100644 class4gl/simulations/trash/run_test.py

diff --git a/MANIFEST b/MANIFEST
index 534f2f4..1dde1bb 100644
--- a/MANIFEST
+++ b/MANIFEST
@@ -1,10 +1,10 @@
 # file GENERATED by distutils, do NOT edit
 setup.py
 bin/__init__.py
-lib/__init__.py
-lib/class4gl.py
-lib/data_air.py
-lib/data_global.py
-lib/interface_functions.py
-lib/interface_multi.py
-lib/model.py
+class4gl/__init__.py
+class4gl/class4gl.py
+class4gl/data_air.py
+class4gl/data_global.py
+class4gl/interface_functions.py
+class4gl/interface_multi.py
+class4gl/model.py
diff --git a/build/lib/bin/__init__.py b/build/lib/bin/__init__.py
index a21583b..58f6cca 100644
--- a/build/lib/bin/__init__.py
+++ b/build/lib/bin/__init__.py
@@ -1,4 +1,3 @@
-from . import model,class4gl,interface_multi,data_air,data_global
 
 __version__ = '0.1.0'
 
diff --git a/build/lib/lib/__init__.py b/build/lib/class4gl/__init__.py
similarity index 100%
rename from build/lib/lib/__init__.py
rename to build/lib/class4gl/__init__.py
diff --git a/build/lib/lib/class4gl.py b/build/lib/class4gl/class4gl.py
similarity index 100%
rename from build/lib/lib/class4gl.py
rename to build/lib/class4gl/class4gl.py
diff --git a/build/lib/lib/data_air.py b/build/lib/class4gl/data_air.py
similarity index 100%
rename from build/lib/lib/data_air.py
rename to build/lib/class4gl/data_air.py
diff --git a/build/lib/lib/data_global.py b/build/lib/class4gl/data_global.py
similarity index 100%
rename from build/lib/lib/data_global.py
rename to build/lib/class4gl/data_global.py
diff --git a/build/lib/lib/interface_functions.py b/build/lib/class4gl/interface_functions.py
similarity index 100%
rename from build/lib/lib/interface_functions.py
rename to build/lib/class4gl/interface_functions.py
diff --git a/build/lib/lib/interface_multi.py b/build/lib/class4gl/interface_multi.py
similarity index 100%
rename from build/lib/lib/interface_multi.py
rename to build/lib/class4gl/interface_multi.py
diff --git a/build/lib/lib/model.py b/build/lib/class4gl/model.py
similarity index 100%
rename from build/lib/lib/model.py
rename to build/lib/class4gl/model.py
diff --git a/class4gl/setup/batch_setup_global.py b/class4gl/setup/batch_setup_global.py
new file mode 100644
index 0000000..4a3f623
--- /dev/null
+++ b/class4gl/setup/batch_setup_global.py
@@ -0,0 +1,42 @@
+
+
+''' 
+Purpose: 
+    launch array job to get sounding and other global forcing data in class4gl input format"
+Usage:
+    python start_setup_global.py
+
+Author:
+    Hendrik Wouters 
+
+'''
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if sys.argv[1] == 'qsub':
+# with qsub
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = len(STNlist) 
+print(PROCS)
+BATCHSIZE = math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
+# elif sys.argv[1] == 'wsub':
+#     
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/setup/setup_bllast.py b/class4gl/setup/setup_bllast.py
new file mode 100644
index 0000000..af8c8bb
--- /dev/null
+++ b/class4gl/setup/setup_bllast.py
@@ -0,0 +1,719 @@
+# -*- coding: utf-8 -*-
+# Read data from BLLAST campaing and convert it to class4gl input
+
+# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
+#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 42.971834,
+                  "longitude" : 0.3671169,
+                  "name" : "the BLLAST experiment"
+                })
+current_station.name = 90001
+
+
+
+
+
+# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
+# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
+
+HOUR_FILES = \
+{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110619_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110620_1750_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110625_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110702_1655_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
+}
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
+                                     #widths=[14]*19,
+                                     #skiprows=9,
+                                     #skipfooter=15,
+                                     #decimal='.',
+                                     #header=None,
+                                     #names = columns,
+                                     #na_values='-----')
+        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
+        print(air_balloon_in.columns)
+        rowmatches = {
+            't':      lambda x: x['TaRad']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['Press']*100.,
+            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
+            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
+            'z':      lambda x: x['Altitude'] -582.,
+            # from virtual temperature to absolute humidity
+            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+        # filter data so that potential temperature always increases with
+        # height 
+        cols = []
+        for column in air_ap_tail.columns:
+            #if column != 'z':
+                cols.append(column)
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        # 
+        # # we copy the pressure at ground level from balloon sounding. The
+        # # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
+        # write local solar time, we need to assign the timezone to UTC (which
+        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
+        # tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise']+dt.timedelta(hours=2))\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        print('ldatetime_daylight',dpars['ldatetime_daylight'])
+        print('ldatetime',dpars['ldatetime'])
+        print('lSunrise',dpars['lSunrise'])
+        dpars['day'] = dpars['ldatetime'].day
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        print('tstart',dpars['tstart'])
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='bllast',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
+    
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+
+    
+    c4gli_morning.pars.sw_ac = []
+    c4gli_morning.pars.sw_ap = True
+    c4gli_morning.pars.sw_lit = False
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/setup_global.py b/class4gl/setup/setup_global.py
new file mode 100644
index 0000000..79224d9
--- /dev/null
+++ b/class4gl/setup/setup_global.py
@@ -0,0 +1,310 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+    one_run = False
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+                
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            try:
+                c4gli.get_profile_wyoming(wy_strm)
+                #print(STN['ID'],c4gli.pars.datetime)
+                #c4gli.get_global_input(globaldata)
+
+                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+                logic = dict()
+                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+                logic['daylight'] = \
+                    ((c4gli.pars.ldatetime_daylight - 
+                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
+                
+                logic['springsummer'] = (c4gli.pars.theta > 278.)
+                
+                # we take 3000 because previous analysis (ie., HUMPPA) has
+                # focussed towards such altitude
+                le3000 = (c4gli.air_balloon.z <= 3000.)
+                logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+
+                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+                
+                print('logic:', logic)
+                # the result
+                morning_ok = np.mean(list(logic.values()))
+                print(morning_ok,c4gli.pars.ldatetime)
+
+            except:
+                morning_ok =False
+                print('obtain morning not good')
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                print('MORNING OK!')
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                print('AFTERNOON PROFILE CLEARED')
+                try:
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+                    print('AFTERNOON PROFILE OK')
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # we will dump the latest afternoon sounding that fits the
+                    # minimum criteria specified by logic_afternoon
+                    print(current_date,current_date_afternoon)
+                    c4gli_afternoon_for_dump = None
+                    while ((current_date_afternoon == current_date) and \
+                           (wy_strm.current is not None)):
+                        logic_afternoon =dict()
+
+                        logic_afternoon['afternoon'] = \
+                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        logic_afternoon['daylight'] = \
+                          ((c4gli_afternoon.pars.ldatetime - \
+                            c4gli_afternoon.pars.ldatetime_daylight \
+                           ).total_seconds()/3600. <= 0.)
+
+
+                        le3000_afternoon = \
+                            (c4gli_afternoon.air_balloon.z <= 3000.)
+                        logic_afternoon['5measurements'] = \
+                            (np.sum(le3000_afternoon) >= 5) 
+
+                        # we only store the last afternoon sounding that fits these
+                        # minimum criteria
+
+                        afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                        print('logic_afternoon: ',logic_afternoon)
+                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                        if afternoon_ok == 1.:
+                            # # doesn't work :(
+                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                            
+                            # so we just create a new one from the same wyoming profile
+                            c4gli_afternoon_for_dump = class4gl_input()
+                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                        wy_strm.find_next()
+                        c4gli_afternoon.clear()
+                        c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                        if wy_strm.current is not None:
+                            current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                        else:
+                            # a dummy date: this will be ignored anyway
+                            current_date_afternoon = dt.date(1900,1,1)
+
+                        # Only in the case we have a good pair of soundings, we
+                        # dump them to disk
+                    if c4gli_afternoon_for_dump is not None:
+                        c4gli.update(source='pairs',pars={'runtime' : \
+                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                                 c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                        print('ALMOST...')
+                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                                
+        
+                            c4gli.get_global_input(globaldata)
+                            print('VERY CLOSE...')
+                            if c4gli.check_source_globaldata() and \
+                                (c4gli.check_source(source='wyoming',\
+                                                   check_only_sections='pars')):
+                                c4gli.dump(fileout)
+                                
+                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                
+                                
+                                # for keyEXP,dictEXP in experiments.items():
+                                #     
+                                #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                #     c4gl = class4gl(c4gli)
+                                #     # c4gl.run()
+                                #     
+                                #     c4gl.dump(c4glfiles[key])
+                                
+                                print('HIT!!!')
+                                one_run = True
+                except:
+                    print('get profile failed')
+                
+    if one_run:
+        STN.name = STN['ID']
+        all_records_morning = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='morning',
+                                      refetch_records=True,
+                                      )
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      odir,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    else:
+        os.system('rm '+fnout)
+        os.system('rm '+fnout_afternoon)
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
new file mode 100644
index 0000000..f9efe2c
--- /dev/null
+++ b/class4gl/setup/setup_goamazon.py
@@ -0,0 +1,740 @@
+# -*- coding: utf-8 -*-
+
+import xarray as xr
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+import glob
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : -3.21,
+                  "longitude" : -60.6,
+                  "name" : "the GOAMAZON experiment"
+                })
+current_station.name = 90002
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
+DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
+
+
+DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
+HOUR_FILES = {}
+for iDT, DT in enumerate(DTS):
+    morning_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
+    if len(possible_files)>0:
+        morning_file= possible_files[0]
+    afternoon_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
+    if len(possible_files)>0:
+        afternoon_file= possible_files[0]
+
+    if (morning_file is not None) and (afternoon_file is not None):
+        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
+                          'afternoon':[17.5,afternoon_file]}
+
+print(HOUR_FILES)
+
+# HOUR_FILES = \
+# {
+#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
+#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+# }
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
+        print(balloon_file)
+        
+        xrin = balloon_file
+        air_balloon = pd.DataFrame()
+
+        air_balloon['t'] = xrin.tdry.values+273.15
+        air_balloon['p'] = xrin.pres.values*100.
+        
+        air_balloon['u'] = xrin.u_wind.values
+        air_balloon['v'] = xrin.v_wind.values
+        air_balloon['WSPD'] = xrin['wspd'].values
+        
+        print(xrin.rh.values.shape)
+        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
+        
+
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        # air_balloon_in = pd.read_fwf(balloon_file,
+        #                              widths=[14]*19,
+        #                              skiprows=9,
+        #                              skipfooter=15,
+        #                              decimal=',',
+        #                              header=None,
+        #                              names = columns,
+        #                              na_values='-----')
+    
+
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
+            'rho': lambda x: x.p /x.t / x.R ,
+        }
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        print('alt in xrin?:','alt' in xrin)
+        if 'alt' in xrin:
+            air_balloon['z'] = xrin.alt.values
+        else:
+            air_balloon['z'] = 0.
+            for irow,row in air_balloon.iloc[1:].iterrows():
+                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
+                        
+             
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn =pair['morning'][1]
+    print(humpafn)
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn = pair['afternoon'][1]
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+"""
+stations_for_iter = stations(path_exp)
+for STNID,station in stations_iterator(stations_for_iter):
+    records_current_station_index = \
+            (records_ini.index.get_level_values('STNID') == STNID)
+    file_current_station_mod = STNID
+
+    with \
+    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        for (STNID,index),record_ini in records_iterator(records_ini):
+            c4gli_ini = get_record_yaml(file_station_ini, 
+                                        record_ini.index_start, 
+                                        record_ini.index_end,
+                                        mode='ini')
+            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+
+            record_mod = records_mod.loc[(STNID,index)]
+            c4gl_mod = get_record_yaml(file_station_mod, 
+                                        record_mod.index_start, 
+                                        record_mod.index_end,
+                                        mode='mod')
+            record_afternoon = records_afternoon.loc[(STNID,index)]
+            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+                                        record_afternoon.index_start, 
+                                        record_afternoon.index_end,
+                                        mode='ini')
+"""
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/setup_humppa.py b/class4gl/setup/setup_humppa.py
new file mode 100644
index 0000000..ff37628
--- /dev/null
+++ b/class4gl/setup/setup_humppa.py
@@ -0,0 +1,732 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 61.8448,
+                  "longitude" : 24.2882,
+                  "name" : "the HUMMPA experiment"
+                })
+current_station.name = 90000
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+
+HOUR_FILES = \
+{ dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
+  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[18,'humppa_071310_1800.txt']},
+  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
+  dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
+  dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
+  dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
+  dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
+  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[21,'humppa_071910_2100.txt']},
+#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
+  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
+  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[18,'humppa_072210_1800.txt']},
+ # something is wrong with ths profile
+ # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
+  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
+  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
+  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
+  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
+  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
+  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[18,'humppa_072910_1800.txt']},
+  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
+  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[15,'humppa_073110_1500.txt']},
+  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
+  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[18,'humppa_080210_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
+  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
+  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
+  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
+  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
+  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
+  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[18,'humppa_081010_1800.txt']},
+}
+
+
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_fwf(balloon_file,
+                                     widths=[14]*19,
+                                     skiprows=9,
+                                     skipfooter=15,
+                                     decimal=',',
+                                     header=None,
+                                     names = columns,
+                                     na_values='-----')
+    
+        rowmatches = {
+            't':      lambda x: x['T[C]']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['P[hPa]']*100.,
+            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'z':      lambda x: x['Altitude[m]'],
+            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/trash/setup_global_old.py b/class4gl/setup/trash/setup_global_old.py
new file mode 100644
index 0000000..d812684
--- /dev/null
+++ b/class4gl/setup/trash/setup_global_old.py
@@ -0,0 +1,284 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under odir+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+
+
+#calculate the root mean square error
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    
+    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+
+
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_csv(fn_stations)
+
+
+STNlist = list(df_stations.iterrows())
+NUMSTNS = len(STNlist)
+PROCS = 100
+BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+
+
+iPROC = int(sys.argv[1])
+
+
+for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings after 1981
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            c4gli.get_profile_wyoming(wy_strm)
+            #print(STN['ID'],c4gli.pars.datetime)
+            #c4gli.get_global_input(globaldata)
+
+            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+            logic = dict()
+            logic['morning'] =  (c4gli.pars.ldatetime.hour < 12.)
+            logic['daylight'] = \
+                ((c4gli.pars.ldatetime_daylight - 
+                  c4gli.pars.ldatetime).total_seconds()/3600. <= 5.)
+            
+            logic['springsummer'] = (c4gli.pars.theta > 278.)
+            
+            # we take 3000 because previous analysis (ie., HUMPPA) has
+            # focussed towards such altitude
+            le3000 = (c4gli.air_balloon.z <= 3000.)
+            logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+            try:
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta)) and \
+                        (rmse(c4gli.air_balloon.theta[leh] , \
+                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                              )
+    
+            except:
+                logic['mlerrlow'] = False
+                print('rmse probably failed')
+
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+            
+            print('logic:', logic)
+            # the result
+            morning_ok = np.mean(list(logic.values()))
+            print(morning_ok,c4gli.pars.ldatetime)
+            
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                if wy_strm.current is not None:
+                    current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                else:
+                    # a dummy date: this will be ignored anyway
+                    current_date_afternoon = dt.date(1900,1,1)
+
+                # we will dump the latest afternoon sounding that fits the
+                # minimum criteria specified by logic_afternoon
+                c4gli_afternoon_for_dump = None
+                while ((current_date_afternoon == current_date) and \
+                       (wy_strm.current is not None)):
+                    logic_afternoon =dict()
+
+                    logic_afternoon['afternoon'] = \
+                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                    logic_afternoon['daylight'] = \
+                      ((c4gli_afternoon.pars.ldatetime - \
+                        c4gli_afternoon.pars.ldatetime_daylight \
+                       ).total_seconds()/3600. <= 2.)
+
+
+                    le3000_afternoon = \
+                        (c4gli_afternoon.air_balloon.z <= 3000.)
+                    logic_afternoon['5measurements'] = \
+                        (np.sum(le3000_afternoon) >= 5) 
+
+                    # we only store the last afternoon sounding that fits these
+                    # minimum criteria
+
+                    afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                    print('logic_afternoon: ',logic_afternoon)
+                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                    if afternoon_ok == 1.:
+                        # # doesn't work :(
+                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                        
+                        # so we just create a new one from the same wyoming profile
+                        c4gli_afternoon_for_dump = class4gl_input()
+                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                    wy_strm.find_next()
+                    c4gli_afternoon.clear()
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # Only in the case we have a good pair of soundings, we
+                    # dump them to disk
+                if c4gli_afternoon_for_dump is not None:
+                    c4gli.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                             c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                    print('ALMOST...')
+                    if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+                            
+        
+                        c4gli.get_global_input(globaldata)
+                        print('VERY CLOSE...')
+                        if c4gli.check_source_globaldata() and \
+                            (c4gli.check_source(source='wyoming',\
+                                               check_only_sections='pars')):
+                            c4gli.dump(fileout)
+                            
+                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                            
+                            
+                            # for keyEXP,dictEXP in experiments.items():
+                            #     
+                            #     c4gli.update(source=keyEXP,pars = dictEXP)
+                            #     c4gl = class4gl(c4gli)
+                            #     # c4gl.run()
+                            #     
+                            #     c4gl.dump(c4glfiles[key])
+                            
+                            print('HIT!!!')
+                
+                
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
new file mode 100644
index 0000000..b5d4cc3
--- /dev/null
+++ b/class4gl/simulations/batch_simulations.py
@@ -0,0 +1,77 @@
+
+import argparse
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if 'path-soundings' in args.__dict__.keys():
+#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+# else:
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
+    parser.add_argument('--exec')
+    parser.add_argument('--experiments')#should be ';'-seperated list
+    parser.add_argument('--split-by',default=-1)
+    args = parser.parse_args()
+
+experiments = args.experiments.split(';')
+#SET = 'GLOBAL'
+SET = args.dataset
+print(args.experiments)
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+for expname in experiments:
+    #exp = EXP_DEFS[expname]
+    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+    os.system('rm -R '+path_exp)
+
+totalchunks = 0
+for istation,current_station in all_stations.iterrows():
+    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
+    totalchunks +=chunks_current_station
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
+                                       ',split_by='+str(args.split_by)+\
+                                       ',exec='+str(args.exec)+\
+                                       ',experiments='+str(args.experiments))
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/simulations/runmodel.py b/class4gl/simulations/runmodel.py
new file mode 100644
index 0000000..fc4fd19
--- /dev/null
+++ b/class4gl/simulations/runmodel.py
@@ -0,0 +1,130 @@
+#
+# Example of how to run the Python code, and access the output
+# This case is identical to the default setup of CLASS (the version with interface) 
+#
+
+from pylab import *
+from model import *
+
+""" 
+Create empty model_input and set up case
+"""
+run1input = model_input()
+
+run1input.dt         = 60.       # time step [s]
+run1input.runtime    = 12*3600    # total run time [s]
+
+# mixed-layer input
+run1input.sw_ml      = True      # mixed-layer model switch
+run1input.sw_shearwe = False     # shear growth mixed-layer switch
+run1input.sw_fixft   = False     # Fix the free-troposphere switch
+run1input.h          = 200.      # initial ABL height [m]
+run1input.Ps         = 101300.   # surface pressure [Pa]
+run1input.divU       = 0.        # horizontal large-scale divergence of wind [s-1]
+run1input.fc         = 1.e-4     # Coriolis parameter [m s-1]
+
+run1input.theta      = 288.      # initial mixed-layer potential temperature [K]
+run1input.dtheta     = 1.        # initial temperature jump at h [K]
+run1input.gammatheta = 0.006     # free atmosphere potential temperature lapse rate [K m-1]
+run1input.advtheta   = 0.        # advection of heat [K s-1]
+run1input.beta       = 0.2       # entrainment ratio for virtual heat [-]
+run1input.wtheta     = 0.1       # surface kinematic heat flux [K m s-1]
+
+run1input.q          = 0.008     # initial mixed-layer specific humidity [kg kg-1]
+run1input.dq         = -0.001    # initial specific humidity jump at h [kg kg-1]
+run1input.gammaq     = 0.        # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
+run1input.advq       = 0.        # advection of moisture [kg kg-1 s-1]
+run1input.wq         = 0.1e-3    # surface kinematic moisture flux [kg kg-1 m s-1]
+
+run1input.CO2        = 422.      # initial mixed-layer CO2 [ppm]
+run1input.dCO2       = -44.      # initial CO2 jump at h [ppm]
+run1input.gammaCO2   = 0.        # free atmosphere CO2 lapse rate [ppm m-1]
+run1input.advCO2     = 0.        # advection of CO2 [ppm s-1]
+run1input.wCO2       = 0.        # surface kinematic CO2 flux [ppm m s-1]
+
+run1input.sw_wind    = False     # prognostic wind switch
+run1input.u          = 6.        # initial mixed-layer u-wind speed [m s-1]
+run1input.du         = 4.        # initial u-wind jump at h [m s-1]
+run1input.gammau     = 0.        # free atmosphere u-wind speed lapse rate [s-1]
+run1input.advu       = 0.        # advection of u-wind [m s-2]
+
+run1input.v          = -4.0      # initial mixed-layer u-wind speed [m s-1]
+run1input.dv         = 4.0       # initial u-wind jump at h [m s-1]
+run1input.gammav     = 0.        # free atmosphere v-wind speed lapse rate [s-1]
+run1input.advv       = 0.        # advection of v-wind [m s-2]
+
+run1input.sw_sl      = False     # surface layer switch
+run1input.ustar      = 0.3       # surface friction velocity [m s-1]
+run1input.z0m        = 0.02      # roughness length for momentum [m]
+run1input.z0h        = 0.002     # roughness length for scalars [m]
+
+run1input.sw_rad     = False     # radiation switch
+run1input.lat        = 51.97     # latitude [deg]
+run1input.lon        = -4.93     # longitude [deg]
+run1input.doy        = 268.      # day of the year [-]
+run1input.tstart     = 6.8       # time of the day [h UTC]
+run1input.cc         = 0.0       # cloud cover fraction [-]
+run1input.Q          = 400.      # net radiation [W m-2] 
+run1input.dFz        = 0.        # cloud top radiative divergence [W m-2] 
+
+run1input.sw_ls      = False     # land surface switch
+run1input.ls_type    = 'js'      # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
+run1input.wg         = 0.21      # volumetric water content top soil layer [m3 m-3]
+run1input.w2         = 0.21      # volumetric water content deeper soil layer [m3 m-3]
+run1input.cveg       = 0.85      # vegetation fraction [-]
+run1input.Tsoil      = 285.      # temperature top soil layer [K]
+run1input.T2         = 286.      # temperature deeper soil layer [K]
+run1input.a          = 0.219     # Clapp and Hornberger retention curve parameter a
+run1input.b          = 4.90      # Clapp and Hornberger retention curve parameter b
+run1input.p          = 4.        # Clapp and Hornberger retention curve parameter c
+run1input.CGsat      = 3.56e-6   # saturated soil conductivity for heat
+
+run1input.wsat       = 0.472     # saturated volumetric water content ECMWF config [-]
+run1input.wfc        = 0.323     # volumetric water content field capacity [-]
+run1input.wwilt      = 0.171     # volumetric water content wilting point [-]
+
+run1input.C1sat      = 0.132     
+run1input.C2ref      = 1.8
+
+run1input.LAI        = 2.        # leaf area index [-]
+run1input.gD         = 0.0       # correction factor transpiration for VPD [-]
+run1input.rsmin      = 110.      # minimum resistance transpiration [s m-1]
+run1input.rssoilmin  = 50.       # minimun resistance soil evaporation [s m-1]
+run1input.alpha      = 0.25      # surface albedo [-]
+
+run1input.Ts         = 290.      # initial surface temperature [K]
+
+run1input.Wmax       = 0.0002    # thickness of water layer on wet vegetation [m]
+run1input.Wl         = 0.0000    # equivalent water layer depth for wet vegetation [m]
+
+run1input.Lambda     = 5.9       # thermal diffusivity skin layer [-]
+
+run1input.c3c4       = 'c3'      # Plant type ('c3' or 'c4')
+
+run1input.sw_cu      = False     # Cumulus parameterization switch
+run1input.dz_h       = 150.      # Transition layer thickness [m]
+
+"""
+Init and run the model
+"""
+r1 = model(run1input)
+r1.run()
+
+"""
+Plot output
+"""
+figure()
+subplot(131)
+plot(r1.out.t, r1.out.h)
+xlabel('time [h]')
+ylabel('h [m]')
+
+subplot(132)
+plot(r1.out.t, r1.out.theta)
+xlabel('time [h]')
+ylabel('theta [K]')
+
+subplot(133)
+plot(r1.out.t, r1.out.q*1000.)
+xlabel('time [h]')
+ylabel('q [g kg-1]')
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
new file mode 100644
index 0000000..719f9a5
--- /dev/null
+++ b/class4gl/simulations/simulations.py
@@ -0,0 +1,260 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--first-station-row')
+parser.add_argument('--last-station-row')
+parser.add_argument('--station-id') # run a specific station id
+parser.add_argument('--dataset')
+parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--error-handling',default='dump_on_success')
+parser.add_argument('--experiments')
+parser.add_argument('--split-by',default=-1)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+path_soundingsSET = args.path_soundings+'/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iter = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iter.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if args.station_id is not None:
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iterator.set_STNID(STNID)
+        run_stations = pd.DataFrame(run_station)
+    else:
+        run_stations = pd.DataFrame(all_stations)
+        if args.last_station_row is not None:
+            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            run_stations = run_stations.iloc[int(args.first_station):]
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_succes':
+                        try:
+                            c4gl.run()
+                            print('run not succesfull')
+                            onerun = True
+
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
new file mode 100644
index 0000000..5dfbaff
--- /dev/null
+++ b/class4gl/simulations/simulations_iter.py
@@ -0,0 +1,364 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk',default=0)
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if 'global_chunk' in args.__dict__.keys():
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if 'last_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    
+    if 'first_station' in args.__dict__.keys():
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    if 'station_chunk' in args.__dict__.keys():
+        run_station_chunk = args.station_chunk
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+    for istation,current_station in run_stations.iterrows():
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                #if iexp == 11:
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+
+                        c4gli_morning.pars.itersteps = i
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                   #   timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    except:
+                        print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/class4gl/simulations/simulations_iter_test.py b/class4gl/simulations/simulations_iter_test.py
new file mode 100644
index 0000000..eefd475
--- /dev/null
+++ b/class4gl/simulations/simulations_iter_test.py
@@ -0,0 +1,367 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    args = parser.parse_args()
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+                #if iexp == 11:
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+                        onerun = True
+
+                        c4gli_morning.pars.itersteps = i
+                    except:
+                        print('run not succesfull')
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                                  include_input=False,\
+                               #   timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/class4gl/simulations/trash/run_test.py b/class4gl/simulations/trash/run_test.py
new file mode 100644
index 0000000..767d960
--- /dev/null
+++ b/class4gl/simulations/trash/run_test.py
@@ -0,0 +1,241 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--global-chunk')
+    parser.add_argument('--first-station')
+    parser.add_argument('--last-station')
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    parser.add_argument('--experiments')
+    parser.add_argument('--split-by',default=-1)# station soundings are split
+                                                # up in chunks
+    parser.add_argument('--station-chunk')
+    parser.add_argument('--c4gl-path',default='')
+    args = parser.parse_args()
+
+if args.c4gl_path == '': 
+    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+else:
+    sys.path.insert(0, args.c4gl_path)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+#SET = 'GLOBAL'
+SET = args.dataset
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+
+all_records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+if args.global_chunk is not None:
+    totalchunks = 0
+    stations_iterator = all_stations.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iterator.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    run_stations = pd.DataFrame(all_stations)
+    if args.last_station is not None:
+        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
+    if args.first_station is not None:
+        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
+    run_station_chunk = 0
+    if args.station_chunk is not None:
+        run_station_chunk = args.station_chunk
+
+#print(all_stations)
+print(run_stations)
+print(args.__dict__.keys())
+records_morning = get_records(run_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                path_soundingsSET,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            print(records_morning_station_chunk)
+
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    try:
+                        c4gl.run()
+                    except:
+                        print('run not succesfull')
+                    onerun = True
+
+                    c4gli_morning.dump(file_ini)
+                    
+                    
+                    c4gl.dump(file_mod,\
+                              include_input=False,\
+                              #timeseries_only=timeseries_only,\
+                             )
+                    onerun = True
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/setup.py b/setup.py
index a806fa0..4dcb51d 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
         name='class4gl',
         version='0.1dev',
-        packages=['lib','bin'],
+        packages=['class4gl','bin'],
         license='GPLv3 licence',
         long_description=open('README.md').read(),
 )

From 919a737f8fb12a261e8ebe511d45dbcf91847b11 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 21 Aug 2018 22:00:36 +0200
Subject: [PATCH 009/129] Delete class4gl.py

---
 class4gl.py | 1611 ---------------------------------------------------
 1 file changed, 1611 deletions(-)
 delete mode 100644 class4gl.py

diff --git a/class4gl.py b/class4gl.py
deleted file mode 100644
index 7baaa51..0000000
--- a/class4gl.py
+++ /dev/null
@@ -1,1611 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-
-Created on Mon Jan 29 12:33:51 2018
-
-Module file for class4gl, which  extents the class-model to be able to take
-global air profiles as input. It exists of:
-
-CLASSES:
-    - an input object, namely class4gl_input. It includes:
-        - a function to read Wyoming sounding data from a yyoming stream object
-        - a function to read global data from a globaldata library object 
-    - the model object: class4gl
-    - ....    
-
-DEPENDENCIES:
-    - xarray
-    - numpy
-    - data_global
-    - Pysolar
-    - yaml
-
-@author: Hendrik Wouters
-
-"""
-
-
-
-""" Setup of envirnoment """
-
-# Standard modules of the stand class-boundary-layer model
-from model import model
-from model import model_output as class4gl_output
-from model import model_input
-from model import qsat
-#from data_soundings import wyoming 
-import Pysolar
-import yaml
-import logging
-import warnings
-import pytz
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-
-# Generic Python Packages
-import numpy as np
-import datetime as dt
-import pandas as pd
-import xarray as xr
-import io
-#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
-from data_global import data_global
-grav = 9.81
-
-# this is just a generic input object
-class generic_input(object):
-    def __init__(self):
-        self.init = True
-
-
-# all units from all variables in CLASS(4GL) should be defined here!
-units = {
-         'h':'m',
-         'theta':'K', 
-         'q':'kg/kg',
-         'cc': '-',
-         'cveg': '-',
-         'wg': 'm3 m-3',
-         'w2': 'm3 m-3',
-         #'wg': 'kg/kg',
-         'Tsoil': 'K',
-         'T2': 'K',
-         'z0m': 'm',
-         'alpha': '-',
-         'LAI': '-',
-         'dhdt':'m/h',
-         'dthetadt':'K/h',
-         'dqdt':'kg/kg/h',
-         'BR': '-',
-         'EF': '-',
-}
-
-class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
-
-    def __init__(self,set_pars_defaults=True,debug_level=None):
-
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        print('hello')
-        self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
-     
-        # # add ch to logger
-        # self.logger.addHandler(ch)
-
-        # """ end set up logger """
-
-
-
-        # these are the standard model input single-value parameters for class
-        self.pars = model_input()
-
-        # diagnostic parameters of the initial profile
-        self.diag = dict()
-
-        # In this variable, we keep track of the different parameters from where it originates from. 
-        self.sources = {}
-
-        if set_pars_defaults:
-            self.set_pars_defaults()
-
-    def set_pars_defaults(self):
-
-        """ 
-        Create empty model_input and set up case
-        """
-        defaults = dict( 
-        dt         = 60.    , # time step [s] 
-        runtime    = 6*3600 ,  # total run time [s]
-        
-        # mixed-layer input
-        sw_ml      = True   ,  # mixed-layer model switch
-        sw_shearwe = False  ,  # shear growth mixed-layer switch
-        sw_fixft   = False  ,  # Fix the free-troposphere switch
-        h          = 200.   ,  # initial ABL height [m]
-        Ps         = 101300.,  # surface pressure [Pa]
-        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
-        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
-        
-        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
-        dtheta     = 1.     ,  # initial temperature jump at h [K]
-        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
-        advtheta   = 0.     ,  # advection of heat [K s-1]
-        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
-        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
-        
-        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
-        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
-        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
-        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
-        
-        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
-        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
-        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
-        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
-        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
-        sw_wind    = True  ,  # prognostic wind switch
-        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
-        du         = 0.     ,  # initial u-wind jump at h [m s-1]
-        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
-        advu       = 0.     ,  # advection of u-wind [m s-2]
-        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
-        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
-        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
-        advv       = 0.     ,  # advection of v-wind [m s-2]
-        sw_sl      = True   , # surface layer switch
-        ustar      = 0.3    ,  # surface friction velocity [m s-1]
-        z0m        = 0.02   ,  # roughness length for momentum [m]
-        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
-        sw_rad     = True   , # radiation switch
-        lat        = 51.97  ,  # latitude [deg]
-        lon        = -4.93  ,  # longitude [deg]
-        doy        = 268.   ,  # day of the year [-]
-        tstart     = 6.8    ,  # time of the day [h UTC]
-        cc         = 0.0    ,  # cloud cover fraction [-]
-        Q          = 400.   ,  # net radiation [W m-2] 
-        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
-        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
-        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
-        cveg       = 0.85   ,  # vegetation fraction [-]
-        Tsoil      = 295.   ,  # temperature top soil layer [K]
-        Ts         = 295.   ,    # initial surface temperature [K]
-        T2         = 296.   ,  # temperature deeper soil layer [K]
-        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
-        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
-        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
-        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
-        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
-        wfc        = 0.323  ,  # volumetric water content field capacity [-]
-        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
-        C1sat      = 0.132  ,  
-        C2ref      = 1.8    ,
-        LAI        = 2.     ,  # leaf area index [-]
-        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
-        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
-        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
-        alpha      = 0.25   ,  # surface albedo [-]
-        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
-        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
-        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
-        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
-        sw_cu      = False  ,  # Cumulus parameterization switch
-        dz_h       = 150.   ,  # Transition layer thickness [m]
-        cala       = None   ,  # soil heat conductivity [W/(K*m)]
-        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
-        sw_ls      = True   ,
-        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
-        sw_lit     = False,
-        )
-        pars = model_input()
-        for key in defaults:
-            pars.__dict__[key] = defaults[key]
-        
-        self.update(source='defaults',pars=pars)
-        
-    def clear(self):
-        """ this procudure clears the class4gl_input """
-
-        for key in list(self.__dict__.keys()):
-            del(self.__dict__[key])
-        self.__init__()
-
-    def dump(self,file):
-        """ this procedure dumps the class4gl_input object into a yaml file
-            
-            Input: 
-                - self.__dict__ (internal): the dictionary from which we read 
-            Output:
-                - file: All the parameters in self.__init__() are written to
-                the yaml file, including pars, air_ap, sources etc.
-        """
-        file.write('---\n')
-        index = file.tell()
-        file.write('# CLASS4GL input; format version: 0.1\n')
-
-        # write out the position of the current record
-        yaml.dump({'index':index}, file, default_flow_style=False)
-
-        # we do not include the none values
-        for key,data in self.__dict__.items():
-            #if ((type(data) == model_input) or (type(class4gl_input):
-            if key == 'pars':
-
-                pars = {'pars' : self.__dict__['pars'].__dict__}
-                parsout = {}
-                for key in pars.keys():
-                    if pars[key] is not None:
-                        parsout[key] = pars[key]
-
-                yaml.dump(parsout, file, default_flow_style=False)
-            elif type(data) == dict:
-                if key == 'sources':
-                    # in case of sources, we want to have a
-                    # condensed list format as well, so we leave out
-                    # 'default_flow_style=False'
-                    yaml.dump({key : data}, file)
-                else: 
-                    yaml.dump({key : data}, file,
-                              default_flow_style=False)
-            elif type(data) == pd.DataFrame:
-                # in case of dataframes (for profiles), we want to have a
-                # condensed list format as well, so we leave out
-                # 'default_flow_style=False'
-                yaml.dump({key: data.to_dict(orient='list')},file)
-
-                # # these are trials to get it into a more human-readable
-                # fixed-width format, but it is too complex
-                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
-                #file.write(stream)
-                
-                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
-                #file.write(key+': !!str |\n')
-                #file.write(str(data)+'\n')
-       
-    def load_yaml_dict(self,yaml_dict,reset=True):
-        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
-            
-            Input: 
-                - yaml_dict: the dictionary from which we read 
-                - reset: reset data before reading        
-            Output:
-                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
-        """
-        
-        if reset:
-            for key in list(self.__dict__.keys()):
-                del(self.__dict__[key])
-            self.__init__()
-
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                self.__dict__[key] = model_input()
-                self.__dict__[key].__dict__ = data
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            elif key == 'sources':
-                self.__dict__[key] = data
-            elif key == 'diag':
-                self.__dict__[key] = data
-            else: 
-                warnings.warn("Key '"+key+"' may not be implemented.")
-                self.__dict__[key] = data
-
-    def update(self,source,**kwargs):
-        """ this procedure is to make updates of input parameters and tracking
-        of their source more convenient. It implements the assignment of
-        parameter source/sensitivity experiment IDs ('eg.,
-        'defaults', 'sounding balloon', any satellite information, climate
-        models, sensitivity tests etc.). These are all stored in a convenient
-        way with as class4gl_input.sources.  This way, the user can always consult with
-        from where parameters data originates from.  
-        
-        Input:
-            - source:    name of the underlying dataset
-            - **kwargs: a dictionary of data input, for which the key values
-            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
-            the values is a again a dictionary/dataframe of datakeys/columns
-            ('wg','PRES','datetime', ...) and datavalues (either single values,
-            profiles ...), eg., 
-
-                pars = {'wg': 0.007  , 'w2', 0.005}
-                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
-                                     300.,...]}
-            
-        Output:
-            - self.__dict__[datatype] : object to which the parameters are
-                                        assigned. They can be consulted with
-                                        self.pars, self.profiles, etc.
-                                        
-            - self.sources[source] : It supplements the overview overview of
-                                     data sources can be consulted with
-                                     self.sources. The structure is as follows:
-                                     as:
-                self.sources = { 
-                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
-                'GLEAM' :  ['pars:wg','pars:w2', ...],
-                 ...
-                }
-        
-        """
-
-        #print(source,kwargs)
-
-        for key,data in kwargs.items():
-
-            #print(key)
-            # if the key is not in class4gl_input object, then just add it. In
-            # that case, the update procedures below will just overwrite it 
-            if key not in self.__dict__:
-                self.__dict__[key] = data
-
-
-            
-
-            #... we do an additional check to see whether there is a type
-            # match. I not then raise a key error
-            if (type(data) != type(self.__dict__[key]) \
-                # we allow dict input for model_input pars
-                and not ((key == 'pars') and (type(data) == dict) and \
-                (type(self.__dict__[key]) == model_input))):
-
-                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
-
-
-            # This variable keeps track of the added data that is supplemented
-            # by the current source. We add this to class4gl_input.sources
-            datakeys = []
-
-            #... and we update the class4gl_input data, and this depends on the
-            # data type
-
-            if type(self.__dict__[key]) == pd.DataFrame:
-                # If the data type is a dataframe, then we update the columns
-                for column in list(data.columns):
-                    #print(column)
-                    self.__dict__[key][column] = data[column]
-                    datakeys.append(column)
-                    
-
-            elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
-                # dictionary of parameters
-                if type(data) == model_input:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data.__dict__}
-                    datakeys = list(data.__dict__.keys())
-                elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
-                    datakeys = list(data.keys())
-                else:
-                    raise TypeError('input key '+key+' is not of the same type\
-                                    as the one in the class4gl_object')
-
-            elif type(self.__dict__[key]) == dict:
-                # if the data type is a dictionary, we update the
-                # dictionary 
-                self.__dict__[key] = {self.__dict__[key] , data}
-                datakeys = list(data.keys())
-
-
-            # if source entry is not existing yet, we add it
-            if source not in self.sources.keys():
-                self.sources[source] = []
-
-
-            # self.logger.debug('updating section "'+\
-            #                  key+' ('+' '.join(datakeys)+')'\
-            #                  '" from source \
-            #                  "'+source+'"')
-
-            # Update the source dictionary: add the provided data keys to the
-            # specified source list
-            for datakey in datakeys:
-                # At first, remove the occurences of the keys in the other
-                # source lists
-                for sourcekey,sourcelist in self.sources.items():
-                    if key+':'+datakey in sourcelist:
-                        self.sources[sourcekey].remove(key+':'+datakey)
-                # Afterwards, add it to the current source list
-                self.sources[source].append(key+':'+datakey)
-
-
-        # # in case the datatype is a class4gl_input_pars, we update its keys
-        # # according to **kwargs dictionary
-        # if type(self.__dict__[datatype]) == class4gl_input_pars:
-        #     # add the data parameters to the datatype object dictionary of the
-        #     # datatype
-        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
-        #                                        **kwargs}
-        # # in case, the datatype reflects a dataframe, we update the columns according
-        # # to the *args list
-        # elif type(self.__dict__[datatype]) == pd.DataFrame:
-        #     for dataframe in args:
-        #         for column in list(dataframe.columns):
-        #             self.__dict__[datatype][column] = dataframe[column]
-        
-
-    def get_profile(self,IOBJ, *args, **argv):
-        # if type(IOBJ) == wyoming:
-        self.get_profile_wyoming(IOBJ,*args,**argv)
-        # else:
-        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
-        
-    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
-        """ 
-            Purpose: 
-                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
-
-            Input:
-                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
-                function will take the profile at the stream's current
-                position. 
-                2. air_ap_mode: which air profile do we take? 
-                    - b : best
-                    - l : according to lower limit for the mixed-layer height
-                            estimate
-                    - u : according to upper limit for the mixed-layer height
-                            estimate
-
-
-            Output:
-                1. all single-value parameters are stored in the
-                   class4gl_input.pars object
-                2. the souding profiles are stored in the in the
-                   class4gl_input.air_balloon dataframe
-                3. modified sounding profiles for which the mixed layer height
-                   is fitted
-                4. ...
-
-        """
-
-
-        # Raise an error in case the input stream is not the correct object
-        # if type(wy_strm) is not wyoming:
-        #    raise TypeError('Not a wyoming type input stream')
-
-        # Let's tell the class_input object that it is a Wyoming fit type
-        self.air_ap_type = 'wyoming'
-        # ... and which mode of fitting we apply
-        self.air_ap_mode = air_ap_mode
-
-        """ Temporary variables used for output """
-        # single value parameters derived from the sounding profile
-        dpars = dict()
-        # profile values
-        air_balloon = pd.DataFrame()
-        # fitted profile values
-        air_ap = pd.DataFrame()
-        
-        string = wy_strm.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = wy_strm.current.find_next('pre').find_next('pre').text
-        
-        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
-        dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
-               }
-        
-        # we get weird output when it's a numpy Timestamp, so we convert it to
-        # pd.datetime type
-
-        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
-        dpars['STNID'] = dpars['Station number']
-
-        # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
-        # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
-                              / \
-                             (air_balloon.MIXR/1000.+1.)
-        # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-        
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
-
-        
-
-        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-
-        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
-
-
-        # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        #is_valid = (air_balloon.z >= 0)
-        # # this is an alternative pipe/numpy method
-        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
-
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
-
-        if len(valid_indices) > 0:
-            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            
-            # the final mixed-layer height that will be used by class. We round it
-            # to 1 decimal so that we get a clean yaml output format
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
-            dpars['Ps'] = np.nan
-
-
-
-
-        if ~np.isnan(dpars['h']):
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u 
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-            
-
-
-
-        # First 3 data points of the mixed-layer fit. We create a empty head
-        # first
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-        
-        #calculate mixed-layer jump ( this should be larger than 0.1)
-        
-        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
-        
-        # make a row object for defining the jump
-        jump = air_ap_head.iloc[0] * np.nan
-            
-        if air_ap_tail.shape[0] > 1:
-
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = dpars['theta']
-        z_low =     dpars['h']
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                (z_mean > (z_low+10.)) and \
-                (theta_mean > (theta_low+0.2) ) and \
-                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-
-
-
-
-
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        #print(air_ap['PRES'].iloc[0])
-
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-
-        
-        dpars['lat'] = dpars['Station latitude']
-        dpars['latitude'] = dpars['lat']
-        
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        dpars['longitude'] = dpars['Station longitude']
-        
-        dpars['ldatetime'] = dpars['datetime'] \
-                            + \
-                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-
-        # # we make a pars object that is similar to the destination object
-        # pars = model_input()
-        # for key,value in dpars.items():
-        #     pars.__dict__[key] = value
-
-
-        # we round the columns to a specified decimal, so that we get a clean
-        # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
-# 
-        for column,decimal in decimals.items():
-            air_balloon[column] = air_balloon[column].round(decimal)
-            air_ap[column] = air_ap[column].round(decimal)
-
-        self.update(source='wyoming',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-
-        
-    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
-    
-        """
-        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
-                 according to the position (lat lon) and the class datetime and timespan
-                 globaldata should be a globaldata multifile object
-        
-        Input: 
-            - globaldata: this is the library object
-            - only_keys: only extract specified keys
-            - exclude_keys: do not inherit specified keys
-        """
-        classdatetime      = np.datetime64(self.pars.datetime_daylight)
-        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
-                                           + \
-                                           dt.timedelta(seconds=self.pars.runtime)\
-                                          )
-
-
-        # # list of variables that we get from global ground data
-        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
-        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
-        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
-        #                 'texture', 'itex', 'isoil', 'BR',
-        #                 'b', 'cveg',
-        #                 'C1sat', 
-        #                 'C2ref', 'p', 'a',
-        #                 ] #globaldata.datasets.keys():
-
-        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
-        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
-
-
-        if type(globaldata) is not data_global:
-            raise TypeError("Wrong type of input library") 
-
-        # by default, we get all dataset keys
-        keys = list(globaldata.datasets.keys())
-
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
-
-        # # In case there is surface pressure, we also calculate the half-level
-        # # and full-level pressure fields
-        # if ('sp' in keys):
-        #     keys.append('pfull')
-        #     keys.append('phalf')
-
-        # If specified, we only take the keys that are in only_keys
-        if only_keys is not None:
-            for key in keys:
-                if key not in only_keys:
-                    keys.remove(key)
-                
-        # If specified, we take out keys that are in exclude keys
-        if exclude_keys is not None:
-            for key in keys:
-                if key in exclude_keys:
-                    keys.remove(key)
-
-        # we set everything to nan first in the pars section (non-profile parameters
-        # without lev argument), so that we can check afterwards whether the
-        # data is well-fetched or not.
-        for key in keys:
-            if not ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None) and \
-                ('lev' in globaldata.datasets[key].page[key].dims)):
-                self.update(source='globaldata',pars={key:np.nan})
-            # # we do not check profile input for now. We assume it is
-            # # available
-            #else:
-            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
-
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
-
-        for key in keys:
-            # If we find it, then we obtain the variables
-            if ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None)):
-
-                # check first whether the dataset has a height coordinate (3d space)
-                if 'lev' in globaldata.datasets[key].page[key].dims:
-
-                    # first, we browse to the correct file that has the current time
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-                        globaldata.datasets[key].browse_page(time=classdatetime)
-
-                    
-                    if (globaldata.datasets[key].page is not None):
-                        # find longitude and latitude coordinates
-                        ilats = (np.abs(globaldata.datasets[key].page.lat -
-                                        self.pars.latitude) < 0.5)
-                        ilons = (np.abs(globaldata.datasets[key].page.lon -
-                                        self.pars.longitude) < 0.5)
-                        
-                        # if we have a time dimension, then we look up the required timesteps during the class simulation
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
-
-                            # In case we didn't find any correct time, we take the
-                            # closest one.
-                            if np.sum(itimes) == 0.:
-
-
-                                classdatetimemean = \
-                                    np.datetime64(self.pars.datetime_daylight + \
-                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
-                                                ))
-
-                                dstimes = globaldata.datasets[key].page.time
-                                time = dstimes.sel(time=classdatetimemean,method='nearest')
-                                itimes = (globaldata.datasets[key].page.time ==
-                                          time)
-                                
-                        else:
-                            # we don't have a time coordinate so it doesn't matter
-                            # what itimes is
-                            itimes = 0
-
-                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
-
-                        # over which dimensions we take a mean:
-                        dims = globaldata.datasets[key].page[key].dims
-                        namesmean = list(dims)
-                        namesmean.remove('lev')
-                        idxmean = [dims.index(namemean) for namemean in namesmean]
-                        
-                        value = \
-                        globaldata.datasets[key].page[key].isel(time=itimes,
-                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
-
-                        # Ideally, source should be equal to the datakey of globaldata.library 
-                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
-                        #  but therefore the globaldata class requires a revision to make this work
-                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
-
-                else:
-                    # this procedure is for reading the ground fields (2d space). 
-                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-    
-                       # first, we browse to the correct file
-                       #print(key)
-                       globaldata.datasets[key].browse_page(time=classdatetime)
-    
-                    if globaldata.datasets[key].page is not None:
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - self.pars.latitude))
-                        ilat = np.where((DIST) == np.min(DIST))[0][0]
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - self.pars.longitude))
-                        ilon = np.where((DIST) == np.min(DIST))[0][0]
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - (self.pars.latitude + 0.5)))
-                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmax = ilat
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - (self.pars.longitude  + 0.5)))
-                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmax = ilon
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lat.values\
-                                - (self.pars.latitude - 0.5)))
-                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmin = ilat
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lon.values\
-                                - (self.pars.longitude  - 0.5)))
-                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmin = ilon        
-                        
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
-                        else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
-                            
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                            
-                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
-                            
-                            classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
-                                                             dt.timedelta(seconds=self.pars.runtime)\
-                                                            ) 
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
-                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
-                                idatetimeend -= 1
-                            idatetime = np.min((idatetime,idatetimeend))
-                            #for gleam, we take the previous day values
-                            if key in ['wg', 'w2']:
-                                idatetime = idatetime - 1
-                                idatetimeend = idatetimeend - 1
-
-                            # in case of soil temperature, we take the exact
-                            # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
-                                idatetimeend = idatetime
-                            
-                            idts = range(idatetime,idatetimeend+1)
-                            
-                            count = 0
-                            self.__dict__[key] = 0.
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    for iidts in idts:
-                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
-                                        count += 1
-                            value = value/count
-                            self.update(source='globaldata',pars={key:value.item()})
-                                
-                        else:
-                                
-                            count = 0
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
-                                    count += 1
-                            value = value/count                        
-
-                            self.update(source='globaldata',pars={key:value.item()})
-
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            self.logger.debug('also update LAI based on LAIpixel and cveg') 
-            # I suppose LAI pixel is already determined in the previous
-            # procedure. Anyway...
-            key = 'LAIpixel'
-
-            if globaldata.datasets[key].page is not None:
-                # first, we browse to the correct file that has the current time
-                if 'time' in list(globaldata.datasets[key].page[key].dims):
-                    globaldata.datasets[key].browse_page(time=classdatetime)
-            
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - self.pars.latitude))
-                ilat = np.where((DIST) == np.min(DIST))[0][0]
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - self.pars.longitude))
-                ilon = np.where((DIST) == np.min(DIST))[0][0]
-                 
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude + 0.5)))
-                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmax = ilat
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values \
-                        - (self.pars.longitude  + 0.5)))
-                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmax = ilon
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude - 0.5)))
-                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmin = ilat
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - (self.pars.longitude  - 0.5)))
-                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmin = ilon        
-                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                
-                
-                if ilatmin < ilatmax:
-                    ilatrange = range(ilatmin,ilatmax+1)
-                else:
-                    ilatrange = range(ilatmax,ilatmin+1)
-                    
-                if ilonmin < ilonmax:
-                    ilonrange = range(ilonmin,ilonmax+1)
-                else:
-                    ilonrange = range(ilonmax,ilonmin+1)           
-                
-                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
-                LAIpixel = 0.
-                count = 0
-                for iilat in [ilat]: #ilatrange
-                    for iilon in [ilon]: #ilonrange
-                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
-                        
-                                        
-                        # if np.isnan(tarray[idatetime]):
-                        #     print("interpolating GIMMS LAIpixel nan value")
-                        #     
-                        #     mask = np.isnan(tarray)
-                        #     
-                        #     #replace each nan value with a interpolated value
-                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-                        #         
-                        #     else:
-                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
-                    
-                        #         tarray *= np.nan 
-                        
-                        count += 1
-                        #tarray_res += tarray
-                LAIpixel = LAIpixel/count
-                
-                count = 0
-                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
-  
-                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
-                #print('LAIpixel:',self.__dict__['LAIpixel'])
-                #print('cveg:',self.__dict__['cveg'])
-                
-                # finally, we rescale the LAI according to the vegetation
-                # fraction
-                value = 0. 
-                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
-                   value =self.pars.LAIpixel/self.pars.cveg
-                else:
-                    # in case of small vegetation fraction, we take just a standard 
-                    # LAI value. It doesn't have a big influence anyway for
-                    # small vegetation
-                    value = 2.
-                #print('LAI:',self.__dict__['LAI'])
-                self.update(source='globaldata',pars={'LAI':value}) 
-
-
-        # in case we have 'sp', we also calculate the 3d pressure fields at
-        # full level and half level
-        if ('sp' in keys) and ('sp' in self.pars.__dict__):
-            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
-
-            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # hydrostatic thickness of each model layer
-            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
-            # # dz = rhodz/(R * T / pfull)
-
-
-            # # subsidence multiplied by density. We calculate the subsidence of
-            # # the in class itself
-            # wrho = np.zeros_like(phalf)
-            # wrho[-1] = 0. 
-
-            # for ihlev in range(0,wrho.shape[0]-1):
-            #     # subsidence multiplied by density is the integral of
-            #     # divergences multiplied by the layer thicknessies
-            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
-            #                     self.air_ac['divU_y'][ihlev:]) * \
-            #                    delpdgrav[ihlev:]).sum()
-
-
-            
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'p':list(pfull)}))
-            self.update(source='globaldata',\
-                        air_ach=pd.DataFrame({'p':list(phalf)}))
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
-            # self.update(source='globaldata',\
-            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
-
-    def check_source(self,source,check_only_sections=None):
-        """ this procedure checks whether data of a specified source is valid.
-
-        INPUT:
-            source: the data source we want to check
-            check_only_sections: a string or list with sections to be checked
-        OUTPUT:
-            returns True or False
-        """
-
-        # we set source ok to false as soon as we find a invalid input
-        source_ok = True
-
-        # convert to a single-item list in case of a string
-        check_only_sections_def = (([check_only_sections]) if \
-                                   type(check_only_sections) is str else \
-                                    check_only_sections)
-                                  
-        if source not in self.sources.keys():
-            self.logger.info('Source '+source+' does not exist')
-            source_ok = False
-
-        for sectiondatakey in self.sources[source]:                             
-            section,datakey = sectiondatakey.split(':')                         
-            if ((check_only_sections_def is None) or \
-                (section in check_only_sections_def)):                          
-                checkdatakeys = []
-                if type(self.__dict__[section]) is pd.DataFrame:
-                    checkdata = self.__dict__[section]
-                elif type(self.__dict__[section]) is model_input:
-                    checkdata = self.__dict__[section].__dict__
-
-                if (datakey not in checkdata):                              
-                    # self.logger.info('Expected key '+datakey+\
-                    #                  ' is not in parameter input')                        
-                    source_ok = False                                           
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
-        
-                    # self.logger.info('Key value of "'+datakey+\
-                    #                  '" is invalid: ('+ \
-                    # str(self.__dict__[section].__dict__[datakey])+')')         
-                    source_ok = False
-
-        return source_ok
-
-    def check_source_globaldata(self):
-        """ this procedure checks whether all global parameter data is
-        available, according to the keys in the self.sources"""
-
-        source_globaldata_ok = True
-
-        #self.get_values_air_input()
-
-        # and now we can get the surface values
-        #class_settings = class4gl_input()
-        #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
-        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
-            source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
-        else:
-            # we only check the ground parameter data (pars section). The 
-            # profile data (air_ap section) are supposed to be valid in any 
-            # case.
-            source_ok = self.check_source(source='globaldata',\
-                                          check_only_sections=['air_ac',\
-                                                               'air_ap',\
-                                                               'pars'])
-            if not source_ok:
-                source_globaldata_ok = False
-        
-            # Additional check: we exclude desert-like
-            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
-                source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
-                source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
-            elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
-                source_globaldata_ok = False
-
-        return source_globaldata_ok
-
-
-class c4gli_iterator():
-    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
-    
-        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
-    """
-    def __init__(self,file):
-        # take file as IO stream
-        self.file = file
-        self.yaml_generator = yaml.load_all(file)
-        self.current_dict = {}
-        self.current_class4gl_input = class4gl_input()
-        separator = self.file.readline() # this is just dummy
-        self.header = file.readline()
-        if self.header != '# CLASS4GL record; format version: 0.1\n':
-            raise NotImplementedError("Wrong format version: '"+self.header+"'")
-    def __iter__(self):
-        return self
-    def __next__(self):
-        self.current_dict = self.yaml_generator.__next__()
-        self.current_class4gl_input.load_yaml_dict(self.current_dict)
-        return self.current_class4gl_input
-
-
-
-#get_cape and lift_parcel are adapted from the SkewT package
-    
-class gl_dia(object):
-    def get_lifted_index(self,timestep=-1):
-        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
-    
-#from SkewT
-#def get_lcl(startp,startt,startdp,nsteps=101):
-#    from numpy import interp
-#    #--------------------------------------------------------------------
-#    # Lift a parcel dry adiabatically from startp to LCL.
-#    # Init temp is startt in K, Init dew point is stwrtdp,
-#    # pressure levels are in Pa    
-#    #--------------------------------------------------------------------
-#
-#    assert startdp<=startt
-#
-#    if startdp==startt:
-#        return np.array([startp]),np.array([startt]),np.array([startdp]),
-#
-#    # Pres=linspace(startp,60000.,nsteps)
-#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
-#
-#    # Lift the dry parcel
-#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
-#    # Mixing ratio isopleth
-#    starte=VaporPressure(startdp)
-#    startw=MixRatio(starte,startp)
-#    e=Pres*startw/(.622+startw)
-#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
-#
-#    # Solve for the intersection of these lines (LCL).
-#    # interp requires the x argument (argument 2)
-#    # to be ascending in order!
-#    P_lcl=interp(0.,T_iso-T_dry,Pres)
-#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
-#
-#    # # presdry=linspace(startp,P_lcl)
-#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
-#
-#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
-#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
-#
-#    return P_lcl,T_lcl
-
-
-
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
-
-
-
-#from class
-def get_lcl(startp,startt,startqv):
-        # Find lifting condensation level iteratively
-    lcl = 20.
-    RHlcl = 0.5
-    
-    itmax = 30
-    it = 0
-    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it
Date: Tue, 21 Aug 2018 22:01:12 +0200
Subject: [PATCH 010/129] restructure again

---
 bin/__init__.py                           |    7 -
 bin/setup/batch_setup_global.py           |   42 -
 bin/setup/setup_bllast.py                 |  719 -------
 bin/setup/setup_global.py                 |  310 ---
 bin/setup/setup_goamazon.py               |  740 -------
 bin/setup/setup_humppa.py                 |  732 -------
 bin/setup/trash/setup_global_old.py       |  284 ---
 bin/simulations/batch_simulations.py      |   77 -
 bin/simulations/runmodel.py               |  130 --
 bin/simulations/simulations.py            |  260 ---
 bin/simulations/simulations_iter.py       |  364 ----
 bin/simulations/simulations_iter_test.py  |  367 ----
 bin/simulations/trash/run_test.py         |  241 ---
 build/lib/bin/__init__.py                 |    6 -
 build/lib/class4gl/__init__.py            |    7 -
 build/lib/class4gl/class4gl.py            | 1611 ---------------
 build/lib/class4gl/data_air.py            |  473 -----
 build/lib/class4gl/data_global.py         |  936 ---------
 build/lib/class4gl/interface_functions.py |  506 -----
 build/lib/class4gl/interface_multi.py     | 2061 -------------------
 build/lib/class4gl/model.py               | 2214 ---------------------
 class4gl.py                               | 1611 ---------------
 22 files changed, 13698 deletions(-)
 delete mode 100644 bin/__init__.py
 delete mode 100644 bin/setup/batch_setup_global.py
 delete mode 100644 bin/setup/setup_bllast.py
 delete mode 100644 bin/setup/setup_global.py
 delete mode 100644 bin/setup/setup_goamazon.py
 delete mode 100644 bin/setup/setup_humppa.py
 delete mode 100644 bin/setup/trash/setup_global_old.py
 delete mode 100644 bin/simulations/batch_simulations.py
 delete mode 100644 bin/simulations/runmodel.py
 delete mode 100644 bin/simulations/simulations.py
 delete mode 100644 bin/simulations/simulations_iter.py
 delete mode 100644 bin/simulations/simulations_iter_test.py
 delete mode 100644 bin/simulations/trash/run_test.py
 delete mode 100644 build/lib/bin/__init__.py
 delete mode 100644 build/lib/class4gl/__init__.py
 delete mode 100644 build/lib/class4gl/class4gl.py
 delete mode 100644 build/lib/class4gl/data_air.py
 delete mode 100644 build/lib/class4gl/data_global.py
 delete mode 100644 build/lib/class4gl/interface_functions.py
 delete mode 100644 build/lib/class4gl/interface_multi.py
 delete mode 100644 build/lib/class4gl/model.py
 delete mode 100644 class4gl.py

diff --git a/bin/__init__.py b/bin/__init__.py
deleted file mode 100644
index a21583b..0000000
--- a/bin/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from . import model,class4gl,interface_multi,data_air,data_global
-
-__version__ = '0.1.0'
-
-__author__ = 'Hendrik Wouters '
-
-__all__ = []
diff --git a/bin/setup/batch_setup_global.py b/bin/setup/batch_setup_global.py
deleted file mode 100644
index 4a3f623..0000000
--- a/bin/setup/batch_setup_global.py
+++ /dev/null
@@ -1,42 +0,0 @@
-
-
-''' 
-Purpose: 
-    launch array job to get sounding and other global forcing data in class4gl input format"
-Usage:
-    python start_setup_global.py
-
-Author:
-    Hendrik Wouters 
-
-'''
-
-import pandas as pd
-import os
-import math
-import numpy as np
-import sys
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-df_stations = pd.read_csv(fn_stations)
-
-# if sys.argv[1] == 'qsub':
-# with qsub
-STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = len(STNlist) 
-print(PROCS)
-BATCHSIZE = math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
-# elif sys.argv[1] == 'wsub':
-#     
-#     
-#     # with wsub
-#     STNlist = list(df_stations.iterrows())
-#     NUMSTNS = len(STNlist)
-#     PROCS = NUMSTNS 
-#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-# 
-#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/SOUNDINGS/setup_global.pbs -t 0-'+str(PROCS-1))
-
diff --git a/bin/setup/setup_bllast.py b/bin/setup/setup_bllast.py
deleted file mode 100644
index af8c8bb..0000000
--- a/bin/setup/setup_bllast.py
+++ /dev/null
@@ -1,719 +0,0 @@
-# -*- coding: utf-8 -*-
-# Read data from BLLAST campaing and convert it to class4gl input
-
-# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
-#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : 42.971834,
-                  "longitude" : 0.3671169,
-                  "name" : "the BLLAST experiment"
-                })
-current_station.name = 90001
-
-
-
-
-
-# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
-# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
-# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
-# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
-
-HOUR_FILES = \
-{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110619_1750_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110620_1750_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110625_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110702_1655_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
-}
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-def efrom_rh100_T(rh100,T):
-    return esat(T)*rh100/100.
-def qfrom_e_p(e,p):
-    return epsilon * e/(p - (1.-epsilon)*e)
-
-def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
-                                     #widths=[14]*19,
-                                     #skiprows=9,
-                                     #skipfooter=15,
-                                     #decimal='.',
-                                     #header=None,
-                                     #names = columns,
-                                     #na_values='-----')
-        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
-        print(air_balloon_in.columns)
-        rowmatches = {
-            't':      lambda x: x['TaRad']+273.15,
-            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
-            'p':      lambda x: x['Press']*100.,
-            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
-            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
-            'z':      lambda x: x['Altitude'] -582.,
-            # from virtual temperature to absolute humidity
-            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
-        }
-        
-        air_balloon = pd.DataFrame()
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon_in)
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
-        }
-        
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-        # filter data so that potential temperature always increases with
-        # height 
-        cols = []
-        for column in air_ap_tail.columns:
-            #if column != 'z':
-                cols.append(column)
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-
-
-
-        # # make theta increase strong enough to avoid numerical
-        # # instability
-        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        # air_ap_tail = pd.DataFrame()
-        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # theta_low = air_ap_head['theta'].iloc[2]
-        # z_low = air_ap_head['z'].iloc[2]
-        # ibottom = 0
-        # for itop in range(0,len(air_ap_tail_orig)):
-        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-        #     if ((theta_mean > (theta_low+0.2) ) and \
-        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
-
-        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-        #         ibottom = itop+1
-        #         theta_low = air_ap_tail.theta.iloc[-1]
-        #         z_low =     air_ap_tail.z.iloc[-1]
-        #     # elif  (itop > len(air_ap_tail_orig)-10):
-        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        # air_ap = \
-        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        # 
-        # # we copy the pressure at ground level from balloon sounding. The
-        # # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
-        # write local solar time, we need to assign the timezone to UTC (which
-        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
-        # tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise']+dt.timedelta(hours=2))\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        print('ldatetime_daylight',dpars['ldatetime_daylight'])
-        print('ldatetime',dpars['ldatetime'])
-        print('lSunrise',dpars['lSunrise'])
-        dpars['day'] = dpars['ldatetime'].day
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        print('tstart',dpars['tstart'])
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='bllast',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
-    
-    print(humpafn)
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-
-    
-    c4gli_morning.pars.sw_ac = []
-    c4gli_morning.pars.sw_ap = True
-    c4gli_morning.pars.sw_lit = False
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-
-
-# stations_for_iter = stations(path_exp)
-# for STNID,station in stations_iterator(stations_for_iter):
-#     records_current_station_index = \
-#             (records_ini.index.get_level_values('STNID') == STNID)
-#     file_current_station_mod = STNID
-# 
-#     with \
-#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-#         for (STNID,index),record_ini in records_iterator(records_ini):
-#             c4gli_ini = get_record_yaml(file_station_ini, 
-#                                         record_ini.index_start, 
-#                                         record_ini.index_end,
-#                                         mode='ini')
-#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-# 
-#             record_mod = records_mod.loc[(STNID,index)]
-#             c4gl_mod = get_record_yaml(file_station_mod, 
-#                                         record_mod.index_start, 
-#                                         record_mod.index_end,
-#                                         mode='mod')
-#             record_afternoon = records_afternoon.loc[(STNID,index)]
-#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-#                                         record_afternoon.index_start, 
-#                                         record_afternoon.index_end,
-#                                         mode='ini')
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/bin/setup/setup_global.py b/bin/setup/setup_global.py
deleted file mode 100644
index 79224d9..0000000
--- a/bin/setup/setup_global.py
+++ /dev/null
@@ -1,310 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thursday, March 29, 11:30 AM
-
-@author: Hendrik Wouters
-
-The dry-2-dry global radio sounding experiment.
-
-usage:
-    python setup_global.py 
-    where  is an integer indicating the row index of the station list
-    under odir+'/'+fn_stations (see below)
-
-this scripts should be called from the pbs script setup_global.pbs
-
-
-
-dependencies:
-    - pandas
-    - class4gl
-    - data_soundings
-
-
-"""
-
-""" import libraries """
-import pandas as pd
-import sys
-#import copy as cp
-import numpy as np
-from sklearn.metrics import mean_squared_error
-import logging
-import datetime as dt
-import os
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-
-
-#calculate the root mean square error
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from data_soundings import wyoming
-#from data_global import data_global
-
-# iniitialize global data
-globaldata = data_global()
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-# read the list of stations with valid ground data (list generated with
-# get_valid_stations.py)
-idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-
-df_stations = pd.read_csv(fn_stations)
-
-
-STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = 100
-BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-
-
-iPROC = int(sys.argv[1])
-
-
-for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
-    one_run = False
-# for iSTN,STN in STNlist[5:]:  
-    
-    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
-    
-
-    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
-    #                   for EXP in experiments.keys()])
-        
-    with open(fnout,'w') as fileout, \
-         open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
-        wy_strm.set_STNM(int(STN['ID']))
-
-        # we consider all soundings after 1981
-        wy_strm.find_first(year=1981)
-        #wy_strm.find(dt.datetime(2004,10,19,6))
-        
-        c4gli = class4gl_input(debug_level=logging.INFO)
-        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
-        # so we continue as long as we can find a new sounding
-                
-        while wy_strm.current is not None:
-            
-            c4gli.clear()
-            try:
-                c4gli.get_profile_wyoming(wy_strm)
-                #print(STN['ID'],c4gli.pars.datetime)
-                #c4gli.get_global_input(globaldata)
-
-                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
-
-                logic = dict()
-                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
-                logic['daylight'] = \
-                    ((c4gli.pars.ldatetime_daylight - 
-                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
-                
-                logic['springsummer'] = (c4gli.pars.theta > 278.)
-                
-                # we take 3000 because previous analysis (ie., HUMPPA) has
-                # focussed towards such altitude
-                le3000 = (c4gli.air_balloon.z <= 3000.)
-                logic['10measurements'] = (np.sum(le3000) >= 10) 
-
-                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
-
-                logic['mlerrlow'] = (\
-                        (len(np.where(leh)[0]) > 0) and \
-                        # in cases where humidity is not defined, the mixed-layer
-                        # values get corr
-                        (not np.isnan(c4gli.pars.theta)) and \
-                        (rmse(c4gli.air_balloon.theta[leh] , \
-                              c4gli.pars.theta,filternan_actual=True) < 1.)\
-                              )
-    
-
-                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
-                
-                print('logic:', logic)
-                # the result
-                morning_ok = np.mean(list(logic.values()))
-                print(morning_ok,c4gli.pars.ldatetime)
-
-            except:
-                morning_ok =False
-                print('obtain morning not good')
-            # the next sounding will be used either for an afternoon sounding
-            # or for the morning sounding of the next day.
-            wy_strm.find_next()
-            # If the morning is ok, then we try to find a decent afternoon
-            # sounding
-            if morning_ok == 1.:
-                print('MORNING OK!')
-                # we get the current date
-                current_date = dt.date(c4gli.pars.ldatetime.year, \
-                                       c4gli.pars.ldatetime.month, \
-                                       c4gli.pars.ldatetime.day)
-                c4gli_afternoon.clear()
-                print('AFTERNOON PROFILE CLEARED')
-                try:
-                    c4gli_afternoon.get_profile_wyoming(wy_strm)
-                    print('AFTERNOON PROFILE OK')
-
-                    if wy_strm.current is not None:
-                        current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
-                    else:
-                        # a dummy date: this will be ignored anyway
-                        current_date_afternoon = dt.date(1900,1,1)
-
-                    # we will dump the latest afternoon sounding that fits the
-                    # minimum criteria specified by logic_afternoon
-                    print(current_date,current_date_afternoon)
-                    c4gli_afternoon_for_dump = None
-                    while ((current_date_afternoon == current_date) and \
-                           (wy_strm.current is not None)):
-                        logic_afternoon =dict()
-
-                        logic_afternoon['afternoon'] = \
-                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                        logic_afternoon['daylight'] = \
-                          ((c4gli_afternoon.pars.ldatetime - \
-                            c4gli_afternoon.pars.ldatetime_daylight \
-                           ).total_seconds()/3600. <= 0.)
-
-
-                        le3000_afternoon = \
-                            (c4gli_afternoon.air_balloon.z <= 3000.)
-                        logic_afternoon['5measurements'] = \
-                            (np.sum(le3000_afternoon) >= 5) 
-
-                        # we only store the last afternoon sounding that fits these
-                        # minimum criteria
-
-                        afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                        print('logic_afternoon: ',logic_afternoon)
-                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                        if afternoon_ok == 1.:
-                            # # doesn't work :(
-                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                            
-                            # so we just create a new one from the same wyoming profile
-                            c4gli_afternoon_for_dump = class4gl_input()
-                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                        wy_strm.find_next()
-                        c4gli_afternoon.clear()
-                        c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                        if wy_strm.current is not None:
-                            current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
-                        else:
-                            # a dummy date: this will be ignored anyway
-                            current_date_afternoon = dt.date(1900,1,1)
-
-                        # Only in the case we have a good pair of soundings, we
-                        # dump them to disk
-                    if c4gli_afternoon_for_dump is not None:
-                        c4gli.update(source='pairs',pars={'runtime' : \
-                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                                 c4gli.pars.datetime_daylight).total_seconds())})
-    
-    
-                        print('ALMOST...')
-                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
-                                
-        
-                            c4gli.get_global_input(globaldata)
-                            print('VERY CLOSE...')
-                            if c4gli.check_source_globaldata() and \
-                                (c4gli.check_source(source='wyoming',\
-                                                   check_only_sections='pars')):
-                                c4gli.dump(fileout)
-                                
-                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                                
-                                
-                                # for keyEXP,dictEXP in experiments.items():
-                                #     
-                                #     c4gli.update(source=keyEXP,pars = dictEXP)
-                                #     c4gl = class4gl(c4gli)
-                                #     # c4gl.run()
-                                #     
-                                #     c4gl.dump(c4glfiles[key])
-                                
-                                print('HIT!!!')
-                                one_run = True
-                except:
-                    print('get profile failed')
-                
-    if one_run:
-        STN.name = STN['ID']
-        all_records_morning = get_records(pd.DataFrame([STN]),\
-                                      odir,\
-                                      subset='morning',
-                                      refetch_records=True,
-                                      )
-        all_records_afternoon = get_records(pd.DataFrame([STN]),\
-                                      odir,\
-                                      subset='afternoon',
-                                      refetch_records=True,
-                                      )
-    else:
-        os.system('rm '+fnout)
-        os.system('rm '+fnout_afternoon)
-
-    # for c4glfile in c4glfiles:
-    #     c4glfile.close()            
-
diff --git a/bin/setup/setup_goamazon.py b/bin/setup/setup_goamazon.py
deleted file mode 100644
index f9efe2c..0000000
--- a/bin/setup/setup_goamazon.py
+++ /dev/null
@@ -1,740 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import xarray as xr
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-import glob
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : -3.21,
-                  "longitude" : -60.6,
-                  "name" : "the GOAMAZON experiment"
-                })
-current_station.name = 90002
-
-# we define the columns ourselves because it is a mess in the file itself.
-columns =\
-['Time[min:sec]',
- 'P[hPa]',
- 'T[C]',
- 'U[%]',
- 'Wsp[m/s]',
- 'Wdir[Grd]',
- 'Lon[°]',
- 'Lat[°]',
- 'Altitude[m]',
- 'GeoPot[m]',
- 'MRI',
- 'RI',    
- 'DewPoint[C]',
- 'Virt. Temp[C]',
- 'Rs[m/min]',
- 'D[kg/m3]',
- 'Azimut[°]',
- 'Elevation[°]',
- 'Range[m]',
-]
-
-DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
-DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
-
-
-DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
-HOUR_FILES = {}
-for iDT, DT in enumerate(DTS):
-    morning_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
-    if len(possible_files)>0:
-        morning_file= possible_files[0]
-    afternoon_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
-    if len(possible_files)>0:
-        afternoon_file= possible_files[0]
-
-    if (morning_file is not None) and (afternoon_file is not None):
-        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
-                          'afternoon':[17.5,afternoon_file]}
-
-print(HOUR_FILES)
-
-# HOUR_FILES = \
-# {
-#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
-#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-# }
-
-
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-def efrom_rh100_T(rh100,T):
-    return esat(T)*rh100/100.
-def qfrom_e_p(e,p):
-    return epsilon * e/(p - (1.-epsilon)*e)
-
-
-
-def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
-        print(balloon_file)
-        
-        xrin = balloon_file
-        air_balloon = pd.DataFrame()
-
-        air_balloon['t'] = xrin.tdry.values+273.15
-        air_balloon['p'] = xrin.pres.values*100.
-        
-        air_balloon['u'] = xrin.u_wind.values
-        air_balloon['v'] = xrin.v_wind.values
-        air_balloon['WSPD'] = xrin['wspd'].values
-        
-        print(xrin.rh.values.shape)
-        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
-        
-
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        # air_balloon_in = pd.read_fwf(balloon_file,
-        #                              widths=[14]*19,
-        #                              skiprows=9,
-        #                              skipfooter=15,
-        #                              decimal=',',
-        #                              header=None,
-        #                              names = columns,
-        #                              na_values='-----')
-    
-
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
-            'rho': lambda x: x.p /x.t / x.R ,
-        }
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        print('alt in xrin?:','alt' in xrin)
-        if 'alt' in xrin:
-            air_balloon['z'] = xrin.alt.values
-        else:
-            air_balloon['z'] = 0.
-            for irow,row in air_balloon.iloc[1:].iterrows():
-                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
-                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
-                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
-                        
-             
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-
-        # there are issues with the lower measurements in the HUMPPA campaign,
-        # for which a steady decrease of potential temperature is found, which
-        # is unrealistic.  Here I filter them away
-        ifirst = 0
-        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
-            ifirst = ifirst+1
-        print ('ifirst:',ifirst)
-        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            print(air_balloon.z.shape,air_balloon.thetav.shape,)
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='humppa',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn =pair['morning'][1]
-    print(humpafn)
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn = pair['afternoon'][1]
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-
-"""
-stations_for_iter = stations(path_exp)
-for STNID,station in stations_iterator(stations_for_iter):
-    records_current_station_index = \
-            (records_ini.index.get_level_values('STNID') == STNID)
-    file_current_station_mod = STNID
-
-    with \
-    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-        for (STNID,index),record_ini in records_iterator(records_ini):
-            c4gli_ini = get_record_yaml(file_station_ini, 
-                                        record_ini.index_start, 
-                                        record_ini.index_end,
-                                        mode='ini')
-            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-
-            record_mod = records_mod.loc[(STNID,index)]
-            c4gl_mod = get_record_yaml(file_station_mod, 
-                                        record_mod.index_start, 
-                                        record_mod.index_end,
-                                        mode='mod')
-            record_afternoon = records_afternoon.loc[(STNID,index)]
-            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-                                        record_afternoon.index_start, 
-                                        record_afternoon.index_end,
-                                        mode='ini')
-"""
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/bin/setup/setup_humppa.py b/bin/setup/setup_humppa.py
deleted file mode 100644
index ff37628..0000000
--- a/bin/setup/setup_humppa.py
+++ /dev/null
@@ -1,732 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : 61.8448,
-                  "longitude" : 24.2882,
-                  "name" : "the HUMMPA experiment"
-                })
-current_station.name = 90000
-
-# we define the columns ourselves because it is a mess in the file itself.
-columns =\
-['Time[min:sec]',
- 'P[hPa]',
- 'T[C]',
- 'U[%]',
- 'Wsp[m/s]',
- 'Wdir[Grd]',
- 'Lon[°]',
- 'Lat[°]',
- 'Altitude[m]',
- 'GeoPot[m]',
- 'MRI',
- 'RI',    
- 'DewPoint[C]',
- 'Virt. Temp[C]',
- 'Rs[m/min]',
- 'D[kg/m3]',
- 'Azimut[°]',
- 'Elevation[°]',
- 'Range[m]',
-]
-
-
-HOUR_FILES = \
-{ dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
-  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[18,'humppa_071310_1800.txt']},
-  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
-  dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
-  dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
-  dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
-  dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
-  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[21,'humppa_071910_2100.txt']},
-#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
-  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
-  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[18,'humppa_072210_1800.txt']},
- # something is wrong with ths profile
- # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
-  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
-  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
-  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
-  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
-  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
-  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[18,'humppa_072910_1800.txt']},
-  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
-  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[15,'humppa_073110_1500.txt']},
-  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
-  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[18,'humppa_080210_1800.txt']},
-  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
-  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
-  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
-  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
-  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
-  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
-  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[18,'humppa_081010_1800.txt']},
-}
-
-
-
-
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        air_balloon_in = pd.read_fwf(balloon_file,
-                                     widths=[14]*19,
-                                     skiprows=9,
-                                     skipfooter=15,
-                                     decimal=',',
-                                     header=None,
-                                     names = columns,
-                                     na_values='-----')
-    
-        rowmatches = {
-            't':      lambda x: x['T[C]']+273.15,
-            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
-            'p':      lambda x: x['P[hPa]']*100.,
-            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
-            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
-            'z':      lambda x: x['Altitude[m]'],
-            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
-        }
-        
-        air_balloon = pd.DataFrame()
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon_in)
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
-        }
-        
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-
-        # there are issues with the lower measurements in the HUMPPA campaign,
-        # for which a steady decrease of potential temperature is found, which
-        # is unrealistic.  Here I filter them away
-        ifirst = 0
-        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
-            ifirst = ifirst+1
-        print ('ifirst:',ifirst)
-        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-
-        # # make theta increase strong enough to avoid numerical
-        # # instability
-        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        # air_ap_tail = pd.DataFrame()
-        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # theta_low = air_ap_head['theta'].iloc[2]
-        # z_low = air_ap_head['z'].iloc[2]
-        # ibottom = 0
-        # for itop in range(0,len(air_ap_tail_orig)):
-        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-        #     if ((theta_mean > (theta_low+0.2) ) and \
-        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
-
-        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-        #         ibottom = itop+1
-        #         theta_low = air_ap_tail.theta.iloc[-1]
-        #         z_low =     air_ap_tail.z.iloc[-1]
-        #     # elif  (itop > len(air_ap_tail_orig)-10):
-        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        # 
-        # air_ap = \
-        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='humppa',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
-    print(humpafn)
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-# stations_for_iter = stations(path_exp)
-# for STNID,station in stations_iterator(stations_for_iter):
-#     records_current_station_index = \
-#             (records_ini.index.get_level_values('STNID') == STNID)
-#     file_current_station_mod = STNID
-# 
-#     with \
-#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-#         for (STNID,index),record_ini in records_iterator(records_ini):
-#             c4gli_ini = get_record_yaml(file_station_ini, 
-#                                         record_ini.index_start, 
-#                                         record_ini.index_end,
-#                                         mode='ini')
-#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-# 
-#             record_mod = records_mod.loc[(STNID,index)]
-#             c4gl_mod = get_record_yaml(file_station_mod, 
-#                                         record_mod.index_start, 
-#                                         record_mod.index_end,
-#                                         mode='mod')
-#             record_afternoon = records_afternoon.loc[(STNID,index)]
-#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-#                                         record_afternoon.index_start, 
-#                                         record_afternoon.index_end,
-#                                         mode='ini')
-
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/bin/setup/trash/setup_global_old.py b/bin/setup/trash/setup_global_old.py
deleted file mode 100644
index d812684..0000000
--- a/bin/setup/trash/setup_global_old.py
+++ /dev/null
@@ -1,284 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thursday, March 29, 11:30 AM
-
-@author: Hendrik Wouters
-
-The dry-2-dry global radio sounding experiment.
-
-usage:
-    python setup_global.py 
-    where  is an integer indicating the row index of the station list
-    under odir+'/'+fn_stations (see below)
-
-this scripts should be called from the pbs script setup_global.pbs
-
-
-
-dependencies:
-    - pandas
-    - class4gl
-    - data_soundings
-
-
-"""
-
-""" import libraries """
-import pandas as pd
-import sys
-#import copy as cp
-import numpy as np
-from sklearn.metrics import mean_squared_error
-import logging
-import datetime as dt
-import os
-import math
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-
-
-#calculate the root mean square error
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from data_soundings import wyoming
-#from data_global import data_global
-
-# iniitialize global data
-globaldata = data_global()
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-# read the list of stations with valid ground data (list generated with
-# get_valid_stations.py)
-idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-
-df_stations = pd.read_csv(fn_stations)
-
-
-STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = 100
-BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-
-
-iPROC = int(sys.argv[1])
-
-
-for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
-# for iSTN,STN in STNlist[5:]:  
-    
-    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
-    
-
-    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
-    #                   for EXP in experiments.keys()])
-        
-    with open(fnout,'w') as fileout, \
-         open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
-        wy_strm.set_STNM(int(STN['ID']))
-
-        # we consider all soundings after 1981
-        wy_strm.find_first(year=1981)
-        #wy_strm.find(dt.datetime(2004,10,19,6))
-        
-        c4gli = class4gl_input(debug_level=logging.INFO)
-        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
-        # so we continue as long as we can find a new sounding
-        while wy_strm.current is not None:
-            
-            c4gli.clear()
-            c4gli.get_profile_wyoming(wy_strm)
-            #print(STN['ID'],c4gli.pars.datetime)
-            #c4gli.get_global_input(globaldata)
-
-            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
-
-            logic = dict()
-            logic['morning'] =  (c4gli.pars.ldatetime.hour < 12.)
-            logic['daylight'] = \
-                ((c4gli.pars.ldatetime_daylight - 
-                  c4gli.pars.ldatetime).total_seconds()/3600. <= 5.)
-            
-            logic['springsummer'] = (c4gli.pars.theta > 278.)
-            
-            # we take 3000 because previous analysis (ie., HUMPPA) has
-            # focussed towards such altitude
-            le3000 = (c4gli.air_balloon.z <= 3000.)
-            logic['10measurements'] = (np.sum(le3000) >= 10) 
-
-            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
-
-            try:
-                logic['mlerrlow'] = (\
-                        (len(np.where(leh)[0]) > 0) and \
-                        # in cases where humidity is not defined, the mixed-layer
-                        # values get corr
-                        (not np.isnan(c4gli.pars.theta)) and \
-                        (rmse(c4gli.air_balloon.theta[leh] , \
-                              c4gli.pars.theta,filternan_actual=True) < 1.)\
-                              )
-    
-            except:
-                logic['mlerrlow'] = False
-                print('rmse probably failed')
-
-            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
-            
-            print('logic:', logic)
-            # the result
-            morning_ok = np.mean(list(logic.values()))
-            print(morning_ok,c4gli.pars.ldatetime)
-            
-            # the next sounding will be used either for an afternoon sounding
-            # or for the morning sounding of the next day.
-            wy_strm.find_next()
-
-            # If the morning is ok, then we try to find a decent afternoon
-            # sounding
-            if morning_ok == 1.:
-                # we get the current date
-                current_date = dt.date(c4gli.pars.ldatetime.year, \
-                                       c4gli.pars.ldatetime.month, \
-                                       c4gli.pars.ldatetime.day)
-                c4gli_afternoon.clear()
-                c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                if wy_strm.current is not None:
-                    current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
-                else:
-                    # a dummy date: this will be ignored anyway
-                    current_date_afternoon = dt.date(1900,1,1)
-
-                # we will dump the latest afternoon sounding that fits the
-                # minimum criteria specified by logic_afternoon
-                c4gli_afternoon_for_dump = None
-                while ((current_date_afternoon == current_date) and \
-                       (wy_strm.current is not None)):
-                    logic_afternoon =dict()
-
-                    logic_afternoon['afternoon'] = \
-                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                    logic_afternoon['daylight'] = \
-                      ((c4gli_afternoon.pars.ldatetime - \
-                        c4gli_afternoon.pars.ldatetime_daylight \
-                       ).total_seconds()/3600. <= 2.)
-
-
-                    le3000_afternoon = \
-                        (c4gli_afternoon.air_balloon.z <= 3000.)
-                    logic_afternoon['5measurements'] = \
-                        (np.sum(le3000_afternoon) >= 5) 
-
-                    # we only store the last afternoon sounding that fits these
-                    # minimum criteria
-
-                    afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                    print('logic_afternoon: ',logic_afternoon)
-                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                    if afternoon_ok == 1.:
-                        # # doesn't work :(
-                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                        
-                        # so we just create a new one from the same wyoming profile
-                        c4gli_afternoon_for_dump = class4gl_input()
-                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                    wy_strm.find_next()
-                    c4gli_afternoon.clear()
-                    c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                    if wy_strm.current is not None:
-                        current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
-                    else:
-                        # a dummy date: this will be ignored anyway
-                        current_date_afternoon = dt.date(1900,1,1)
-
-                    # Only in the case we have a good pair of soundings, we
-                    # dump them to disk
-                if c4gli_afternoon_for_dump is not None:
-                    c4gli.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                             c4gli.pars.datetime_daylight).total_seconds())})
-    
-    
-                    print('ALMOST...')
-                    if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
-                            
-        
-                        c4gli.get_global_input(globaldata)
-                        print('VERY CLOSE...')
-                        if c4gli.check_source_globaldata() and \
-                            (c4gli.check_source(source='wyoming',\
-                                               check_only_sections='pars')):
-                            c4gli.dump(fileout)
-                            
-                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                            
-                            
-                            # for keyEXP,dictEXP in experiments.items():
-                            #     
-                            #     c4gli.update(source=keyEXP,pars = dictEXP)
-                            #     c4gl = class4gl(c4gli)
-                            #     # c4gl.run()
-                            #     
-                            #     c4gl.dump(c4glfiles[key])
-                            
-                            print('HIT!!!')
-                
-                
-    # for c4glfile in c4glfiles:
-    #     c4glfile.close()            
-
diff --git a/bin/simulations/batch_simulations.py b/bin/simulations/batch_simulations.py
deleted file mode 100644
index b5d4cc3..0000000
--- a/bin/simulations/batch_simulations.py
+++ /dev/null
@@ -1,77 +0,0 @@
-
-import argparse
-
-import pandas as pd
-import os
-import math
-import numpy as np
-import sys
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-df_stations = pd.read_csv(fn_stations)
-
-# if 'path-soundings' in args.__dict__.keys():
-#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-# else:
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
-    parser.add_argument('--exec')
-    parser.add_argument('--experiments')#should be ';'-seperated list
-    parser.add_argument('--split-by',default=-1)
-    args = parser.parse_args()
-
-experiments = args.experiments.split(';')
-#SET = 'GLOBAL'
-SET = args.dataset
-print(args.experiments)
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-for expname in experiments:
-    #exp = EXP_DEFS[expname]
-    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-    os.system('rm -R '+path_exp)
-
-totalchunks = 0
-for istation,current_station in all_stations.iterrows():
-    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
-    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
-    totalchunks +=chunks_current_station
-
-#if sys.argv[1] == 'qsub':
-# with qsub
-os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
-                                       ',split_by='+str(args.split_by)+\
-                                       ',exec='+str(args.exec)+\
-                                       ',experiments='+str(args.experiments))
-# elif sys.argv[1] == 'wsub':
-#     
-#     # with wsub
-#     STNlist = list(df_stations.iterrows())
-#     NUMSTNS = len(STNlist)
-#     PROCS = NUMSTNS 
-#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-# 
-#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
-
diff --git a/bin/simulations/runmodel.py b/bin/simulations/runmodel.py
deleted file mode 100644
index fc4fd19..0000000
--- a/bin/simulations/runmodel.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#
-# Example of how to run the Python code, and access the output
-# This case is identical to the default setup of CLASS (the version with interface) 
-#
-
-from pylab import *
-from model import *
-
-""" 
-Create empty model_input and set up case
-"""
-run1input = model_input()
-
-run1input.dt         = 60.       # time step [s]
-run1input.runtime    = 12*3600    # total run time [s]
-
-# mixed-layer input
-run1input.sw_ml      = True      # mixed-layer model switch
-run1input.sw_shearwe = False     # shear growth mixed-layer switch
-run1input.sw_fixft   = False     # Fix the free-troposphere switch
-run1input.h          = 200.      # initial ABL height [m]
-run1input.Ps         = 101300.   # surface pressure [Pa]
-run1input.divU       = 0.        # horizontal large-scale divergence of wind [s-1]
-run1input.fc         = 1.e-4     # Coriolis parameter [m s-1]
-
-run1input.theta      = 288.      # initial mixed-layer potential temperature [K]
-run1input.dtheta     = 1.        # initial temperature jump at h [K]
-run1input.gammatheta = 0.006     # free atmosphere potential temperature lapse rate [K m-1]
-run1input.advtheta   = 0.        # advection of heat [K s-1]
-run1input.beta       = 0.2       # entrainment ratio for virtual heat [-]
-run1input.wtheta     = 0.1       # surface kinematic heat flux [K m s-1]
-
-run1input.q          = 0.008     # initial mixed-layer specific humidity [kg kg-1]
-run1input.dq         = -0.001    # initial specific humidity jump at h [kg kg-1]
-run1input.gammaq     = 0.        # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-run1input.advq       = 0.        # advection of moisture [kg kg-1 s-1]
-run1input.wq         = 0.1e-3    # surface kinematic moisture flux [kg kg-1 m s-1]
-
-run1input.CO2        = 422.      # initial mixed-layer CO2 [ppm]
-run1input.dCO2       = -44.      # initial CO2 jump at h [ppm]
-run1input.gammaCO2   = 0.        # free atmosphere CO2 lapse rate [ppm m-1]
-run1input.advCO2     = 0.        # advection of CO2 [ppm s-1]
-run1input.wCO2       = 0.        # surface kinematic CO2 flux [ppm m s-1]
-
-run1input.sw_wind    = False     # prognostic wind switch
-run1input.u          = 6.        # initial mixed-layer u-wind speed [m s-1]
-run1input.du         = 4.        # initial u-wind jump at h [m s-1]
-run1input.gammau     = 0.        # free atmosphere u-wind speed lapse rate [s-1]
-run1input.advu       = 0.        # advection of u-wind [m s-2]
-
-run1input.v          = -4.0      # initial mixed-layer u-wind speed [m s-1]
-run1input.dv         = 4.0       # initial u-wind jump at h [m s-1]
-run1input.gammav     = 0.        # free atmosphere v-wind speed lapse rate [s-1]
-run1input.advv       = 0.        # advection of v-wind [m s-2]
-
-run1input.sw_sl      = False     # surface layer switch
-run1input.ustar      = 0.3       # surface friction velocity [m s-1]
-run1input.z0m        = 0.02      # roughness length for momentum [m]
-run1input.z0h        = 0.002     # roughness length for scalars [m]
-
-run1input.sw_rad     = False     # radiation switch
-run1input.lat        = 51.97     # latitude [deg]
-run1input.lon        = -4.93     # longitude [deg]
-run1input.doy        = 268.      # day of the year [-]
-run1input.tstart     = 6.8       # time of the day [h UTC]
-run1input.cc         = 0.0       # cloud cover fraction [-]
-run1input.Q          = 400.      # net radiation [W m-2] 
-run1input.dFz        = 0.        # cloud top radiative divergence [W m-2] 
-
-run1input.sw_ls      = False     # land surface switch
-run1input.ls_type    = 'js'      # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-run1input.wg         = 0.21      # volumetric water content top soil layer [m3 m-3]
-run1input.w2         = 0.21      # volumetric water content deeper soil layer [m3 m-3]
-run1input.cveg       = 0.85      # vegetation fraction [-]
-run1input.Tsoil      = 285.      # temperature top soil layer [K]
-run1input.T2         = 286.      # temperature deeper soil layer [K]
-run1input.a          = 0.219     # Clapp and Hornberger retention curve parameter a
-run1input.b          = 4.90      # Clapp and Hornberger retention curve parameter b
-run1input.p          = 4.        # Clapp and Hornberger retention curve parameter c
-run1input.CGsat      = 3.56e-6   # saturated soil conductivity for heat
-
-run1input.wsat       = 0.472     # saturated volumetric water content ECMWF config [-]
-run1input.wfc        = 0.323     # volumetric water content field capacity [-]
-run1input.wwilt      = 0.171     # volumetric water content wilting point [-]
-
-run1input.C1sat      = 0.132     
-run1input.C2ref      = 1.8
-
-run1input.LAI        = 2.        # leaf area index [-]
-run1input.gD         = 0.0       # correction factor transpiration for VPD [-]
-run1input.rsmin      = 110.      # minimum resistance transpiration [s m-1]
-run1input.rssoilmin  = 50.       # minimun resistance soil evaporation [s m-1]
-run1input.alpha      = 0.25      # surface albedo [-]
-
-run1input.Ts         = 290.      # initial surface temperature [K]
-
-run1input.Wmax       = 0.0002    # thickness of water layer on wet vegetation [m]
-run1input.Wl         = 0.0000    # equivalent water layer depth for wet vegetation [m]
-
-run1input.Lambda     = 5.9       # thermal diffusivity skin layer [-]
-
-run1input.c3c4       = 'c3'      # Plant type ('c3' or 'c4')
-
-run1input.sw_cu      = False     # Cumulus parameterization switch
-run1input.dz_h       = 150.      # Transition layer thickness [m]
-
-"""
-Init and run the model
-"""
-r1 = model(run1input)
-r1.run()
-
-"""
-Plot output
-"""
-figure()
-subplot(131)
-plot(r1.out.t, r1.out.h)
-xlabel('time [h]')
-ylabel('h [m]')
-
-subplot(132)
-plot(r1.out.t, r1.out.theta)
-xlabel('time [h]')
-ylabel('theta [K]')
-
-subplot(133)
-plot(r1.out.t, r1.out.q*1000.)
-xlabel('time [h]')
-ylabel('q [g kg-1]')
diff --git a/bin/simulations/simulations.py b/bin/simulations/simulations.py
deleted file mode 100644
index 719f9a5..0000000
--- a/bin/simulations/simulations.py
+++ /dev/null
@@ -1,260 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-
-import argparse
-
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--first-station-row')
-parser.add_argument('--last-station-row')
-parser.add_argument('--station-id') # run a specific station id
-parser.add_argument('--dataset')
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--error-handling',default='dump_on_success')
-parser.add_argument('--experiments')
-parser.add_argument('--split-by',default=-1)# station soundings are split
-                                            # up in chunks
-
-parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-EXP_DEFS  =\
-{
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-path_soundingsSET = args.path_soundings+'/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iter = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iter.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    if args.station_id is not None:
-        stations_iter = stations_iterator(all_stations)
-        STNID,run_station = stations_iterator.set_STNID(STNID)
-        run_stations = pd.DataFrame(run_station)
-    else:
-        run_stations = pd.DataFrame(all_stations)
-        if args.last_station_row is not None:
-            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
-        if args.first_station_row is not None:
-            run_stations = run_stations.iloc[int(args.first_station):]
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = args.path_experiments+'/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            print(records_morning_station_chunk)
-
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-
-                    if args.error_handling == 'dump_always':
-                        try:
-                            c4gl.run()
-                        except:
-                            print('run not succesfull')
-                        onerun = True
-
-                        c4gli_morning.dump(file_ini)
-                        
-                        
-                        c4gl.dump(file_mod,\
-                                  include_input=False,\
-                                  #timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    # in this case, only the file will dumped if the runs were
-                    # successful
-                    elif args.error_handling == 'dump_on_succes':
-                        try:
-                            c4gl.run()
-                            print('run not succesfull')
-                            onerun = True
-
-                            c4gli_morning.dump(file_ini)
-                            
-                            
-                            c4gl.dump(file_mod,\
-                                      include_input=False,\
-                                      #timeseries_only=timeseries_only,\
-                                     )
-                            onerun = True
-                        except:
-                            print('run not succesfull')
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/bin/simulations/simulations_iter.py b/bin/simulations/simulations_iter.py
deleted file mode 100644
index 5dfbaff..0000000
--- a/bin/simulations/simulations_iter.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk',default=0)
-    args = parser.parse_args()
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if 'global_chunk' in args.__dict__.keys():
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    if 'last_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    
-    if 'first_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    if 'station_chunk' in args.__dict__.keys():
-        run_station_chunk = args.station_chunk
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-    for istation,current_station in run_stations.iterrows():
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                #if iexp == 11:
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    
-                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
-                    EFobs = c4gli_morning.pars.EF
-                    
-                    b = c4gli_morning.pars.wwilt
-                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
-                    
-                    
-                    try:
-                        #fb = f(b)
-                        c4gli_morning.pars.wg = b
-                        c4gli_morning.pars.w2 = b
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fb = EFmod - EFobs
-                        EFmodb = EFmod
-                        c4glb = c4gl
-                        c4gli_morningb = c4gli_morning
-                        
-                        #fc = f(c)
-                        c4gli_morning.pars.wg = c
-                        c4gli_morning.pars.w2 = c
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fc = EFmod - EFobs
-                        print (EFmodb,EFobs,fb)
-                        print (EFmod,EFobs,fc)
-                        c4glc = c4gl
-                        c4gli_morningc = c4gli_morning
-                        i=0
-                        
-
-                        if fc*fb > 0.:
-                            if abs(fb) < abs(fc):
-                                c4gl = c4glb
-                                c4gli_morning = c4gli_morningb
-                            else:
-                                c4gl = c4glc
-                                c4gli_morning = c4gli_morningc
-                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
-                        
-                        else:
-                            print('starting ITERATION!!!')
-                            cn  = c - fc/(fc-fb)*(c-b)
-                            
-                            
-                            #fcn = f(cn)
-                            c4gli_morning.pars.wg = np.asscalar(cn)
-                            c4gli_morning.pars.w2 = np.asscalar(cn)
-                            c4gl = class4gl(c4gli_morning)
-                            c4gl.run()
-                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                            
-                            tol = 0.02
-                            ftol = 10.
-                            maxiter = 10
-                            
-                            is1=0
-                            is1max=1
-                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
-                                if fc * fcn > 0:
-                                    temp = c
-                                    c = b
-                                    b = temp
-                                
-                                a = b
-                                fa = fb
-                                b = c
-                                fb = fc
-                                c = cn
-                                fc = fcn
-                                              
-                                print(i,a,b,c,fcn)
-                                
-                                s1 = c - fc/(fc-fb)*(c-b) 
-                                s2 = c - fc/(fc-fa)*(c-a)
-                                
-                                
-                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
-                                
-                                
-                                if (abs(s1-b) < abs(s2-b)):
-                                    is1 = 0
-                                else:
-                                    is1 +=1
-                                    
-                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
-                                if is1 < is1max:           
-                                    s = s1
-                                    print('s1')
-                                else:
-                                    is1 = 0
-                                    s = s2
-                                    print('s2')
-                                
-                                if c > b:
-                                    l = b
-                                    r = c
-                                else:
-                                    l = c
-                                    r = b
-                                
-                                m = (b+c)/2.
-                                     
-                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
-                                    cn = s
-                                    print('midpoint')
-                                else:
-                                    cn = m
-                                    print('bissection')
-                                    
-                                
-                                #fcn = f(cn)
-                                c4gli_morning.pars.wg = np.asscalar(cn)
-                                c4gli_morning.pars.w2 = np.asscalar(cn)
-                                c4gl = class4gl(c4gli_morning)
-                                c4gl.run()
-                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                                
-                            
-                                i+=1
-                                
-                            if i == maxiter:
-                                raise StopIteration('did not converge')
-
-
-
-
-                        #c4gl = class4gl(c4gli_morning)
-                        #c4gl.run()
-
-                        c4gli_morning.pars.itersteps = i
-                        c4gli_morning.dump(file_ini)
-                        
-                        
-                        c4gl.dump(file_mod,\
-                                      include_input=False,\
-                                   #   timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    except:
-                        print('run not succesfull')
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/bin/simulations/simulations_iter_test.py b/bin/simulations/simulations_iter_test.py
deleted file mode 100644
index eefd475..0000000
--- a/bin/simulations/simulations_iter_test.py
+++ /dev/null
@@ -1,367 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk')
-    args = parser.parse_args()
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    run_stations = pd.DataFrame(all_stations)
-    if args.last_station is not None:
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    if args.first_station is not None:
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    run_station_chunk = 0
-    if args.station_chunk is not None:
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-                #if iexp == 11:
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    
-                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
-                    EFobs = c4gli_morning.pars.EF
-                    
-                    b = c4gli_morning.pars.wwilt
-                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
-                    
-                    
-                    try:
-                        #fb = f(b)
-                        c4gli_morning.pars.wg = b
-                        c4gli_morning.pars.w2 = b
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fb = EFmod - EFobs
-                        EFmodb = EFmod
-                        c4glb = c4gl
-                        c4gli_morningb = c4gli_morning
-                        
-                        #fc = f(c)
-                        c4gli_morning.pars.wg = c
-                        c4gli_morning.pars.w2 = c
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fc = EFmod - EFobs
-                        print (EFmodb,EFobs,fb)
-                        print (EFmod,EFobs,fc)
-                        c4glc = c4gl
-                        c4gli_morningc = c4gli_morning
-                        i=0
-                        
-
-                        if fc*fb > 0.:
-                            if abs(fb) < abs(fc):
-                                c4gl = c4glb
-                                c4gli_morning = c4gli_morningb
-                            else:
-                                c4gl = c4glc
-                                c4gli_morning = c4gli_morningc
-                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
-                        
-                        else:
-                            print('starting ITERATION!!!')
-                            cn  = c - fc/(fc-fb)*(c-b)
-                            
-                            
-                            #fcn = f(cn)
-                            c4gli_morning.pars.wg = np.asscalar(cn)
-                            c4gli_morning.pars.w2 = np.asscalar(cn)
-                            c4gl = class4gl(c4gli_morning)
-                            c4gl.run()
-                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                            
-                            tol = 0.02
-                            ftol = 10.
-                            maxiter = 10
-                            
-                            is1=0
-                            is1max=1
-                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
-                                if fc * fcn > 0:
-                                    temp = c
-                                    c = b
-                                    b = temp
-                                
-                                a = b
-                                fa = fb
-                                b = c
-                                fb = fc
-                                c = cn
-                                fc = fcn
-                                              
-                                print(i,a,b,c,fcn)
-                                
-                                s1 = c - fc/(fc-fb)*(c-b) 
-                                s2 = c - fc/(fc-fa)*(c-a)
-                                
-                                
-                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
-                                
-                                
-                                if (abs(s1-b) < abs(s2-b)):
-                                    is1 = 0
-                                else:
-                                    is1 +=1
-                                    
-                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
-                                if is1 < is1max:           
-                                    s = s1
-                                    print('s1')
-                                else:
-                                    is1 = 0
-                                    s = s2
-                                    print('s2')
-                                
-                                if c > b:
-                                    l = b
-                                    r = c
-                                else:
-                                    l = c
-                                    r = b
-                                
-                                m = (b+c)/2.
-                                     
-                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
-                                    cn = s
-                                    print('midpoint')
-                                else:
-                                    cn = m
-                                    print('bissection')
-                                    
-                                
-                                #fcn = f(cn)
-                                c4gli_morning.pars.wg = np.asscalar(cn)
-                                c4gli_morning.pars.w2 = np.asscalar(cn)
-                                c4gl = class4gl(c4gli_morning)
-                                c4gl.run()
-                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                                
-                            
-                                i+=1
-                                
-                            if i == maxiter:
-                                raise StopIteration('did not converge')
-
-
-
-
-                        #c4gl = class4gl(c4gli_morning)
-                        #c4gl.run()
-                        onerun = True
-
-                        c4gli_morning.pars.itersteps = i
-                    except:
-                        print('run not succesfull')
-                    c4gli_morning.dump(file_ini)
-                    
-                    
-                    c4gl.dump(file_mod,\
-                                  include_input=False,\
-                               #   timeseries_only=timeseries_only,\
-                             )
-                    onerun = True
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/bin/simulations/trash/run_test.py b/bin/simulations/trash/run_test.py
deleted file mode 100644
index 767d960..0000000
--- a/bin/simulations/trash/run_test.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk')
-    parser.add_argument('--c4gl-path',default='')
-    args = parser.parse_args()
-
-if args.c4gl_path == '': 
-    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-else:
-    sys.path.insert(0, args.c4gl_path)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    run_stations = pd.DataFrame(all_stations)
-    if args.last_station is not None:
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    if args.first_station is not None:
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    run_station_chunk = 0
-    if args.station_chunk is not None:
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            print(records_morning_station_chunk)
-
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    try:
-                        c4gl.run()
-                    except:
-                        print('run not succesfull')
-                    onerun = True
-
-                    c4gli_morning.dump(file_ini)
-                    
-                    
-                    c4gl.dump(file_mod,\
-                              include_input=False,\
-                              #timeseries_only=timeseries_only,\
-                             )
-                    onerun = True
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/build/lib/bin/__init__.py b/build/lib/bin/__init__.py
deleted file mode 100644
index 58f6cca..0000000
--- a/build/lib/bin/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-
-__version__ = '0.1.0'
-
-__author__ = 'Hendrik Wouters '
-
-__all__ = []
diff --git a/build/lib/class4gl/__init__.py b/build/lib/class4gl/__init__.py
deleted file mode 100644
index a21583b..0000000
--- a/build/lib/class4gl/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from . import model,class4gl,interface_multi,data_air,data_global
-
-__version__ = '0.1.0'
-
-__author__ = 'Hendrik Wouters '
-
-__all__ = []
diff --git a/build/lib/class4gl/class4gl.py b/build/lib/class4gl/class4gl.py
deleted file mode 100644
index 7baaa51..0000000
--- a/build/lib/class4gl/class4gl.py
+++ /dev/null
@@ -1,1611 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-
-Created on Mon Jan 29 12:33:51 2018
-
-Module file for class4gl, which  extents the class-model to be able to take
-global air profiles as input. It exists of:
-
-CLASSES:
-    - an input object, namely class4gl_input. It includes:
-        - a function to read Wyoming sounding data from a yyoming stream object
-        - a function to read global data from a globaldata library object 
-    - the model object: class4gl
-    - ....    
-
-DEPENDENCIES:
-    - xarray
-    - numpy
-    - data_global
-    - Pysolar
-    - yaml
-
-@author: Hendrik Wouters
-
-"""
-
-
-
-""" Setup of envirnoment """
-
-# Standard modules of the stand class-boundary-layer model
-from model import model
-from model import model_output as class4gl_output
-from model import model_input
-from model import qsat
-#from data_soundings import wyoming 
-import Pysolar
-import yaml
-import logging
-import warnings
-import pytz
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-
-# Generic Python Packages
-import numpy as np
-import datetime as dt
-import pandas as pd
-import xarray as xr
-import io
-#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
-from data_global import data_global
-grav = 9.81
-
-# this is just a generic input object
-class generic_input(object):
-    def __init__(self):
-        self.init = True
-
-
-# all units from all variables in CLASS(4GL) should be defined here!
-units = {
-         'h':'m',
-         'theta':'K', 
-         'q':'kg/kg',
-         'cc': '-',
-         'cveg': '-',
-         'wg': 'm3 m-3',
-         'w2': 'm3 m-3',
-         #'wg': 'kg/kg',
-         'Tsoil': 'K',
-         'T2': 'K',
-         'z0m': 'm',
-         'alpha': '-',
-         'LAI': '-',
-         'dhdt':'m/h',
-         'dthetadt':'K/h',
-         'dqdt':'kg/kg/h',
-         'BR': '-',
-         'EF': '-',
-}
-
-class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
-
-    def __init__(self,set_pars_defaults=True,debug_level=None):
-
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        print('hello')
-        self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
-     
-        # # add ch to logger
-        # self.logger.addHandler(ch)
-
-        # """ end set up logger """
-
-
-
-        # these are the standard model input single-value parameters for class
-        self.pars = model_input()
-
-        # diagnostic parameters of the initial profile
-        self.diag = dict()
-
-        # In this variable, we keep track of the different parameters from where it originates from. 
-        self.sources = {}
-
-        if set_pars_defaults:
-            self.set_pars_defaults()
-
-    def set_pars_defaults(self):
-
-        """ 
-        Create empty model_input and set up case
-        """
-        defaults = dict( 
-        dt         = 60.    , # time step [s] 
-        runtime    = 6*3600 ,  # total run time [s]
-        
-        # mixed-layer input
-        sw_ml      = True   ,  # mixed-layer model switch
-        sw_shearwe = False  ,  # shear growth mixed-layer switch
-        sw_fixft   = False  ,  # Fix the free-troposphere switch
-        h          = 200.   ,  # initial ABL height [m]
-        Ps         = 101300.,  # surface pressure [Pa]
-        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
-        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
-        
-        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
-        dtheta     = 1.     ,  # initial temperature jump at h [K]
-        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
-        advtheta   = 0.     ,  # advection of heat [K s-1]
-        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
-        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
-        
-        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
-        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
-        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
-        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
-        
-        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
-        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
-        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
-        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
-        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
-        sw_wind    = True  ,  # prognostic wind switch
-        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
-        du         = 0.     ,  # initial u-wind jump at h [m s-1]
-        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
-        advu       = 0.     ,  # advection of u-wind [m s-2]
-        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
-        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
-        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
-        advv       = 0.     ,  # advection of v-wind [m s-2]
-        sw_sl      = True   , # surface layer switch
-        ustar      = 0.3    ,  # surface friction velocity [m s-1]
-        z0m        = 0.02   ,  # roughness length for momentum [m]
-        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
-        sw_rad     = True   , # radiation switch
-        lat        = 51.97  ,  # latitude [deg]
-        lon        = -4.93  ,  # longitude [deg]
-        doy        = 268.   ,  # day of the year [-]
-        tstart     = 6.8    ,  # time of the day [h UTC]
-        cc         = 0.0    ,  # cloud cover fraction [-]
-        Q          = 400.   ,  # net radiation [W m-2] 
-        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
-        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
-        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
-        cveg       = 0.85   ,  # vegetation fraction [-]
-        Tsoil      = 295.   ,  # temperature top soil layer [K]
-        Ts         = 295.   ,    # initial surface temperature [K]
-        T2         = 296.   ,  # temperature deeper soil layer [K]
-        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
-        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
-        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
-        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
-        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
-        wfc        = 0.323  ,  # volumetric water content field capacity [-]
-        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
-        C1sat      = 0.132  ,  
-        C2ref      = 1.8    ,
-        LAI        = 2.     ,  # leaf area index [-]
-        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
-        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
-        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
-        alpha      = 0.25   ,  # surface albedo [-]
-        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
-        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
-        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
-        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
-        sw_cu      = False  ,  # Cumulus parameterization switch
-        dz_h       = 150.   ,  # Transition layer thickness [m]
-        cala       = None   ,  # soil heat conductivity [W/(K*m)]
-        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
-        sw_ls      = True   ,
-        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
-        sw_lit     = False,
-        )
-        pars = model_input()
-        for key in defaults:
-            pars.__dict__[key] = defaults[key]
-        
-        self.update(source='defaults',pars=pars)
-        
-    def clear(self):
-        """ this procudure clears the class4gl_input """
-
-        for key in list(self.__dict__.keys()):
-            del(self.__dict__[key])
-        self.__init__()
-
-    def dump(self,file):
-        """ this procedure dumps the class4gl_input object into a yaml file
-            
-            Input: 
-                - self.__dict__ (internal): the dictionary from which we read 
-            Output:
-                - file: All the parameters in self.__init__() are written to
-                the yaml file, including pars, air_ap, sources etc.
-        """
-        file.write('---\n')
-        index = file.tell()
-        file.write('# CLASS4GL input; format version: 0.1\n')
-
-        # write out the position of the current record
-        yaml.dump({'index':index}, file, default_flow_style=False)
-
-        # we do not include the none values
-        for key,data in self.__dict__.items():
-            #if ((type(data) == model_input) or (type(class4gl_input):
-            if key == 'pars':
-
-                pars = {'pars' : self.__dict__['pars'].__dict__}
-                parsout = {}
-                for key in pars.keys():
-                    if pars[key] is not None:
-                        parsout[key] = pars[key]
-
-                yaml.dump(parsout, file, default_flow_style=False)
-            elif type(data) == dict:
-                if key == 'sources':
-                    # in case of sources, we want to have a
-                    # condensed list format as well, so we leave out
-                    # 'default_flow_style=False'
-                    yaml.dump({key : data}, file)
-                else: 
-                    yaml.dump({key : data}, file,
-                              default_flow_style=False)
-            elif type(data) == pd.DataFrame:
-                # in case of dataframes (for profiles), we want to have a
-                # condensed list format as well, so we leave out
-                # 'default_flow_style=False'
-                yaml.dump({key: data.to_dict(orient='list')},file)
-
-                # # these are trials to get it into a more human-readable
-                # fixed-width format, but it is too complex
-                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
-                #file.write(stream)
-                
-                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
-                #file.write(key+': !!str |\n')
-                #file.write(str(data)+'\n')
-       
-    def load_yaml_dict(self,yaml_dict,reset=True):
-        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
-            
-            Input: 
-                - yaml_dict: the dictionary from which we read 
-                - reset: reset data before reading        
-            Output:
-                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
-        """
-        
-        if reset:
-            for key in list(self.__dict__.keys()):
-                del(self.__dict__[key])
-            self.__init__()
-
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                self.__dict__[key] = model_input()
-                self.__dict__[key].__dict__ = data
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            elif key == 'sources':
-                self.__dict__[key] = data
-            elif key == 'diag':
-                self.__dict__[key] = data
-            else: 
-                warnings.warn("Key '"+key+"' may not be implemented.")
-                self.__dict__[key] = data
-
-    def update(self,source,**kwargs):
-        """ this procedure is to make updates of input parameters and tracking
-        of their source more convenient. It implements the assignment of
-        parameter source/sensitivity experiment IDs ('eg.,
-        'defaults', 'sounding balloon', any satellite information, climate
-        models, sensitivity tests etc.). These are all stored in a convenient
-        way with as class4gl_input.sources.  This way, the user can always consult with
-        from where parameters data originates from.  
-        
-        Input:
-            - source:    name of the underlying dataset
-            - **kwargs: a dictionary of data input, for which the key values
-            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
-            the values is a again a dictionary/dataframe of datakeys/columns
-            ('wg','PRES','datetime', ...) and datavalues (either single values,
-            profiles ...), eg., 
-
-                pars = {'wg': 0.007  , 'w2', 0.005}
-                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
-                                     300.,...]}
-            
-        Output:
-            - self.__dict__[datatype] : object to which the parameters are
-                                        assigned. They can be consulted with
-                                        self.pars, self.profiles, etc.
-                                        
-            - self.sources[source] : It supplements the overview overview of
-                                     data sources can be consulted with
-                                     self.sources. The structure is as follows:
-                                     as:
-                self.sources = { 
-                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
-                'GLEAM' :  ['pars:wg','pars:w2', ...],
-                 ...
-                }
-        
-        """
-
-        #print(source,kwargs)
-
-        for key,data in kwargs.items():
-
-            #print(key)
-            # if the key is not in class4gl_input object, then just add it. In
-            # that case, the update procedures below will just overwrite it 
-            if key not in self.__dict__:
-                self.__dict__[key] = data
-
-
-            
-
-            #... we do an additional check to see whether there is a type
-            # match. I not then raise a key error
-            if (type(data) != type(self.__dict__[key]) \
-                # we allow dict input for model_input pars
-                and not ((key == 'pars') and (type(data) == dict) and \
-                (type(self.__dict__[key]) == model_input))):
-
-                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
-
-
-            # This variable keeps track of the added data that is supplemented
-            # by the current source. We add this to class4gl_input.sources
-            datakeys = []
-
-            #... and we update the class4gl_input data, and this depends on the
-            # data type
-
-            if type(self.__dict__[key]) == pd.DataFrame:
-                # If the data type is a dataframe, then we update the columns
-                for column in list(data.columns):
-                    #print(column)
-                    self.__dict__[key][column] = data[column]
-                    datakeys.append(column)
-                    
-
-            elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
-                # dictionary of parameters
-                if type(data) == model_input:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data.__dict__}
-                    datakeys = list(data.__dict__.keys())
-                elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
-                    datakeys = list(data.keys())
-                else:
-                    raise TypeError('input key '+key+' is not of the same type\
-                                    as the one in the class4gl_object')
-
-            elif type(self.__dict__[key]) == dict:
-                # if the data type is a dictionary, we update the
-                # dictionary 
-                self.__dict__[key] = {self.__dict__[key] , data}
-                datakeys = list(data.keys())
-
-
-            # if source entry is not existing yet, we add it
-            if source not in self.sources.keys():
-                self.sources[source] = []
-
-
-            # self.logger.debug('updating section "'+\
-            #                  key+' ('+' '.join(datakeys)+')'\
-            #                  '" from source \
-            #                  "'+source+'"')
-
-            # Update the source dictionary: add the provided data keys to the
-            # specified source list
-            for datakey in datakeys:
-                # At first, remove the occurences of the keys in the other
-                # source lists
-                for sourcekey,sourcelist in self.sources.items():
-                    if key+':'+datakey in sourcelist:
-                        self.sources[sourcekey].remove(key+':'+datakey)
-                # Afterwards, add it to the current source list
-                self.sources[source].append(key+':'+datakey)
-
-
-        # # in case the datatype is a class4gl_input_pars, we update its keys
-        # # according to **kwargs dictionary
-        # if type(self.__dict__[datatype]) == class4gl_input_pars:
-        #     # add the data parameters to the datatype object dictionary of the
-        #     # datatype
-        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
-        #                                        **kwargs}
-        # # in case, the datatype reflects a dataframe, we update the columns according
-        # # to the *args list
-        # elif type(self.__dict__[datatype]) == pd.DataFrame:
-        #     for dataframe in args:
-        #         for column in list(dataframe.columns):
-        #             self.__dict__[datatype][column] = dataframe[column]
-        
-
-    def get_profile(self,IOBJ, *args, **argv):
-        # if type(IOBJ) == wyoming:
-        self.get_profile_wyoming(IOBJ,*args,**argv)
-        # else:
-        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
-        
-    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
-        """ 
-            Purpose: 
-                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
-
-            Input:
-                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
-                function will take the profile at the stream's current
-                position. 
-                2. air_ap_mode: which air profile do we take? 
-                    - b : best
-                    - l : according to lower limit for the mixed-layer height
-                            estimate
-                    - u : according to upper limit for the mixed-layer height
-                            estimate
-
-
-            Output:
-                1. all single-value parameters are stored in the
-                   class4gl_input.pars object
-                2. the souding profiles are stored in the in the
-                   class4gl_input.air_balloon dataframe
-                3. modified sounding profiles for which the mixed layer height
-                   is fitted
-                4. ...
-
-        """
-
-
-        # Raise an error in case the input stream is not the correct object
-        # if type(wy_strm) is not wyoming:
-        #    raise TypeError('Not a wyoming type input stream')
-
-        # Let's tell the class_input object that it is a Wyoming fit type
-        self.air_ap_type = 'wyoming'
-        # ... and which mode of fitting we apply
-        self.air_ap_mode = air_ap_mode
-
-        """ Temporary variables used for output """
-        # single value parameters derived from the sounding profile
-        dpars = dict()
-        # profile values
-        air_balloon = pd.DataFrame()
-        # fitted profile values
-        air_ap = pd.DataFrame()
-        
-        string = wy_strm.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = wy_strm.current.find_next('pre').find_next('pre').text
-        
-        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
-        dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
-               }
-        
-        # we get weird output when it's a numpy Timestamp, so we convert it to
-        # pd.datetime type
-
-        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
-        dpars['STNID'] = dpars['Station number']
-
-        # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
-        # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
-                              / \
-                             (air_balloon.MIXR/1000.+1.)
-        # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-        
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
-
-        
-
-        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-
-        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
-
-
-        # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        #is_valid = (air_balloon.z >= 0)
-        # # this is an alternative pipe/numpy method
-        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
-
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
-
-        if len(valid_indices) > 0:
-            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            
-            # the final mixed-layer height that will be used by class. We round it
-            # to 1 decimal so that we get a clean yaml output format
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
-            dpars['Ps'] = np.nan
-
-
-
-
-        if ~np.isnan(dpars['h']):
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u 
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-            
-
-
-
-        # First 3 data points of the mixed-layer fit. We create a empty head
-        # first
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-        
-        #calculate mixed-layer jump ( this should be larger than 0.1)
-        
-        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
-        
-        # make a row object for defining the jump
-        jump = air_ap_head.iloc[0] * np.nan
-            
-        if air_ap_tail.shape[0] > 1:
-
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = dpars['theta']
-        z_low =     dpars['h']
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                (z_mean > (z_low+10.)) and \
-                (theta_mean > (theta_low+0.2) ) and \
-                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-
-
-
-
-
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        #print(air_ap['PRES'].iloc[0])
-
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-
-        
-        dpars['lat'] = dpars['Station latitude']
-        dpars['latitude'] = dpars['lat']
-        
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        dpars['longitude'] = dpars['Station longitude']
-        
-        dpars['ldatetime'] = dpars['datetime'] \
-                            + \
-                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-
-        # # we make a pars object that is similar to the destination object
-        # pars = model_input()
-        # for key,value in dpars.items():
-        #     pars.__dict__[key] = value
-
-
-        # we round the columns to a specified decimal, so that we get a clean
-        # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
-# 
-        for column,decimal in decimals.items():
-            air_balloon[column] = air_balloon[column].round(decimal)
-            air_ap[column] = air_ap[column].round(decimal)
-
-        self.update(source='wyoming',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-
-        
-    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
-    
-        """
-        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
-                 according to the position (lat lon) and the class datetime and timespan
-                 globaldata should be a globaldata multifile object
-        
-        Input: 
-            - globaldata: this is the library object
-            - only_keys: only extract specified keys
-            - exclude_keys: do not inherit specified keys
-        """
-        classdatetime      = np.datetime64(self.pars.datetime_daylight)
-        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
-                                           + \
-                                           dt.timedelta(seconds=self.pars.runtime)\
-                                          )
-
-
-        # # list of variables that we get from global ground data
-        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
-        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
-        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
-        #                 'texture', 'itex', 'isoil', 'BR',
-        #                 'b', 'cveg',
-        #                 'C1sat', 
-        #                 'C2ref', 'p', 'a',
-        #                 ] #globaldata.datasets.keys():
-
-        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
-        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
-
-
-        if type(globaldata) is not data_global:
-            raise TypeError("Wrong type of input library") 
-
-        # by default, we get all dataset keys
-        keys = list(globaldata.datasets.keys())
-
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
-
-        # # In case there is surface pressure, we also calculate the half-level
-        # # and full-level pressure fields
-        # if ('sp' in keys):
-        #     keys.append('pfull')
-        #     keys.append('phalf')
-
-        # If specified, we only take the keys that are in only_keys
-        if only_keys is not None:
-            for key in keys:
-                if key not in only_keys:
-                    keys.remove(key)
-                
-        # If specified, we take out keys that are in exclude keys
-        if exclude_keys is not None:
-            for key in keys:
-                if key in exclude_keys:
-                    keys.remove(key)
-
-        # we set everything to nan first in the pars section (non-profile parameters
-        # without lev argument), so that we can check afterwards whether the
-        # data is well-fetched or not.
-        for key in keys:
-            if not ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None) and \
-                ('lev' in globaldata.datasets[key].page[key].dims)):
-                self.update(source='globaldata',pars={key:np.nan})
-            # # we do not check profile input for now. We assume it is
-            # # available
-            #else:
-            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
-
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
-
-        for key in keys:
-            # If we find it, then we obtain the variables
-            if ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None)):
-
-                # check first whether the dataset has a height coordinate (3d space)
-                if 'lev' in globaldata.datasets[key].page[key].dims:
-
-                    # first, we browse to the correct file that has the current time
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-                        globaldata.datasets[key].browse_page(time=classdatetime)
-
-                    
-                    if (globaldata.datasets[key].page is not None):
-                        # find longitude and latitude coordinates
-                        ilats = (np.abs(globaldata.datasets[key].page.lat -
-                                        self.pars.latitude) < 0.5)
-                        ilons = (np.abs(globaldata.datasets[key].page.lon -
-                                        self.pars.longitude) < 0.5)
-                        
-                        # if we have a time dimension, then we look up the required timesteps during the class simulation
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
-
-                            # In case we didn't find any correct time, we take the
-                            # closest one.
-                            if np.sum(itimes) == 0.:
-
-
-                                classdatetimemean = \
-                                    np.datetime64(self.pars.datetime_daylight + \
-                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
-                                                ))
-
-                                dstimes = globaldata.datasets[key].page.time
-                                time = dstimes.sel(time=classdatetimemean,method='nearest')
-                                itimes = (globaldata.datasets[key].page.time ==
-                                          time)
-                                
-                        else:
-                            # we don't have a time coordinate so it doesn't matter
-                            # what itimes is
-                            itimes = 0
-
-                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
-
-                        # over which dimensions we take a mean:
-                        dims = globaldata.datasets[key].page[key].dims
-                        namesmean = list(dims)
-                        namesmean.remove('lev')
-                        idxmean = [dims.index(namemean) for namemean in namesmean]
-                        
-                        value = \
-                        globaldata.datasets[key].page[key].isel(time=itimes,
-                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
-
-                        # Ideally, source should be equal to the datakey of globaldata.library 
-                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
-                        #  but therefore the globaldata class requires a revision to make this work
-                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
-
-                else:
-                    # this procedure is for reading the ground fields (2d space). 
-                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-    
-                       # first, we browse to the correct file
-                       #print(key)
-                       globaldata.datasets[key].browse_page(time=classdatetime)
-    
-                    if globaldata.datasets[key].page is not None:
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - self.pars.latitude))
-                        ilat = np.where((DIST) == np.min(DIST))[0][0]
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - self.pars.longitude))
-                        ilon = np.where((DIST) == np.min(DIST))[0][0]
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - (self.pars.latitude + 0.5)))
-                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmax = ilat
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - (self.pars.longitude  + 0.5)))
-                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmax = ilon
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lat.values\
-                                - (self.pars.latitude - 0.5)))
-                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmin = ilat
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lon.values\
-                                - (self.pars.longitude  - 0.5)))
-                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmin = ilon        
-                        
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
-                        else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
-                            
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                            
-                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
-                            
-                            classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
-                                                             dt.timedelta(seconds=self.pars.runtime)\
-                                                            ) 
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
-                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
-                                idatetimeend -= 1
-                            idatetime = np.min((idatetime,idatetimeend))
-                            #for gleam, we take the previous day values
-                            if key in ['wg', 'w2']:
-                                idatetime = idatetime - 1
-                                idatetimeend = idatetimeend - 1
-
-                            # in case of soil temperature, we take the exact
-                            # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
-                                idatetimeend = idatetime
-                            
-                            idts = range(idatetime,idatetimeend+1)
-                            
-                            count = 0
-                            self.__dict__[key] = 0.
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    for iidts in idts:
-                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
-                                        count += 1
-                            value = value/count
-                            self.update(source='globaldata',pars={key:value.item()})
-                                
-                        else:
-                                
-                            count = 0
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
-                                    count += 1
-                            value = value/count                        
-
-                            self.update(source='globaldata',pars={key:value.item()})
-
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            self.logger.debug('also update LAI based on LAIpixel and cveg') 
-            # I suppose LAI pixel is already determined in the previous
-            # procedure. Anyway...
-            key = 'LAIpixel'
-
-            if globaldata.datasets[key].page is not None:
-                # first, we browse to the correct file that has the current time
-                if 'time' in list(globaldata.datasets[key].page[key].dims):
-                    globaldata.datasets[key].browse_page(time=classdatetime)
-            
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - self.pars.latitude))
-                ilat = np.where((DIST) == np.min(DIST))[0][0]
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - self.pars.longitude))
-                ilon = np.where((DIST) == np.min(DIST))[0][0]
-                 
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude + 0.5)))
-                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmax = ilat
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values \
-                        - (self.pars.longitude  + 0.5)))
-                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmax = ilon
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude - 0.5)))
-                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmin = ilat
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - (self.pars.longitude  - 0.5)))
-                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmin = ilon        
-                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                
-                
-                if ilatmin < ilatmax:
-                    ilatrange = range(ilatmin,ilatmax+1)
-                else:
-                    ilatrange = range(ilatmax,ilatmin+1)
-                    
-                if ilonmin < ilonmax:
-                    ilonrange = range(ilonmin,ilonmax+1)
-                else:
-                    ilonrange = range(ilonmax,ilonmin+1)           
-                
-                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
-                LAIpixel = 0.
-                count = 0
-                for iilat in [ilat]: #ilatrange
-                    for iilon in [ilon]: #ilonrange
-                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
-                        
-                                        
-                        # if np.isnan(tarray[idatetime]):
-                        #     print("interpolating GIMMS LAIpixel nan value")
-                        #     
-                        #     mask = np.isnan(tarray)
-                        #     
-                        #     #replace each nan value with a interpolated value
-                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-                        #         
-                        #     else:
-                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
-                    
-                        #         tarray *= np.nan 
-                        
-                        count += 1
-                        #tarray_res += tarray
-                LAIpixel = LAIpixel/count
-                
-                count = 0
-                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
-  
-                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
-                #print('LAIpixel:',self.__dict__['LAIpixel'])
-                #print('cveg:',self.__dict__['cveg'])
-                
-                # finally, we rescale the LAI according to the vegetation
-                # fraction
-                value = 0. 
-                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
-                   value =self.pars.LAIpixel/self.pars.cveg
-                else:
-                    # in case of small vegetation fraction, we take just a standard 
-                    # LAI value. It doesn't have a big influence anyway for
-                    # small vegetation
-                    value = 2.
-                #print('LAI:',self.__dict__['LAI'])
-                self.update(source='globaldata',pars={'LAI':value}) 
-
-
-        # in case we have 'sp', we also calculate the 3d pressure fields at
-        # full level and half level
-        if ('sp' in keys) and ('sp' in self.pars.__dict__):
-            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
-
-            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # hydrostatic thickness of each model layer
-            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
-            # # dz = rhodz/(R * T / pfull)
-
-
-            # # subsidence multiplied by density. We calculate the subsidence of
-            # # the in class itself
-            # wrho = np.zeros_like(phalf)
-            # wrho[-1] = 0. 
-
-            # for ihlev in range(0,wrho.shape[0]-1):
-            #     # subsidence multiplied by density is the integral of
-            #     # divergences multiplied by the layer thicknessies
-            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
-            #                     self.air_ac['divU_y'][ihlev:]) * \
-            #                    delpdgrav[ihlev:]).sum()
-
-
-            
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'p':list(pfull)}))
-            self.update(source='globaldata',\
-                        air_ach=pd.DataFrame({'p':list(phalf)}))
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
-            # self.update(source='globaldata',\
-            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
-
-    def check_source(self,source,check_only_sections=None):
-        """ this procedure checks whether data of a specified source is valid.
-
-        INPUT:
-            source: the data source we want to check
-            check_only_sections: a string or list with sections to be checked
-        OUTPUT:
-            returns True or False
-        """
-
-        # we set source ok to false as soon as we find a invalid input
-        source_ok = True
-
-        # convert to a single-item list in case of a string
-        check_only_sections_def = (([check_only_sections]) if \
-                                   type(check_only_sections) is str else \
-                                    check_only_sections)
-                                  
-        if source not in self.sources.keys():
-            self.logger.info('Source '+source+' does not exist')
-            source_ok = False
-
-        for sectiondatakey in self.sources[source]:                             
-            section,datakey = sectiondatakey.split(':')                         
-            if ((check_only_sections_def is None) or \
-                (section in check_only_sections_def)):                          
-                checkdatakeys = []
-                if type(self.__dict__[section]) is pd.DataFrame:
-                    checkdata = self.__dict__[section]
-                elif type(self.__dict__[section]) is model_input:
-                    checkdata = self.__dict__[section].__dict__
-
-                if (datakey not in checkdata):                              
-                    # self.logger.info('Expected key '+datakey+\
-                    #                  ' is not in parameter input')                        
-                    source_ok = False                                           
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
-        
-                    # self.logger.info('Key value of "'+datakey+\
-                    #                  '" is invalid: ('+ \
-                    # str(self.__dict__[section].__dict__[datakey])+')')         
-                    source_ok = False
-
-        return source_ok
-
-    def check_source_globaldata(self):
-        """ this procedure checks whether all global parameter data is
-        available, according to the keys in the self.sources"""
-
-        source_globaldata_ok = True
-
-        #self.get_values_air_input()
-
-        # and now we can get the surface values
-        #class_settings = class4gl_input()
-        #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
-        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
-            source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
-        else:
-            # we only check the ground parameter data (pars section). The 
-            # profile data (air_ap section) are supposed to be valid in any 
-            # case.
-            source_ok = self.check_source(source='globaldata',\
-                                          check_only_sections=['air_ac',\
-                                                               'air_ap',\
-                                                               'pars'])
-            if not source_ok:
-                source_globaldata_ok = False
-        
-            # Additional check: we exclude desert-like
-            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
-                source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
-                source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
-            elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
-                source_globaldata_ok = False
-
-        return source_globaldata_ok
-
-
-class c4gli_iterator():
-    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
-    
-        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
-    """
-    def __init__(self,file):
-        # take file as IO stream
-        self.file = file
-        self.yaml_generator = yaml.load_all(file)
-        self.current_dict = {}
-        self.current_class4gl_input = class4gl_input()
-        separator = self.file.readline() # this is just dummy
-        self.header = file.readline()
-        if self.header != '# CLASS4GL record; format version: 0.1\n':
-            raise NotImplementedError("Wrong format version: '"+self.header+"'")
-    def __iter__(self):
-        return self
-    def __next__(self):
-        self.current_dict = self.yaml_generator.__next__()
-        self.current_class4gl_input.load_yaml_dict(self.current_dict)
-        return self.current_class4gl_input
-
-
-
-#get_cape and lift_parcel are adapted from the SkewT package
-    
-class gl_dia(object):
-    def get_lifted_index(self,timestep=-1):
-        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
-    
-#from SkewT
-#def get_lcl(startp,startt,startdp,nsteps=101):
-#    from numpy import interp
-#    #--------------------------------------------------------------------
-#    # Lift a parcel dry adiabatically from startp to LCL.
-#    # Init temp is startt in K, Init dew point is stwrtdp,
-#    # pressure levels are in Pa    
-#    #--------------------------------------------------------------------
-#
-#    assert startdp<=startt
-#
-#    if startdp==startt:
-#        return np.array([startp]),np.array([startt]),np.array([startdp]),
-#
-#    # Pres=linspace(startp,60000.,nsteps)
-#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
-#
-#    # Lift the dry parcel
-#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
-#    # Mixing ratio isopleth
-#    starte=VaporPressure(startdp)
-#    startw=MixRatio(starte,startp)
-#    e=Pres*startw/(.622+startw)
-#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
-#
-#    # Solve for the intersection of these lines (LCL).
-#    # interp requires the x argument (argument 2)
-#    # to be ascending in order!
-#    P_lcl=interp(0.,T_iso-T_dry,Pres)
-#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
-#
-#    # # presdry=linspace(startp,P_lcl)
-#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
-#
-#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
-#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
-#
-#    return P_lcl,T_lcl
-
-
-
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
-
-
-
-#from class
-def get_lcl(startp,startt,startqv):
-        # Find lifting condensation level iteratively
-    lcl = 20.
-    RHlcl = 0.5
-    
-    itmax = 30
-    it = 0
-    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       #self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_air_input()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        #print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_air_input()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_air_input()
-       
-
-
-    def get_values_air_input(self,latitude=None,longitude=None):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        BLHV = np.max((BLHV,10.))
-        BLHVu = np.max((BLHVu,10.))
-        BLHVd = np.max((BLHVd,10.))
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
-                
-                # get index of lowest valid observation. This seems to vary
-                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
-                if len(idxvalid) > 0:
-                    #print('idxvalid',idxvalid)
-                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
-                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
-                    else:
-                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
-                    #print(col,meanabl)
-               
-                
-                # if col == 'PRES':
-                #     meanabl =  
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV',]:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
-                        dtheta = np.max((0.1,dtheta_pre))
-                        #meanabl = meanabl - (dtheta - dtheta_pre)
-                        #print('dtheta_pre',dtheta_pre)
-                        #print('dtheta',dtheta)
-                        #print('meanabl',meanabl)
-                        #stop
-                        
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
-            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-            
-            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
-        
-            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
-            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
-
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-
-        try:
-            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
-        except:
-            print("could not convert latitude coordinate")
-            PARAMS.insert(0,'latitude', np.nan)
-            PARAMS.insert(0,'lat', np.nan)
-        try:
-            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
-            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
-            PARAMS.insert(0,'lon', 0.)
-        except:
-            print("could not convert longitude coordinate")
-            PARAMS.insert(0,'longitude', np.nan)
-            PARAMS.insert(0,'lon', 0.)
-
-        if latitude is not None:
-            print('overwriting latitude with specified value')
-            PARAMS['latitude'] = np.float(latitude)
-            PARAMS['lat'] = np.float(latitude)
-        if longitude is not None:
-            print('overwriting longitude with specified value')
-            PARAMS['longitude'] = np.float(longitude)
-        try:
-            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
-            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
-            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            # This is the nearest datetime when sun is up (for class)
-            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
-            # apply the same time shift for UTC datetime
-            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
-            
-        except:
-            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
-            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
-            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
-            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
-
-        
-
-        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
-        # as we are forcing lon equal to zero this is is expressed in local suntime
-        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
-
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
-        elif self.mode == 'b': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNb
-            BLCOLUMN = ONE_COLUMNb
-        elif self.mode == 'u': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNu
-            BLCOLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNd
-            BLCOLUMN = ONE_COLUMNd
-        else:
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb
-
-        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
-        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
-        # print(BLCOLUMN['HAGL'][lt6000])
-        # print(BLCOLUMN['HAGL'][lt2500])
-        # 
-        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
-
-        #print(BLCOLUMN['HAGL'][lt2500])
-        PARAMS.insert(0,'OK',
-                      ((BLHVe < 200.) and 
-                       ( len(np.where(lt6000)[0]) > 5) and
-                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
-                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
-                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
-                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
-                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
-                      )
-                     )
-
-        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
-        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
-        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
-        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
-        
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = USE_ONECOLUMN
-        # if self.mode == 'o': #original 
-        #     self.ONE_COLUMN = ONE_COLUMN
-        # elif self.mode == 'b': # best BLH
-        #     self.ONE_COLUMN = ONE_COLUMNb
-        # elif self.mode == 'u':# upper BLH
-        #     self.ONE_COLUMN = ONE_COLUMNu
-        # elif self.mode == 'd': # lower BLH
-        #     self.ONE_COLUMN=ONE_COLUMNd
-        # else:
-        #     self.ONE_COLUMN = ONE_COLUMN
-
diff --git a/build/lib/class4gl/data_global.py b/build/lib/class4gl/data_global.py
deleted file mode 100644
index 9c3d9b5..0000000
--- a/build/lib/class4gl/data_global.py
+++ /dev/null
@@ -1,936 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: Hendrik Wouters
-
-Purpose: provides class routines for ground and atmosphere conditions used for
-the CLASS miced-layer model
-
-Usage:
-    from data_global import data_global
-    from class4gl import class4gl_input
-    from data_soundings import wyoming
-
-    # create a data_global object and load initial data pages
-    globaldata = data_global()
-    globaldata.load_datasets()
-    # create a class4gl_input object
-    c4gli = class4gl_input()
-    # Initialize it with profile data. We need to do this first. Actually this
-    # will set the coordinate parameters (datetime, latitude, longitude) in
-    # class4gl_input.pars.__dict__, which is required to read point data from
-    # the data_global object.
-
-    # open a Wyoming stream for a specific station
-    wy_strm = wyoming(STNM=91376)
-    # load the first profile
-    wy_strm.find_first()
-    # load the profile data into the class4gl_input object
-    c4gli.get_profile_wyoming(wy_strm)
-    
-    # and finally, read the global input data for this profile
-    c4gli.get_global_input(globaldata)
-
-
-"""
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-#import pynacolada as pcd
-import pandas as pd
-import xarray as xr
-import os
-import glob
-import sys
-import errno
-import warnings
-import logging
-
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-class book(object):
-    """ this is a class for a dataset spread over multiple files. It has a
-    similar purpose  open_mfdataset, but only 1 file (called current 'page')
-    one is loaded at a time. This saves precious memory.  """
-    def __init__(self,fn,concat_dim = None,debug_level=None):
-        self.logger = logging.getLogger('book')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # filenames are expanded as a list and sorted by filename
-        self.pages = glob.glob(fn); self.pages.sort()
-        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
-        if len(self.pages) == 0:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
-        self.ipage = -1; self.page = None
-        self.renames = {} # each time when opening a file, a renaming should be done.
-        self.set_page(0)
-
-        # we consider that the outer dimension is the one we concatenate
-        self.concat_dim = concat_dim
-        if self.concat_dim is None:
-            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
-
-    # this wraps the xarray sel-commmand
-    def sel(*args, **kwargs):
-        for dim in kwargs.keys():
-            if dim == self.concat_dim:
-                self.browse_page(**{dim: kwargs[dim]})
-        return page.sel(*args,**kwargs)
-
-
-    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
-    #def __getattr__(self,attr):
-    #    orig_attr = self.page.__getattribute__(attr)
-    #    if callable(orig_attr):
-    #        def hooked(*args, **kwargs):
-    #            for dim in kwargs.keys():
-    #                if dim == self.concat_dim:
-    #                    self.browse_page(**{dim: kwargs[dim]})
-    #
-    #            result = orig_attr(*args, **kwargs)
-    #            # prevent wrapped_class from becoming unwrapped
-    #            if result == self.page:
-    #                return self
-    #            self.post()
-    #            return result
-    #        return hooked
-    #    else:
-    #        return orig_attr
-
-    def set_renames(self,renames):
-        #first, we convert back to original names, and afterwards, we apply the update of the renames.
-        reverse_renames = dict((v,k) for k,v in self.renames.items())
-        self.renames = renames
-        if self.page is not None:
-            self.page = self.page.rename(reverse_renames)
-            self.page = self.page.rename(self.renames)
-
-    def set_page(self,ipage,page=None):
-        """ this sets the right page according to ipage:
-                - We do not switch the page if we are already at the right one
-                - we set the correct renamings (level -> lev, latitude -> lat,
-                etc.)
-                - The dataset is also squeezed.
-        """
-
-        if ((ipage != self.ipage) or (page is not None)):
-
-            if self.page is not None:
-                self.page.close()
-
-            self.ipage = ipage
-            if page is not None:
-                self.page = page
-            else:
-                if self.ipage == -1:
-                   self.page = None
-                else:
-                    #try:
-
-                    self.logger.info("Switching to page "+str(self.ipage)+': '\
-                                     +self.pages[self.ipage])
-                    self.page = xr.open_dataset(self.pages[self.ipage])
-
-
-            # do some final corrections to the dataset to make them uniform
-            if self.page is not None:
-               if 'latitude' in self.page.dims:
-#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
-               if 'level' in self.page.dims:
-                   self.page = self.page.rename({'level':'lev'})
-
-               self.page = self.page.rename(self.renames)
-               self.page = self.page.squeeze(drop=True)
-
-    def browse_page(self,rewind=2,**args):
-
-        # at the moment, this is only tested with files that are stacked according to the time dimension.
-        dims = args.keys()
-
-
-        if self.ipage == -1:
-            self.set_page(0)
-
-        found = False
-        iipage = 0
-        startipage = self.ipage - rewind
-        while (iipage < len(self.pages)) and not found:
-            ipage = (iipage+startipage) % len(self.pages)
-            for dim in args.keys():
-                this_file = True
-
-                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
-                if 'dims' not in self.__dict__:
-                    self.dims = {}
-                if dim not in self.dims.keys():
-                    self.dims[dim] = [None]*len(self.pages)
-
-                if self.dims[dim][ipage] is None:
-                    self.logger.info('Loading coordinates of dimension "'+dim+\
-                                     '" of page "' +str(ipage)+'".')
-                    self.set_page(ipage)
-                    # print(ipage)
-                    # print(dim)
-                    # print(dim,self.page[dim].values)
-                    self.dims[dim][ipage] = self.page[dim].values
-
-                # determine current time range of the current page
-                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
-                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
-
-                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
-                    this_file = False
-
-            if this_file:
-                found = True
-                self.set_page(ipage)
-            else:
-
-                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
-                #    iipage = len(self.pages) # we stop searching
-
-                iipage += 1
-
-        if not found:
-            self.logger.info("Page not found. Setting to page -1")
-            #iipage = len(self.pages) # we stop searching further
-            self.set_page(-1)
-
-        if self.ipage != -1:
-            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
-        else:
-            self.logger.debug("I'm now at page "+ str(self.ipage))
-
-
-class data_global(object):
-    def __init__(self,sources= {
-        # # old gleam
-        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
-        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
-        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
-        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
-        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
-        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
-        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
-        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
-        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
-        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
-        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
-        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
-        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
-        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
-        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
-        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
-        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
-        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
-        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
-        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
-        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
-        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
-        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
-        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
-        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
-        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
-        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
-        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
-        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
-        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
-        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
-        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
-        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
-        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
-        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
-        },debug_level=None):
-        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
-        self.sources = sources
-        self.datarefs = {}
-        self.datasets = {}
-        self.datetime = dt.datetime(1981,1,1)
-
-        self.logger = logging.getLogger('data_global')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-        self.debug_level = debug_level
-
-        warnings.warn('omitting pressure field p and advection')
-
-    def in_library(self,fn):
-        if fn not in self.library.keys():
-            return False
-        else:
-            print("Warning: "+fn+" is already in the library.")
-            return True
-
-    def add_to_library(self,fn):
-        if not self.in_library(fn):
-            print("opening: "+fn)
-            self.library[fn] = \
-                book(fn,concat_dim='time',debug_level=self.debug_level)
-
-            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
-            #if 'latitude' in self.library[fn].variables:
-            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-
-    # default procedure for loading datasets into the globaldata library
-    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
-        if type(varssource) is str:
-            varssource = [varssource]
-        if type(varsdest) is str:
-            varsdest = [varsdest]
-
-        self.add_to_library(input_fn)
-
-        if varssource is None:
-            varssource = []
-            for var in self.sources[input_fn].variables:
-                avoid = \
-                ['lat','lon','latitude','longitude','time','lev','level']
-                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
-                    varssource.append(var)
-
-        if varsdest is None:
-            varsdest = varssource
-
-        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        for ivar,vardest in enumerate(varsdest):
-            varsource = varssource[ivar]
-            print('setting '+vardest+' as '+varsource+' from '+input_fn)
-
-            if vardest in self.datarefs.keys():
-                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
-            #self.add_to_library(fn,varsource,vardest)
-            if vardest != varsource:
-                libkey = input_fn+'.'+varsource+'.'+vardest
-                if libkey not in self.library.keys():
-                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
-                    self.library[libkey] = book(input_fn,\
-                                                debug_level=self.debug_level)
-                    self.library[libkey].set_renames({varsource: vardest})
-
-                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-            else:
-                self.datarefs[vardest] = input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-
-            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
-            #     print('Warning: '+ vardest "not in " + input_fn)
-
-
-
-    def load_datasets(self,sources = None,recalc=0):
-
-        if sources is None:
-            sources = self.sources
-        for key in sources.keys():
-            #datakey,vardest,*args = key.split(':')
-            datakey,vardest = key.split(':')
-            #print(datakey)
-
-            fnvarsource = sources[key].split(':')
-            if len(fnvarsource) > 2:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource,fnargs = fnvarsource
-                fnargs = [fnargs]
-            elif len(fnvarsource) > 1:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource = fnvarsource
-                fnargs = []
-            else:
-                fn = sources[key]
-                varsource = vardest
-            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
-
-    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
-            # the default way of loading a 2d dataset
-            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
-                self.load_dataset_default(fn,varsource,vardest)
-            elif datakey == 'IGBPDIS':
-                if vardest == 'alpha':
-                    ltypes = ['W','B','H','TC']
-                    for ltype in ltypes:
-                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
-                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
-
-
-                    # landfr = {}
-                    # for ltype in ['W','B','H','TC']:
-                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
-
-
-
-                    keytemp = 'alpha'
-                    fnkeytemp = fn+':IGBPDIS:alpha'
-                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
-                        self.library[fnkeytemp]  = book(fnkeytemp,
-                                                        debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-                    else:
-                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
-                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
-                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
-                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
-                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
-                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
-                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
-
-                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-
-                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
-                        for ltype in ltypes:
-                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
-
-                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
-                        print('writing file to: '+fnkeytemp)
-                        os.system('rm '+fnkeytemp)
-                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
-                        self.library[fnkeytemp].close()
-
-
-                        self.library[fnkeytemp]  = \
-                            book(fnkeytemp,debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-
-
-                else:
-                    self.load_dataset_default(fn,varsource,vardest)
-
-
-            elif datakey == 'GLAS':
-                self.load_dataset_default(fn,varsource,vardest)
-                if vardest == 'z0m':
-                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
-                elif vardest == 'z0h':
-                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
-            elif datakey == 'DSMW':
-
-
-                # Procedure of the thermal properties:
-                # 1. determine soil texture from DSMW/10.
-                # 2. soil type with look-up table (according to DWD/EXTPAR)
-                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
-                #    with parameter look-up table from Noilhan and Planton (1989).
-                #    Note: The look-up table is inspired on DWD/COSMO
-
-                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
-
-
-
-                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
-                self.load_dataset_default(fn,'DSMW')
-                print('calculating texture')
-                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
-                TEMP  = {}
-                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
-                TEMP3 = {}
-                for SPKEY in SPKEYS:
-
-
-                    keytemp = SPKEY+'_values'
-                    fnoutkeytemp = fnout+':DSMW:'+keytemp
-                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                    else:
-                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
-                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
-                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-                        # for faster computation, we need to get it to memory out of Dask.
-                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
-                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
-
-                # yes, I know I only check the last file.
-                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
-                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
-                        print('idx',idx,SPKEY)
-                        SEL = (TEMP2 == idx)
-                    #     print(idx,len(TEMP3))
-                        for SPKEY in SPKEYS:
-                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
-
-                    for SPKEY in SPKEYS:
-                        keytemp = SPKEY+'_values'
-                        fnoutkeytemp = fnout+':DSMW:'+keytemp
-                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
-                        os.system('rm '+fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].close()
-
-
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                keytemp = 'texture'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-                else:
-                    self.library[fn+':DSMW:texture'] = xr.Dataset()
-                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
-                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
-                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
-                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-
-                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
-
-                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
-                    zundef[zundef < 0] = np.nan
-                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
-                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
-
-                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-
-
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-                print('calculating texture type')
-
-
-
-                keytemp = 'itex'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-                else:
-                    self.library[fnoutkeytemp] = xr.Dataset()
-                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-                    X = self.datasets['texture'].page['texture'].values*100
-                    X[pd.isnull(X)] = -9
-
-
-                    self.datasets[keytemp][keytemp].values = X
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
-                    self.datasets['itex'].close()
-
-
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-
-                keytemp = 'isoil'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                isoil_reprocessed = False
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-                else:
-                    isoil_reprocessed = True
-                    print('calculating soil type')
-                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    ITEX = self.datasets['itex'].page['itex'].values
-                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
-                    LOOKUP = [
-                              [-10 ,9],# ocean
-                              [0 ,7],# fine textured, clay (soil type 7)
-                              [20,6],# medium to fine textured, loamy clay (soil type 6)
-                              [40,5],# medium textured, loam (soil type 5)
-                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                              [80,3],# coarse textured, sand (soil type 3)
-                              [100,9],# coarse textured, sand (soil type 3)
-                            ]
-                    for iitex,iisoil in LOOKUP:
-                        ISOIL[ITEX > iitex] = iisoil
-                        print('iitex,iisoil',iitex,iisoil)
-
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    LOOKUP = [
-                              [9001, 1 ], # ice, glacier (soil type 1)
-                              [9002, 2 ], # rock, lithosols (soil type 2)
-                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                              [9,    9 ], # undefined (ocean)
-                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                              [9000, 9 ], # undefined (inland lake)
-                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                            ]
-                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
-
-                    CODE_VALUES[ITEX == 901200] = 9012
-                    for icode,iisoil in LOOKUP:
-                        ISOIL[CODE_VALUES == icode] = iisoil
-
-                    self.datasets['isoil']['isoil'].values = ISOIL
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-                    print('saved inbetween file to: '+fnoutkeytemp)
-
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                #adopted from data_soil.f90 (COSMO5.0)
-                SP_LOOKUP = {
-                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
-                  # (by index)                                           loam                    loam                                water      ice
-                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-                  # Important note: For peat, the unknown values below are set equal to that of loam
-                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
-                  #error in table 2 of NP89: values need to be multiplied by e-6
-                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
-
-                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
-                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
-                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
-                }
-
-
-                # isoil_reprocessed = False
-                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-
-                #     self.library[fn+':DSMW:isoil'] = \
-                #             book(fnoutkeytemp,debug_level=self.debug_level)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-                # else:
-                #     isoil_reprocessed = True
-                #     print('calculating soil type')
-                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-
-
-
-                # this should become cleaner in future but let's hard code it for now.
-                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
-                print('calculating soil parameter')
-                DATATEMPSPKEY = {}
-                if (recalc < 1) and (isoil_reprocessed == False): 
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        keytemp = SPKEY
-                        fnoutkeytemp=fnout+':DSMW:'+keytemp
-                        self.library[fn+':DSMW:'+SPKEY] =\
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
-                        self.datarefs[SPKEY] =fnoutkeytemp
-                else:
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-
-                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
-                    ISOIL = self.datasets['isoil'].page['isoil'].values
-                    print(np.where(ISOIL>0.))
-                    for i in range(11):
-                        SELECT = (ISOIL == i)
-                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
-
-                        os.system('rm '+fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].close()
-                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
-
-                        self.library[fn+':DSMW:'+SPKEY] = \
-                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-
-
-            else:
-                self.load_dataset_default(fn,varsource,vardest)
-
-
-
-
-
-
-#
-#                 # only print the last parameter value in the plot
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'cala'
-#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'crhoc'
-#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#     key = "CERES"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         CERES_start_date = dt.datetime(2000,3,1)
-#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-#
-#         var = 'cc'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#         print(class_settings.lat,class_settings.lon)
-#
-#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
-#
-#         input_nc.close()
-#
-
-
-#     key = "GIMMS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-#         print("Reading Leag Area Index from "+input_fn)
-#         var = 'LAI'
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-#
-#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-#
-#         if np.isnan(tarray[idatetime]):
-#             print("interpolating GIMMS cveg nan value")
-#
-#             mask = np.isnan(tarray)
-#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-#             else:
-#                 print("Warning. Could not interpolate GIMMS cveg nan value")
-#
-#         class_settings.__dict__[var] = tarray[idatetime]
-#
-#         input_nc.close()
-#
-#     key = "IGBPDIS_ALPHA"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         var = 'alpha'
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-#         print("Reading albedo from "+input_fn)
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#
-#         landfr = {}
-#         for ltype in ['W','B','H','TC']:
-#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-#
-#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-#
-#         alpha=0.
-#         for ltype in landfr.keys():
-#             alpha += landfr[ltype]*aweights[ltype]
-#
-#
-#         class_settings.__dict__[var] = alpha
-#         input_nc.close()
-#
-#
-#     key = "ERAINT_ST"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         print("Reading soil temperature from "+input_fn)
-#
-#         var = 'Tsoil'
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         var = 'T2'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-#
-#
-#         input_nc.close()
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #var = 'T2'
-#     #valold = class_settings.__dict__[var]
-#     #
-#     #class_settings.__dict__[var] = 305.
-#     #class_settings.__dict__['Tsoil'] = 302.
-#     #valnew = class_settings.__dict__[var]
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #
-#     #var = 'Lambda'
-#     #valold = class_settings.__dict__[var]
-#
-#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
-#     ## I need to ask Chiel.
-#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-#     #
-#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
-#     #class_settings.__dict__[var] = valnew
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     key = "GLAS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-#         print("Reading canopy height for determining roughness length from "+input_fn)
-#         var = 'z0m'
-#
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-#
-#         lowerlimit = 0.01
-#         if testval < lowerlimit:
-#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-#             class_settings.__dict__[var] = lowerlimit
-#         else:
-#             class_settings.__dict__[var] = testval
-#
-#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-#
-#
-#         input_nc.close()
-
-
-
-
-
diff --git a/build/lib/class4gl/interface_functions.py b/build/lib/class4gl/interface_functions.py
deleted file mode 100644
index 3e483f3..0000000
--- a/build/lib/class4gl/interface_functions.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-#from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-#'_afternoon.yaml'
-def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
-    filename = yaml_file.name
-    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-    #yaml_file = open(filename)
-
-    #print('going to next observation',filename)
-    yaml_file.seek(index_start)
-
-    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-
-    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-    filebuffer.write(buf)
-    filebuffer.close()
-    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-    
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-
-    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-    print(command)
-    os.system(command)
-    jsonstream = open(filename+'.buffer.json.'+str(index_start))
-    record_dict = json.load(jsonstream)
-    jsonstream.close()
-    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-
-
-    if mode =='mod':
-        modelout = class4gl()
-        modelout.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        return modelout
-    elif mode == 'ini':
-
- 
-        # datetimes are incorrectly converted to strings. We need to convert them
-        # again to datetimes
-        for key,value in record_dict['pars'].items():
-            # we don't want the key with columns that have none values
-            if value is not None: 
-                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
-               # elif (type(value) == str):
-                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-
-            if (value == 0.9e19) or (value == '.9e19'):
-                record_dict['pars'][key] = np.nan
-        for key in record_dict.keys():
-            #print(key)
-            if key in ['air_ap','air_balloon',]:
-                #NNprint('check')
-                for datakey,datavalue in record_dict[key].items():
-                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-
-        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        c4gli = class4gl_input()
-        print(c4gli.logger,'hello')
-        c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-        return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-
-class stations(object):
-    def __init__(self,path,suffix='ini',refetch_stations=False):
-
-        self.path = path
-
-        self.file = self.path+'/stations_list.csv'
-        if (os.path.isfile(self.file)) and (not refetch_stations):
-            self.table = pd.read_csv(self.file)
-        else:
-            self.table = self.get_stations(suffix=suffix)
-            self.table.to_csv(self.file)
-        
-        self.table = self.table.set_index('STNID')
-        #print(self.table)
-
-    def get_stations(self,suffix):
-        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
-        if len(stations_list_files) == 0:
-            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
-        stations_list_files.sort()
-        print(stations_list_files)
-        if len(stations_list_files) == 0:
-            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
-        stations_list = []
-        for stations_list_file in stations_list_files:
-            thisfile = open(stations_list_file,'r')
-            yamlgen = yaml.load_all(thisfile)
-            try:
-                first_record  = yamlgen.__next__()
-            except:
-                first_record = None
-            if first_record is not None:
-                stations_list.append({})
-                for column in ['STNID','latitude','longitude']:
-                    #print(first_record['pars'].keys())
-                    stations_list[-1][column] = first_record['pars'][column]
-                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
-            yamlgen.close()
-            thisfile.close()
-    
-        print(stations_list)
-        return pd.DataFrame(stations_list)
-
-class stations_iterator(object):
-    def __init__(self,stations):
-        self.stations = stations
-        self.ix = -1 
-    def __iter__(self):
-        return self
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.stations.table)) 
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_row(self,row):
-        self.ix = row
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_STNID(self,STNID):
-        self.ix = np.where((self.stations.table.index == STNID))[0][0]
-        print(self.ix)
-        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-    def close():
-        del(self.ix)
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.records))
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-# #'_afternoon.yaml'
-# def get_record_yaml(yaml_file,index_start,index_end):
-#     filename = yaml_file.name
-#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-#     #yaml_file = open(filename)
-# 
-#     #print('going to next observation',filename)
-#     yaml_file.seek(index_start)
-# 
-#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-# 
-#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-#     filebuffer.write(buf)
-#     filebuffer.close()
-#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-#     
-#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-# 
-#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-#     print(command)
-#     os.system(command)
-#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
-#     record_dict = json.load(jsonstream)
-#     jsonstream.close()
-#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-#  
-#     # datetimes are incorrectly converted to strings. We need to convert them
-#     # again to datetimes
-#     for key,value in record_dict['pars'].items():
-#         # we don't want the key with columns that have none values
-#         if value is not None: 
-#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
-#            # elif (type(value) == str):
-#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-#                 
-#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
-# 
-#         if (value == 0.9e19) or (value == '.9e19'):
-#             record_dict['pars'][key] = np.nan
-#     for key in record_dict.keys():
-#         print(key)
-#         if key in ['air_ap','air_balloon',]:
-#             print('check')
-#             for datakey,datavalue in record_dict[key].items():
-#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-# 
-#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-# 
-#     c4gli = class4gl_input()
-#     c4gli.load_yaml_dict(record_dict)
-#     return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
-
-    records = pd.DataFrame()
-    for STNID,station in stations.iterrows():
-        dictfnchunks = []
-        if getchunk is 'all':
-
-            # we try the old single-chunk filename format first (usually for
-            # original profile pairs)
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
-            if os.path.isfile(fn):
-                chunk = 0
-                dictfnchunks.append(dict(fn=fn,chunk=chunk))
-
-            # otherwise, we use the new multi-chunk filename format
-            else:
-                chunk = 0
-                end_of_chunks = False
-                while not end_of_chunks:
-                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(fn):
-                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
-                    else:
-                        end_of_chunks = True
-                    chunk += 1
-
-            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
-            # yamlfilenames = glob.glob(globyamlfilenames)
-            # yamlfilenames.sort()
-        else:
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
-            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
-            
-        if len(dictfnchunks) > 0:
-            for dictfnchunk in dictfnchunks:
-                yamlfilename = dictfnchunk['fn']
-                chunk = dictfnchunk['chunk']
-                print(chunk)
-
-                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
-                pklfilename = yamlfilename.replace('.yaml','.pkl')
-
-                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
-                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
-                generate_pkl = False
-                if not os.path.isfile(pklfilename): 
-                    print('pkl file does not exist. I generate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                elif not (os.path.getmtime(yamlfilename) <  \
-                    os.path.getmtime(pklfilename)):
-                    print('pkl file older than yaml file, so I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-
-                if refetch_records:
-                    print('refetch_records flag is True. I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(pklfilename)])
-                   # irecord = 0
-                else:
-                    with open(yamlfilename) as yaml_file:
-
-                        dictout = {}
-
-                        next_record_found = False
-                        end_of_file = False
-                        while (not next_record_found) and (not end_of_file):
-                            linebuffer = yaml_file.readline()
-                            next_record_found = (linebuffer == '---\n')
-                            end_of_file = (linebuffer == '')
-                        next_tell = yaml_file.tell()
-                        
-                        while not end_of_file:
-
-                            print(' next record:',next_tell)
-                            current_tell = next_tell
-                            next_record_found = False
-                            yaml_file.seek(current_tell)
-                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
-                            linebuffer = ''
-                            while ( (not next_record_found) and (not end_of_file)):
-                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
-                                linebuffer = yaml_file.readline()
-                                next_record_found = (linebuffer == '---\n')
-                                end_of_file = (linebuffer == '')
-                            filebuffer.close()
-                            
-                            next_tell = yaml_file.tell()
-                            index_start = current_tell
-                            index_end = next_tell
-
-                            
-                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                            print(command)
-                            
-                            os.system(command)
-                            #jsonoutput = subprocess.check_output(command,shell=True) 
-                            #print(jsonoutput)
-                            #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
-                            record = json.load(jsonstream)
-                            dictouttemp = {}
-                            for key,value in record['pars'].items():
-                                # we don't want the key with columns that have none values
-                                if value is not None: 
-                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
-                                   if (type(value) in regular_numeric_types):
-                                        dictouttemp[key] = value
-                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
-                            recordindex = record['index']
-                            dictouttemp['chunk'] = chunk
-                            dictouttemp['index_start'] = index_start
-                            dictouttemp['index_end'] = index_end
-                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
-                            for key,value in dictouttemp.items():
-                                if key not in dictout.keys():
-                                    dictout[key] = {}
-                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
-                            print(' obs record registered')
-                            jsonstream.close()
-                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
-                    records_station = pd.DataFrame.from_dict(dictout)
-                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+pklfilename+') for station '\
-                          +str(STNID))
-                    records_station.to_pickle(pklfilename)
-                    # else:
-                    #     os.system('rm '+pklfilename)
-                    records = pd.concat([records,records_station])
-    return records
-
-def stdrel(mod,obs,columns):
-    stdrel = pd.DataFrame(columns = columns)
-    for column in columns:
-        stdrel[column] = \
-                (mod.groupby('STNID')[column].transform('mean') -
-                 obs.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') + \
-                (mod[column] -
-                 mod.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') 
-    return stdrel
-
-def pct(obs,columns):
-    pct = pd.DataFrame(columns=columns)
-    for column in columns:
-        #print(column)
-        pct[column] = ""
-        pct[column] = obs[column].rank(pct=True)
-    return pct
-
-def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
-    stats = pd.DataFrame()
-    for key in keys: 
-        stats['d'+key+'dt'] = ""
-        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
-                              (obs_afternoon.ldatetime - \
-                               obs_morning.ldatetime).dt.seconds*3600.
-    return stats
-
diff --git a/build/lib/class4gl/interface_multi.py b/build/lib/class4gl/interface_multi.py
deleted file mode 100644
index 83148e5..0000000
--- a/build/lib/class4gl/interface_multi.py
+++ /dev/null
@@ -1,2061 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-# from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-cdictpres = {'blue': (\
-                   (0.,    0.,  0.),
-                   (0.25,  0.25, 0.25),
-                   (0.5,  .70, 0.70),
-                   (0.75, 1.0, 1.0),
-                   (1,     1.,  1.),
-                   ),
-       'green': (\
-                   (0. ,   0., 0.0),
-                   (0.25,  0.50, 0.50),
-                   (0.5,  .70, 0.70),
-                   (0.75,  0.50, 0.50),
-                   (1  ,    0,  0.),
-                   ),
-       'red':  (\
-                  (0 ,  1.0, 1.0),
-                  (0.25 ,  1.0, 1.0),
-                   (0.5,  .70, 0.70),
-                  (0.75 , 0.25, 0.25),
-                  (1,    0., 0.),
-                  )}
-
-statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-os.system('module load Ruby')
-
-class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
-        """ creates an interactive interface for analysing class4gl experiments
-
-        INPUT:
-            path_exp : path of the experiment output
-            path_obs : path of the observations 
-            globaldata: global data that is being shown on the map
-            refetch_stations: do we need to build the list of the stations again?
-        OUTPUT:
-            the procedure returns an interface object with interactive plots
-
-        """
-        
-        # set the ground
-        self.globaldata = globaldata
-
- 
-        self.path_exp = path_exp
-        self.path_obs = path_obs
-        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
-
-        # # get the list of stations
-        # stationsfile = self.path_exp+'/stations_list.csv'
-        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
-        #     stations = pd.read_csv(stationsfile)
-        # else:
-        #     stations = get_stations(self.path_exp)
-        #     stations.to_csv(stationsfile)
-
-        # stations = stations.set_index('STNID')
-
-        self.frames = {}
-
-        self.frames['stats'] = {}
-        self.frames['worldmap'] = {}
-                
-        self.frames['profiles'] = {}
-        self.frames['profiles'] = {}
-        self.frames['profiles']['DT'] = None
-        self.frames['profiles']['STNID'] = None
-
-        #self.frames['worldmap']['stationsfile'] = stationsfile
-        self.frames['worldmap']['stations'] = stations(self.path_exp, \
-                                                       suffix='ini',\
-                                                       refetch_stations=refetch_stations)
-
-        # Initially, the stats frame inherets the values/iterators of
-        # worldmap
-        for key in self.frames['worldmap'].keys():
-            self.frames['stats'][key] = self.frames['worldmap'][key]
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_ini'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='ini',\
-                                           refetch_records=refetch_records
-                                           )
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_mod'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='mod',\
-                                           refetch_records=refetch_records
-                                           )
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_obs_afternoon'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_obs,\
-                                           subset='afternoon',\
-                                           refetch_records=refetch_records
-                                           )
-
-        self.frames['stats']['records_all_stations_mod'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['records_all_stations_ini']['dates'] = \
-            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
-
-
-        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
-
-        self.frames['stats']['records_all_stations_obs_afternoon'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['viewkeys'] = ['h','theta','q']
-        print('Calculating table statistics')
-        self.frames['stats']['records_all_stations_mod_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_mod'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-
-        self.frames['stats']['inputkeys'] = inputkeys
-        
-        # self.frames['stats']['inputkeys'] = \
-        #     [ key for key in \
-        #       self.globaldata.datasets.keys() \
-        #       if key in \
-        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
-
-
-        # get units from the class4gl units database
-        self.units = dict(units)
-        # for those that don't have a definition yet, we just ask a question
-        # mark
-        for var in self.frames['stats']['inputkeys']:
-            self.units[var] = '?'
-
-        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
-        self.frames['stats']['records_all_stations_ini_pct'] = \
-                  pct(self.frames['stats']['records_all_stations_ini'], \
-                      columns = self.frames['stats']['inputkeys'])
-
-        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
-        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-        #     mod['
-
-        # 
-        # 
-        # \
-        #        self.frames['stats']['records_all_stations_mod'], \
-
-
-
-        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
-        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
-        #               columns = [ 'd'+key+'dt' for key in \
-        #                           self.frames['stats']['viewkeys']], \
-        #              )
-
-        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
-        #               obs = self.frames['stats']['records_all_stations_ini'], \
-        #               columns = self.frames['stats']['viewkeys'], \
-        #              )
-        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
-        
-        print('filtering pathological data')
-        # some observational sounding still seem problematic, which needs to be
-        # investigated. In the meantime, we filter them
-        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
-
-        # we filter ALL data frames!!!
-        for key in self.frames['stats'].keys():
-            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
-               (self.frames['stats'][key].index.names == indextype):
-                self.frames['stats'][key] = self.frames['stats'][key][valid]
-        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
-
-        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
-
-
-        print("filtering stations from interface that have no records")
-        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
-            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                    == STNID).sum() == 0):
-                print("dropping", STNID)
-                self.frames['worldmap']['stations'].table = \
-                        self.frames['worldmap']['stations'].table.drop(STNID)
-                    
-        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-        
-        # TO TEST: should be removed, since it's is also done just below
-        self.frames['stats']['stations_iterator'] = \
-            self.frames['worldmap']['stations_iterator'] 
-
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
-        self.next_station()
-
-        # self.goto_datetime_worldmap(
-        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-        #     'after')
-    def sel_station(self,STNID=None,rownumber=None):
-
-        if (STNID is not None) and (rownumber is not None):
-            raise ValueError('Please provide either STNID or rownumber, not both.')
-
-        if (STNID is None) and (rownumber is None):
-            raise ValueError('Please provide either STNID or rownumber.')
-            
-        if STNID is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
-            print(
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-            )
-            self.update_station()
-        elif rownumber is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
-            self.update_station()
-
-
-
-    def next_station(self,event=None,jump=1):
-        with suppress(StopIteration):
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-                = self.frames['worldmap']['stations_iterator'].__next__(jump)
-            # self.frames['worldmap']['stations_iterator'].close()
-            # del(self.frames['worldmap']['stations_iterator'])
-            # self.frames['worldmap']['stations_iterator'] = \
-            #                 selfself.frames['worldmap']['stations'].iterrows()
-            # self.frames['worldmap']['STNID'],\
-            # self.frames['worldmap']['current_station'] \
-            #     = self.frames['worldmap']['stations_iterator'].__next__()
-
-        self.update_station()
-
-    def prev_station(self,event=None):
-        self.next_station(jump = -1,event=event)
-    def update_station(self):
-        for key in ['STNID','current_station','stations_iterator']: 
-            self.frames['stats'][key] = self.frames['worldmap'][key] 
-
-
-
-        # generate index of the current station
-        self.frames['stats']['records_current_station_index'] = \
-            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-             == \
-             self.frames['stats']['current_station'].name)
-
-        # create the value table of the records of the current station
-        tab_suffixes = \
-                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        for tab_suffix in tab_suffixes:
-            self.frames['stats']['records_current_station'+tab_suffix] = \
-                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-        # go to first record of current station
-        self.frames['stats']['records_iterator'] = \
-                        records_iterator(self.frames['stats']['records_current_station_mod'])
-        (self.frames['stats']['STNID'] , \
-        self.frames['stats']['current_record_chunk'] , \
-        self.frames['stats']['current_record_index']) , \
-        self.frames['stats']['current_record_mod'] = \
-                        self.frames['stats']['records_iterator'].__next__()
-
-        for key in self.frames['stats'].keys():
-            self.frames['profiles'][key] = self.frames['stats'][key]
-
-        STNID = self.frames['profiles']['STNID']
-        chunk = self.frames['profiles']['current_record_chunk']
-        if 'current_station_file_ini' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_ini'].close()
-        self.frames['profiles']['current_station_file_ini'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-        if 'current_station_file_mod' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_mod'].close()
-        self.frames['profiles']['current_station_file_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_afternoon'].close()
-        self.frames['profiles']['current_station_file_afternoon'] = \
-            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-        self.frames['profiles']['records_iterator'] = \
-                        records_iterator(self.frames['profiles']['records_current_station_mod'])
-        (self.frames['profiles']['STNID'] , \
-        self.frames['profiles']['current_record_chunk'] , \
-        self.frames['profiles']['current_record_index']) , \
-        self.frames['profiles']['current_record_mod'] = \
-                        self.frames['profiles']['records_iterator'].__next__()
-
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-
-        self.update_record()
-
-    def next_record(self,event=None,jump=1):
-        with suppress(StopIteration):
-            (self.frames['profiles']['STNID'] , \
-            self.frames['profiles']['current_record_chunk'] , \
-            self.frames['profiles']['current_record_index']) , \
-            self.frames['profiles']['current_record_mod'] = \
-                      self.frames['profiles']['records_iterator'].__next__(jump)
-        # except (StopIteration):
-        #     self.frames['profiles']['records_iterator'].close()
-        #     del( self.frames['profiles']['records_iterator'])
-        #     self.frames['profiles']['records_iterator'] = \
-        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
-        #     (self.frames['profiles']['STNID'] , \
-        #     self.frames['profiles']['current_record_index']) , \
-        #     self.frames['profiles']['current_record_mod'] = \
-        #                     self.frames['profiles']['records_iterator'].__next__()
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        self.update_record()
-
-    def prev_record(self,event=None):
-        self.next_record(jump=-1,event=event)
-
-    def update_record(self):
-        self.frames['profiles']['current_record_ini'] =  \
-            self.frames['profiles']['records_current_station_ini'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'],\
-                  self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon'] =  \
-            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'] , \
-                  self.frames['profiles']['current_record_index'])]
-
-        self.frames['profiles']['current_record_mod_stats'] = \
-                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
-                    self.frames['profiles']['STNID'], \
-                    self.frames['profiles']['current_record_chunk'], \
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_ini_pct'] = \
-                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        # frame
-        # note that the current station, record is the same as the stats frame for initialization
-
-        # select first 
-        #self.frames['profiles']['current_record_index'], \
-        #self.frames['profiles']['record_yaml_mod'] = \
-        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
-        #                   self.frames['stats']['current_record_index'])
-        self.frames['profiles']['record_yaml_mod'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_mod'], \
-               self.frames['profiles']['current_record_mod'].index_start,
-               self.frames['profiles']['current_record_mod'].index_end,
-               mode='mod')
-                                
-        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_ini'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_ini'], \
-               record_ini.index_start,
-               record_ini.index_end,
-                mode='ini')
-
-        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_obs_afternoon'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_afternoon'], \
-               record_afternoon.index_start,
-               record_afternoon.index_end,
-                mode='ini')
-
-
-        key = self.frames['worldmap']['inputkey']
-        # only redraw the map if the current world map has a time
-        # dimension
-        if 'time' in self.globaldata.datasets[key].page[key].dims:
-            self.goto_datetime_worldmap(
-                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                'after')
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap',
-                                                  'profiles'])
-        else:
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap_stations',
-                                                  'profiles'])
-
-    def abline(self,slope, intercept,axis):
-        """Plot a line from slope and intercept"""
-        #axis = plt.gca()
-        x_vals = np.array(axis.get_xlim())
-        y_vals = intercept + slope * x_vals
-        axis.plot(x_vals, y_vals, 'k--')
-
-    def plot(self):
-        import pylab as pl
-        from matplotlib.widgets import Button
-        import matplotlib.pyplot as plt
-        import matplotlib as mpl
-        '''
-        Definition of the axes for the sounding table stats
-        '''
-        
-        fig = pl.figure(figsize=(14,9))
-        axes = {} #axes
-        btns = {} #buttons
-
-        # frames, which sets attributes for a group of axes, buttens, 
-        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
-            label = 'stats_'+str(key)
-            axes[label] = fig.add_subplot(\
-                            len(self.frames['stats']['viewkeys']),\
-                            5,\
-                            5*ikey+1,label=label)
-            # Actually, the axes should be a part of the frame!
-            #self.frames['stats']['axes'] = axes[
-
-            # pointer to the axes' point data
-            axes[label].data = {}
-
-            # pointer to the axes' color fields
-            axes[label].fields = {}
-
-
-        fig.tight_layout()
-        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
-
-        label ='stats_colorbar'
-        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
-        axes[label].fields = {}
-
-        from matplotlib.colors import LinearSegmentedColormap
-        cdictpres = {'blue': (\
-                           (0.,    0.,  0.),
-                           (0.25,  0.25, 0.25),
-                           (0.5,  .70, 0.70),
-                           (0.75, 1.0, 1.0),
-                           (1,     1.,  1.),
-                           ),
-               'green': (\
-                           (0. ,   0., 0.0),
-                           (0.25,  0.50, 0.50),
-                           (0.5,  .70, 0.70),
-                           (0.75,  0.50, 0.50),
-                           (1  ,    0,  0.),
-                           ),
-               'red':  (\
-                          (0 ,  1.0, 1.0),
-                          (0.25 ,  1.0, 1.0),
-                           (0.5,  .70, 0.70),
-                          (0.75 , 0.25, 0.25),
-                          (1,    0., 0.),
-                          )}
-        
-        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-        label = 'times'
-               
-        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-
-
-        label = 'worldmap'
-               
-        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-        axes[label].lat = None
-        axes[label].lon = None
-
-        label = 'worldmap_colorbar'
-        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
-        axes[label].fields = {}
-
-        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
-        label = 'worldmap_stations'
-        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
-        axes[label].data = {}
-
-        fig.canvas.mpl_connect('pick_event', self.on_pick)
-        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
-
-
-        """ buttons definitions """
-        
-        label = 'bprev_dataset'
-        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous dataset')
-        btns[label].on_clicked(self.prev_dataset)
-
-        label = 'bnext_dataset'
-        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next dataset')
-        btns[label].on_clicked(self.next_dataset)
-
-        label = 'bprev_datetime'
-        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous datetime')
-        btns[label].on_clicked(self.prev_datetime)
-
-        label = 'bnext_datetime'
-        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next datetime')
-        btns[label].on_clicked(self.next_datetime)
-
-
-        label = 'bprev_station'
-        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous station')
-        btns[label].on_clicked(self.prev_station)
-
-        label = 'bnext_station'
-        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next station')
-        btns[label].on_clicked(self.next_station)
-
-        label = 'bprev_record'
-        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous record')
-        btns[label].on_clicked(self.prev_record)
-
-        label = 'bnext_record'
-        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next record')
-        btns[label].on_clicked(self.next_record)
-
-
-        # self.nstatsview = nstatsview
-        # self.statsviewcmap = statsviewcmap
-        self.fig = fig
-        self.axes = axes
-        self.btns = btns
-        self.tbox = {}
-        # self.hover_active = False
-
-        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
-        #                                transform=plt.gcf().transFigure)
-
-        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
-                                          transform=plt.gcf().transFigure)
-
-        label = 'air_ap:theta'
-        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
-
-        label = 'air_ap:q'
-        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
-
-        label = 'out:h'
-        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
-
-        label = 'out:theta'
-        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
-
-        label = 'out:q'
-        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
-
-        label = 'SEB'
-        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
-
-
-        self.hover_active = False
-        self.fig = fig
-        self.fig.show()
-        self.fig.canvas.draw()
-        self.refresh_plot_interface()
-
-
-    # def scan_stations(self):
-    #     blabla
-        
-
-
-    # def get_records(current_file):
-    #     records = pd.DataFrame()
-
-    #     # initial position
-    #     next_record_found = False
-    #     while(not next_record_found):
-    #         next_record_found = (current_file.readline() == '---\n')
-    #     next_tell = current_file.tell() 
-    #     end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #     while not end_of_file:
-    #         current_tell = next_tell
-    #         next_record_found = False
-    #         current_file.seek(current_tell)
-    #         while ( (not next_record_found) and (not end_of_file)):
-    #             current_line = current_file.readline()
-    #             next_record_found = (currentline == '---\n')
-    #             end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #         # we store the position of the next record
-    #         next_tell = current_file.tell() 
-    #         
-    #         # we get the current record. Unfortunately we need to reset the
-    #         # yaml record generator first.
-    #         current_yamlgen.close()
-    #         current_yamlgen = yaml.load_all(current_file)
-    #         current_file.seek(current_tell)
-    #         current_record_mod = current_yamlgen.__next__()
-    #     current_yamlgen.close()
-
-    #     return records
-
-       #      next_record_found = False
-       #      while(not record):
-       #          next_record_found = (self.current_file.readline() == '---\n')
-       #      self.current_tell0 = self.current_file.tell() 
-
-       #  
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell0 = self.current_file.tell() 
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell1 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell0)
-       #  self.r0 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell1)
-       #  next_record_found = False
-       #  while ( (not next_record_found) and (not end_of_file):
-       #      current_line = self.current_file.readline()
-       #      next_record_found = (currentline == '---\n')
-       #      end_of_file = (currentline == '') # an empty line means we are at the end
-
-       #  self.current_tell2 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell1)
-       #  self.r1 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell2)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell3 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell2)
-       #  self.r2 = self.current_yamlgen.__next__()
-
-       #  # go to position of next record in file
-       #  self.current_file.seek(self.current_tell3)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell4 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell3)
-       #  self.r3 = self.current_yamlgen.__next__()
- 
-       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
-
-    def goto_datetime_worldmap(self,DT,shift=None):
-        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
-            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
-            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
-            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
-                self.frames['worldmap']['iDT'] += 1
-            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
-                self.frames['worldmap']['iDT'] -= 1 
-            # for gleam, we take the values of the previous day
-            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
-                self.frames['worldmap']['iDT'] -= 2 
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-        #else:
-        #    self.frames['worldmap'].pop('DT')
-
-    def next_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def prev_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def next_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-    def prev_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-
-
-    def sel_dataset(self,inputkey):
-        self.frames['worldmap']['inputkey'] = inputkey
-        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
-        self.goto_datetime_worldmap(
-            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-            'after')# get nearest datetime of the current dataset to the profile
-        if "fig" in self.__dict__.keys():
-            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
-       
-    # def prev_station(self,event=None):
-    #     self.istation = (self.istation - 1) % self.stations.shape[0]
-    #     self.update_station()
-
-
-
-
-    #def update_datetime(self):
-    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
-    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
-    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
-    #        print(self.worldmapfocus['DT'])
-    #        self.refresh_plot_interface(only='worldmap')
-
-    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
-
-        #print('r1')
-        for argkey in args.keys():
-            self.__dict__[arg] = args[argkey]
-
-        axes = self.axes
-        tbox = self.tbox
-        frames = self.frames
-        fig = self.fig
- 
-        if (only is None) or ('worldmap' in only):
-            globaldata = self.globaldata
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
-            else:
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
-            keystotranspose = ['lat','lon']
-            for key in dict(datasetxr.dims).keys():
-                if key not in keystotranspose:
-                    keystotranspose.append(key)
-
-            datasetxr = datasetxr.transpose(*keystotranspose)
-            datasetxr = datasetxr.sortby('lat',ascending=False)
-
-            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
-            lonleft = lonleft - 360.
-            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
-            label = 'worldmap'
-            axes[label].clear()
-            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
-            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
-
-        if (only is None) or ('worldmap' in only):
-            #if 'axmap' not in self.__dict__ :
-            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
-            #else:
-
-            #stations = self.stations
-
-
-            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
-            #     resolution = 'l', 
-            # area_thresh = 0.1,
-            #     llcrnrlon=-180., llcrnrlat=-90.0,
-            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
-            # 
-            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
-            # self.gmap.drawcountries(color='white',linewidth=0.3)
-            # #self.gmap.fillcontinents(color = 'gray')
-            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
-            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
-            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
-            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # #self.ax5.shadedrelief()
-
-           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
-
-
-            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
-            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
-
-            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
-            if 'lev' in field.dims:
-                field = field.isel(lev=-1)
-
-            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
-            axes[label].axis('off')
-
-            from matplotlib import cm
-            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
-            
-            
-            title=frames['worldmap']['inputkey']
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
-            axes[label].set_title(title)
-
-            label ='worldmap_colorbar'
-            axes[label].clear()
-            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
-
-
-            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
-            # x,y = self.gmap(lons,lats)
-            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
-            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
-
-        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
-
-            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
-            store_xlim = {}
-            store_ylim = {}
-            for ikey, key in enumerate(statskeys_out):
-                if (only is not None) and ('stats_lightupdate' in only):
-                    store_xlim[key] = axes['stats_'+key].get_xlim()
-                    store_ylim[key] = axes['stats_'+key].get_ylim()
-                self.axes['stats_'+key].clear()    
-
-            label = 'times'
-            self.axes[label].clear()
-
-            key = 'dthetadt'
-            x = self.frames['stats']['records_all_stations_ini']['datetime']
-            #print(x)
-            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-            #print(y)
-            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            #print(z)
-
-            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
-            self.axes[label].data[label] = self.axes[label].scatter(x.values,
-                                                                    y.values,
-                                                                    c=z.values,
-                                                                    cmap=self.statsviewcmap,
-                                                                    s=2,
-                                                                    vmin=0.,
-                                                                    vmax=1.,
-                                                                    alpha=alpha_cloud_pixels)
-
-            
-            x = self.frames['stats']['records_current_station_ini']['datetime']
-            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-
-            x = self.frames['profiles']['records_current_station_ini']['datetime']
-            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
-            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
-
-            for ikey, key in enumerate(statskeys_out):
-
-                # show data of all stations
-                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_all_stations_mod_stats'][key]
-                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                qvalmax = x.quantile(0.999)
-                qvalmin = x.quantile(0.001)
-                print('applying extra filter over extreme values for plotting stats')
-                selx = (x >= qvalmin) & (x < qvalmax)
-                sely = (x >= qvalmin) & (x < qvalmax)
-                x = x[selx & sely]
-                y = y[selx & sely]
-                z = z[selx & sely]
-                self.axes['stats_'+key].data['stats_'+key] = \
-                       self.axes['stats_'+key].scatter(x,y, c=z,\
-                                cmap=self.statsviewcmap,\
-                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
-
-                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_current_station_mod_stats'][key]
-                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['profiles']['records_current_station_mod_stats'][key]
-                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
-
-                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
-                y = self.frames['stats']['current_record_mod_stats'][key]
-                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
-                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
-                    axes['stats_'+key].annotate(text, \
-                                               xy=(x,y),\
-                                               xytext=(0.05,0.05),\
-                                               textcoords='axes fraction',\
-                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
-                                               color='white',\
-                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
-                # self.axes['stats_'+key].data[key+'_current_record'] = \
-                #        self.axes['stats_'+key].scatter(x,y, c=z,\
-                #                 cmap=self.statsviewcmap,\
-                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
-
-                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
-                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
-                # # highlight data for curent station
-                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
-
-                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
-
-                if ikey == len(statskeys_out)-1:
-                    self.axes['stats_'+key].set_xlabel('external')
-                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
-                axes['stats_'+key].set_ylabel('model')
-
-
-                if (only is not None) and ('stats_lightupdate' in only):
-                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
-                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
-                else:
-                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
-                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
-                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
-                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
-                self.abline(1,0,axis=self.axes['stats_'+key])
-
-        if (only is None) or ('stats_colorbar' in only):
-            label ='stats_colorbar'
-            axes[label].clear()
-            import matplotlib as mpl
-            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
-            self.axes[label].fields[label] = \
-             mpl.colorbar.ColorbarBase(self.axes[label],\
-                        orientation='horizontal',\
-                        label="percentile of "+self.frames['worldmap']['inputkey'],
-                        alpha=1.,
-                                cmap=self.statsviewcmap,\
-                                       norm=norm
-                         )
-
-        #print('r1')
-        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
-            #print('r2')
-            label = 'worldmap_stations'
-            axes[label].clear()
-            
-            stations = self.frames['worldmap']['stations'].table
-            globaldata = self.globaldata
-            
-            key = label
-
-            #print('r3')
-            if (stations is not None):
-                xlist = []
-                ylist = []
-                #print('r4')
-                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
-            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    xlist.append(x)
-                    ylist.append(y)
-                #picker is needed to make it clickable (pick_event)
-                axes[label].data[label] = axes[label].scatter(xlist,ylist,
-                                                              c='r', s=15,
-                                                              picker = 15,
-                                                              label=key,
-                                                              edgecolor='k',
-                                                              linewidth=0.8)
-
-            # cb.set_label('Wilting point [kg kg-3]')
-                #print('r5')
-
-                
-            #     xseries = []
-            #     yseries = []
-            #     for iSTN,STN in stations.iterrows():
-            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
-            #         xseries.append(x)                    
-            #         yseries.append(y)
-            #         
-            #         
-            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
-                    
-                if ('current_station' in frames['worldmap']):
-                    #print('r5')
-                    STN = frames['stats']['current_station']
-                    STNID = frames['stats']['STNID']
-                    #print('r5')
-
-                    x,y = len(axes['worldmap'].lon)* \
-                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
-                          len(axes['worldmap'].lat)* \
-                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    #print('r6')
-                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
-                    #                          == \
-                    #                          self.frames['worldmap']['STNID'])\
-                    #                         & \
-                    #                         (self.seltablestats['DT'] \
-                    #                          == self.axes['statsview0].focus['DT']) \
-                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
-                    #print('r7')
-                    text = 'STNID: '+ format(STNID,'10.0f') + \
-                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
-                            ', LON: '+format(STN['longitude'],'3.3f')+ \
-                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
-
-                            #+', VAL: '+format(VAL,'.3e')
-
-                    axes[label].scatter(x,y, c='r', s=30,\
-                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
-                    #print('r8')
-            
-                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
-                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
-                    #colorstation = max((min((1.,colorstation)),0.))
-                    colorstation =0.2
-                    from matplotlib import cm
-                    axes[label].annotate(text,
-                                         xy=(x,y),
-                                         xytext=(0.05,0.05),
-                                         textcoords='axes fraction', 
-                                         bbox=dict(boxstyle="round",
-                                         fc = cm.viridis(colorstation)),
-                                         arrowprops=dict(arrowstyle="->",
-                                                         linewidth=1.1),
-                                         color='white' if colorstation < 0.5 else 'black')
-                    #print('r9')
-
-                    # #pos = sc.get_offsets()[ind["ind"][0]]
-                    # 
-                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
-                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
-                    # axes[label].data[label+'statannotate'].set_text(text)
-                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
-                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
-            #print('r9')
-            axes[label].axis('off')
-            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
-            axes[label].set_ylim((len(axes['worldmap'].lat),0))
-            #print('r10')
-
-        if (only is None) or ('profiles' in only): 
-            #print('r11')
-
-            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
-            # # self.update_station(goto_first_sounding=False)
-            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
-            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
-            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
-            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
-
-            label = 'air_ap:theta'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-                # +\
-                # ' -> '+ \
-                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-            
-            
-            
-            
-            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-            #print('r12')
-
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
-            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
-            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
-            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            #print('r13')
-            # 
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r14')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-
-            #print('r15')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-                          
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r16')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r17')
-            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
-            print(hmax)
-            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
-            if valid_mod:
-
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="mod "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                                 +'LT')
-
-            #print('r18')
-            axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('theta [K]')
-
-            label = 'air_ap:q'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-
-            #print('r19')
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            if valid_mod:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            else:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            # 
-            #print('r20')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r21')
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            #print('r23')
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r24')
-            if valid_mod:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="fit ")#+\
-                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
-                             #+'LT')
-            #print('r25')
-            #axes[label].legend()
-
-            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            #axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('q [kg/kg]')
-
-            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-
-            # #pl.subplots_adjust(right=0.6)
-
-            # label = 'q_pro'
-            # axes[label].clear()
-
-            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
-            # 
-            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
-            # 
-            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
-
-            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
-            # #pl.subplots_adjust(right=0.6)
-            # axes[label].set_xlabel('specific humidity [kg/kg]')
- 
-
-            #print('r26')
-            time = self.frames['profiles']['record_yaml_mod'].out.time
-            for ilabel,label in enumerate(['h','theta','q']):
-                axes["out:"+label].clear()
-                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
-                axes["out:"+label].set_ylabel(label)
-                if ilabel == 2:
-                    axes["out:"+label].set_xlabel('local sun time [h]')
-                
-            #print('r27')
-            label = 'SEB'
-            axes[label].clear()
-            
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
-            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
-            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
-            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
-                
-            #print('r28')
-            
-            axes[label].legend()
-            
-            #         for ax in self.fig_timeseries_axes:
-#             ax.clear()
-#         
-#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
-#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
-#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
-#         #print(self.morning_sounding.c4gl.out.Swin)
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
-#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
-#         self.fig_timeseries_axes[3].legend()
-#         self.fig.canvas.draw()
-            
-
-
-
-
-
-
-        #self.ready()
-        #print('r29')
-        fig.canvas.draw()
-        #fig.show()
-
-        self.axes = axes
-        self.tbox = tbox
-        self.fig = fig
-
-    def on_pick(self,event):
-        #print("HELLO")
-        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
-        #self.axes['theta_pro'].clear()
-        #self.axes['q_pro'].clear()
-        
-
-        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
-        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
-        keys_to_axes = {}
-        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
-
-        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
-        keys_to_axes['worldmap'] = 'worldmap'
-        
-        axes = self.axes
-        #nstatsview = self.nstatsview
-        #statsviewcmap = self.statsviewcmap
-        stations = self.frames['worldmap']['stations'].table
-
-
-        #print("p1")
-        current = event
-        artist = event.artist
-        
-        selkey = artist.get_label()
-        
-        #print(keys_to_axes)
-        
-        label = keys_to_axes[selkey]
-        #print("HELLO",selkey,label)
-
-        # # Get to know in which axes we are
-        # label = None
-        # for axeskey in axes.keys():
-        #     if event.inaxes == axes[axeskey]:
-        #         label = axeskey
-        #         
-
-        # cont, pos = None, None
-        
-        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
-        ind = event.ind
-        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
-        d = axes[label].collections[0]
-        #d.set_offset_position('data')
-        xy = d.get_offsets()
-        x, y =  xy[:,0],xy[:,1]
-        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
-
-        #print("p2")
-        if len(ind) > 0:
-            #print("p3")
-            pos = x[ind[0]], y[ind[0]]
-
-            #if label[:-1] == 'statsview':
-            #    #seltablestatsstdrel = self.seltablestatsstdrel
-            #    #seltablestatspct = self.seltablestatspct
-
-            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-            #    
-            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
-            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-            #    
-            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
-            #el
-            if (label == 'worldmap') or (label == 'worldmap_stations'):
-                self.hover_active = False
-                if (self.frames['worldmap']['STNID'] !=
-                    self.frames['profiles']['STNID']):
-                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
-                # so we just need to perform update_station
-                    self.update_station()
-            elif (label[:5] == 'stats'):
-
-                self.hover_active = False
-                if (self.frames['stats']['STNID'] !=
-                self.frames['profiles']['STNID']) or \
-                   (self.frames['stats']['current_record_chunk'] != 
-                    self.frames['profiles']['current_record_chunk']) or \
-                   (self.frames['stats']['current_record_index'] != 
-                    self.frames['profiles']['current_record_index']):
-
-
-
-                    for key in ['STNID','current_station','stations_iterator']: 
-                        self.frames['worldmap'][key] = self.frames['stats'][key] 
-
-                    for key in self.frames['stats'].keys():
-                        self.frames['profiles'][key] = self.frames['stats'][key]
-
-                    STNID = self.frames['profiles']['STNID']
-                    chunk = self.frames['profiles']['current_record_chunk']
-                    if 'current_station_file_ini' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_ini'].close()
-                    self.frames['profiles']['current_station_file_ini'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-                    if 'current_station_file_mod' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_mod'].close()
-                    self.frames['profiles']['current_station_file_mod'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_afternoon'].close()
-                    self.frames['profiles']['current_station_file_afternoon'] = \
-                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-                    # go to hovered record of current station
-                    self.frames['profiles']['records_iterator'] = \
-                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # ... and go to the record of the profile window (last one that
-                    # was picked by the user)
-                    found = False
-                    EOF = False
-                    while (not found) and (not EOF):
-                        try:
-                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
-                            #print("hello*")
-                            #print(self.frames['profiles']['current_record_index'])
-                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
-                               (index == self.frames['profiles']['current_record_index']) and \
-                               (STNID == self.frames['profiles']['STNID']):
-                                #print('found!')
-                                found = True
-                        except StopIteration:
-                            EOF = True
-                    if found:
-                        self.frames['stats']['current_record_mod'] = record
-                        self.frames['stats']['current_record_chunk'] = chunk
-                        self.frames['stats']['current_record_index'] = index
-                    # # for the profiles we make a distinct record iterator, so that the
-                    # # stats iterator can move independently
-                    # self.frames['profiles']['records_iterator'] = \
-                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # (self.frames['profiles']['STNID'] , \
-                    # self.frames['profiles']['current_record_index']) , \
-                    # self.frames['profiles']['current_record_mod'] = \
-                    #                 self.frames['profiles']['records_iterator'].__next__()
-
-
-                    # for the profiles we make a distinct record iterator, so that the
-                    # stats iterator can move independently
-
-                    self.update_record()
-
-
-
-    def on_plot_hover(self,event):
-        axes = self.axes
-        #print('h1')
-
-        # Get to know in which axes we are
-        label = None
-        for axeskey in axes.keys():
-            if event.inaxes == axes[axeskey]:
-                label = axeskey
-                
-        #print('h2')
-
-        cont, pos = None, None
-        #print (label)
-        
-        if label is not None:
-            if  ('data' in axes[label].__dict__.keys()) and \
-                (label in axes[label].data.keys()) and \
-                (axes[label].data[label] is not None):
-                
-                #print('h3')
-                cont, ind =  axes[label].data[label].contains(event)
-                selkey = axes[label].data[label].get_label()
-                if len(ind["ind"]) > 0:
-                    #print('h4')
-                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
-                    #print('pos',pos,selkey)
-
-
-                    #if label[:-1] == 'statsview':
-                    #    seltablestatsstdrel = self.seltablestatsstdrel
-                    #    seltablestatspct = self.seltablestatspct
-
-                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
-                    #    self.hover_active = True
-                    #    
-                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
-                    #    
-                    #el
-                    #print(label[:5])
-                    if (label[:5] == 'stats') or (label == 'times'):
-                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
-                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
-                        
-
-                        if label[:5] == 'stats':
-                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                            (self.frames['stats']['STNID'] ,
-                             self.frames['stats']['current_record_chunk'], 
-                             self.frames['stats']['current_record_index']) = \
-                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-                        # elif label[:5] == 'stats':
-                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
-                        #     (self.frames['stats']['STNID'] ,
-                        #      self.frames['stats']['current_record_chunk'], 
-                        #      self.frames['stats']['current_record_index']) = \
-                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-
-
-                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-                        
-                        # # TO TEST: should be removed, since it's is also done just below
-                        # self.frames['stats']['stations_iterator'] = \
-                        #     self.frames['worldmap']['stations_iterator'] 
-                
-                
-                        # self.goto_datetime_worldmap(
-                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-                        #     'after')
-
-
-                        # scrolling to the right station
-                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                        EOF = False
-                        found = False
-                        while (not found and not EOF):
-                            if (STNID == self.frames['stats']['STNID']):
-                                   found = True 
-                            if not found:
-                                try:
-                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                                except (StopIteration):
-                                    EOF = True
-                        if found:
-                        #    self.frames['stats']['STNID'] = STNID
-                            self.frames['stats']['current_station'] =  station
-
-                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
-                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
-
-
-                        # generate index of the current station
-                        self.frames['stats']['records_current_station_index'] = \
-                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                             == self.frames['stats']['STNID'])
-
-
-                        tab_suffixes = \
-                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            self.frames['stats']['records_current_station'+tab_suffix] = \
-                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-
-                        # go to hovered record of current station
-                        self.frames['stats']['records_iterator'] = \
-                                        records_iterator(self.frames['stats']['records_current_station_mod'])
-
-
-                        # ... and go to the record of the profile window (last one that
-                        # was picked by the user)
-                        found = False
-                        EOF = False
-                        while (not found) and (not EOF):
-                            try:
-                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                                #print("hello*")
-                                #print(self.frames['profiles']['current_record_index'])
-                                if (index == self.frames['stats']['current_record_index']) and \
-                                   (chunk == self.frames['stats']['current_record_chunk']) and \
-                                   (STNID == self.frames['stats']['STNID']):
-                                    #print('found!')
-                                    found = True
-                            except StopIteration:
-                                EOF = True
-                        if found:
-                            #print('h5')
-                            self.frames['stats']['current_record_mod'] = record
-                            self.frames['stats']['current_record_chunk'] = chunk
-                            self.frames['stats']['current_record_index'] = index
-
-                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
-                        tab_suffixes = \
-                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            #print(tab_suffix)
-                            #print(self.frames['stats']['records_current_station'+tab_suffix])
-                            self.frames['stats']['current_record'+tab_suffix] =  \
-                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                      (self.frames['stats']['STNID'] , \
-                                       self.frames['stats']['current_record_chunk'] , \
-                                       self.frames['stats']['current_record_index'])]
-
-
-                        self.hover_active = True
-                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                        # print('h13')
-                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
-                        #     self.goto_datetime_worldmap(
-                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                        #         'after')
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap',
-                        #                                           'profiles'])
-                        # else:
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap_stations',
-                        #                                           'profiles'])
-
-
-
-                    elif label in ['worldmap_stations','worldmap']:
-                        #print('h5')
-
-                        if (self.axes['worldmap'].lat is not None) and \
-                           (self.axes['worldmap'].lon is not None):
-
-
-                            #self.loading()
-                            self.fig.canvas.draw()
-                            self.fig.show()
-
-
-                            # get position of 
-                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
-                                                                 self.axes['worldmap'].lat[0]) + \
-                                           self.axes['worldmap'].lat[0],4)
-                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
-                                                                 self.axes['worldmap'].lon[0]) + \
-                                           self.axes['worldmap'].lon[0],4)
-                        
-                            stations = self.frames['worldmap']['stations'].table
-                            #print('h7')
-                        
-                            #reset stations iterator:
-                            # if 'stations_iterator' in self.frames['worldmap'].keys():
-                            #     self.frames['worldmap']['stations_iterator'].close()
-                            #     del(self.frames['worldmap']['stations_iterator'])
-                            # if 'stations_iterator' in self.frames['stats'].keys():
-                            #     self.frames['stats']['stations_iterator'].close()
-                            #     del(self.frames['stats']['stations_iterator'])
-                            self.frames['worldmap']['stations_iterator'] =\
-                               stations_iterator(self.frames['worldmap']['stations'])
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                            EOF = False
-                            found = False
-                            while (not found and not EOF):
-                                #print('h8',station.latitude,latmap)
-                                #print('h8',station.longitude,lonmap)
-                                if (round(station.latitude,3) == round(latmap,3)) and \
-                                    (round(station.longitude,3) == round(lonmap,3)):
-                                       found = True 
-                                if not found:
-                                    try:
-                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                                    except (StopIteration):
-                                        EOF = True
-                            if found:
-                                self.frames['worldmap']['STNID'] = STNID
-                                self.frames['worldmap']['current_station'] = \
-                                        station
-                        
-                            self.frames['stats']['stations_iterator'] = \
-                                self.frames['worldmap']['stations_iterator'] 
-                            #print('h8')
-                            # inherit station position for the stats frame...
-                            for key in self.frames['worldmap'].keys():
-                                self.frames['stats'][key] = self.frames['worldmap'][key]
-                                
-                            ## fetch records of current station...
-                            #self.frames['stats']['records_current_station_mod'] =\
-                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                            # ... and their indices
-                            self.frames['stats']['records_current_station_index'] = \
-                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                                     == \
-                                     self.frames['stats']['current_station'].name)
-
-
-                            tab_suffixes = \
-                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['records_current_station'+tab_suffix] = \
-                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-                            # ... create a record iterator ...
-                            #self.frames['stats']['records_iterator'].close()
-                            del(self.frames['stats']['records_iterator'])
-                            self.frames['stats']['records_iterator'] = \
-                                self.frames['stats']['records_current_station_mod'].iterrows()
-
-
-
-                        
-                            #print('h9')
-                            # ... and go to to the first record of the current station
-                            (self.frames['stats']['STNID'] , \
-                             self.frames['stats']['current_record_chunk'] , \
-                             self.frames['stats']['current_record_index']) , \
-                            self.frames['stats']['current_record_mod'] = \
-                                self.frames['stats']['records_iterator'].__next__()
-                        
-
-
-
-                            #print('h10')
-                            # cash the current record
-                            tab_suffixes = \
-                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['current_record'+tab_suffix] =  \
-                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                          (self.frames['stats']['STNID'] , \
-                                           self.frames['stats']['current_record_chunk'] , \
-                                           self.frames['stats']['current_record_index'])]
-
-                            #print('h11')
-                            
-                            self.hover_active = True
-                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                            #print('h13')
-
-                        
-
-            #if (stations is not None):
-            #    for iSTN,STN in stations.iterrows():
-            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
-            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
-
-        # self.fig.show()
- 
-        # we are hovering on nothing, so we are going back to the position of
-        # the profile sounding
-        if pos is None:
-            if self.hover_active == True:
-                #print('h1*')
-                
-                #self.loading()
-                # to do: reset stations iterators
-
-                # get station and record index from the current profile
-                for key in ['STNID', 'current_station']:
-                    self.frames['stats'][key] = self.frames['profiles'][key]
-
-                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
-                self.frames['stats']['current_station'] = \
-                        self.frames['profiles']['current_station']
-                #print('h3a*')
-                self.frames['stats']['records_current_station_mod'] = \
-                        self.frames['profiles']['records_current_station_mod']
-                #print('h3b*')
-
-                # the next lines recreate the records iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-
-                # reset stations iterator...
-                #self.frames['stats']['records_iterator'].close()
-                del(self.frames['stats']['records_iterator'])
-                self.frames['stats']['records_iterator'] = \
-                    self.frames['stats']['records_current_station_mod'].iterrows()
-                #print('h4*')
-
-                # ... and go to the record of the profile window (last one that
-                # was picked by the user)
-                found = False
-                EOF = False
-                while (not found) and (not EOF):
-                    try:
-                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                        #print("hello*")
-                        #print(self.frames['profiles']['current_record_index'])
-                        #print(self.frames['profiles']['STNID'])
-                        #print(STNID,index)
-                        if (index == self.frames['profiles']['current_record_index']) and \
-                            (chunk == self.frames['profiles']['current_record_chunk']) and \
-                            (STNID == self.frames['profiles']['STNID']):
-                            #print('found!')
-                            found = True
-                    except StopIteration:
-                        EOF = True
-                if found:
-                    #print('h5*')
-                    self.frames['stats']['current_record_mod'] = record
-                    self.frames['stats']['current_record_chunk'] = chunk
-                    self.frames['stats']['current_record_index'] = index
-
-                #print('h6*')
-
-
-
-                # # fetch records of current station...
-                # self.frames['stats']['records_current_station_mod'] =\
-                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                # ... and their indices
-                self.frames['stats']['records_current_station_index'] = \
-                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                         == \
-                         self.frames['stats']['current_station'].name)
-
-
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['records_current_station'+tab_suffix] = \
-                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-                
-
-                # cash the records of the current stations
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['current_record'+tab_suffix] =  \
-                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                              (self.frames['stats']['STNID'] , \
-                               self.frames['stats']['current_record_chunk'] , \
-                               self.frames['stats']['current_record_index'])]
-
-
-                # the next lines recreate the stations iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-                #print('h7*')
-
-                # reset the stations iterators
-                for framekey in ['stats','worldmap']:
-                    ##print(framekey)
-                    if 'stations_iterator' in self.frames[framekey]:
-                        #self.frames[framekey]['stations_iterator'].close()
-                        del(self.frames[framekey]['stations_iterator'])
-
-                self.frames['worldmap']['current_station'] = \
-                        self.frames['profiles']['current_station']
-
-                #recreate the stations iterator for the worldmap...
-                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-
-                # ... and go the position of the profile
-                #print('h8*')
-                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                EOF = False
-                found = False
-                while (not found and not EOF):
-                    if STNID == self.frames['profiles']['STNID'] :
-                        found = True 
-                    if not found:
-                        try:
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                        except (StopIteration):
-                            EOF = True
-                if found:
-                    self.frames['worldmap']['current_station'] = station
-                    self.frames['worldmap']['STNID'] = STNID
-                #print('h9*')
-                self.frames['stats']['stations_iterator'] = \
-                    self.frames['worldmap']['stations_iterator'] 
-
-                # the stats window now inherits the current station from the
-                # worldmap
-                for key in ['STNID','current_station','stations_iterator']: 
-                    self.frames['stats'][key] = self.frames['worldmap'][key] 
-                #print('h10*')
-
-                # # we now only need inherit station position and go to first record
-                # for key in self.frames['worldmap'].keys():
-                #     self.frames['stats'][key] = self.frames['worldmap'][key]
-
-                # self.frames['stats']['records_current_station'] =\
-                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
-
-                # #print(self.frames['stats']['records_current_station'])
-                # self.frames['stats']['records_iterator'] = \
-                #                 self.frames['stats']['records_current_station'].iterrows()
-                # (self.frames['stats']['STNID'] , \
-                # self.frames['stats']['current_record_index']) , \
-                # self.frames['stats']['current_record_mod'] = \
-                #                 self.frames['stats']['records_iterator'].__next__()
-                
-
-
-
-
-
-
-                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
-                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
-                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
-                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-                self.hover_active = False
-                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
-    # def loading(self):
-    #     self.tbox['loading'].set_text('Loading...')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-    #     sleep(0.1)
-    # def ready(self):
-    #     self.tbox['loading'].set_text('Ready')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-
-
-
diff --git a/build/lib/class4gl/model.py b/build/lib/class4gl/model.py
deleted file mode 100644
index 8760411..0000000
--- a/build/lib/class4gl/model.py
+++ /dev/null
@@ -1,2214 +0,0 @@
-# 
-# CLASS
-# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
-# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
-# Copyright (c) 2011-2015 Chiel van Heerwaarden
-# Copyright (c) 2011-2015 Bart van Stratum
-# Copyright (c) 2011-2015 Kees van den Dries
-# 
-# This file is part of CLASS
-# 
-# CLASS is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published bygamma
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-# 
-# CLASS is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-# 
-# You should have received a copy of the GNU General Public License
-# along with CLASS.  If not, see .
-#
-
-import copy as cp
-import numpy as np
-import sys
-import warnings
-import pandas as pd
-from ribtol_hw import zeta_hs2 , funcsche
-import logging
-#from SkewT.thermodynamics import Density
-#import ribtol
-
-grav = 9.81
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-
-def qsat(T,p):
-    return 0.622 * esat(T) / p
-
-
-def ribtol(Rib, zsl, z0m, z0h): 
-    Rib = np.float64(Rib)
-    zsl = np.float64(zsl)
-    z0m = np.float64(z0m)
-    z0h = np.float64(z0h)
-    #print(Rib,zsl,z0m,z0h)
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    #print(Rib,zsl,z0m,z0h)
-    while (abs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-        #print(L,fx/fxdif)
-        if(abs(L) > 1e12):
-            break
-
-    return L
-  
-def psim(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psim = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-  
-def psih(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * np.log( (1. + x*x) / 2.)
-        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
- 
-class model:
-    def __init__(self, model_input = None,debug_level=None):
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        self.logger = logging.getLogger('model')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        """ initialize the different components of the model """ 
-
-        if model_input is not None:
-            # class4gl style input
-            if 'pars' in model_input.__dict__.keys():
-
-                # we make a reference to the full input first, so we can dump it
-                # afterwards
-                self.input_c4gl = model_input
-
-                # we copy the regular parameters first. We keep the classical input
-                # format as self.input so that we don't have to change the entire
-                # model code.
-                self.input = cp.deepcopy(model_input.pars)
-
-                # we copy other sections we are interested in, such as profile
-                # data, and store it also under input
-
-                # I know we mess up a bit the structure of the class4gl_input, but
-                # we will make it clean again at the time of dumping data
-
-                # So here, we copy the profile data into self.input
-                # 1. Air circulation data 
-                if 'sw_ac' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ac']:
-                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
-                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
-
-                    # correct pressure of levels according to surface pressure
-                    # error (so that interpolation is done in a consistent way)
-
-                    p_e = self.input.Ps - self.input.sp
-                    for irow in self.input.air_ac.index[::-1]:
-                       self.input.air_ac.p.iloc[irow] =\
-                        self.input.air_ac.p.iloc[irow] + p_e
-                       p_e = p_e -\
-                       (self.input.air_ac.p.iloc[irow]+p_e)/\
-                        self.input.air_ac.p.iloc[irow] *\
-                        self.input.air_ac.delpdgrav.iloc[irow]*grav
-
-
-
-                # 2. Air circulation data 
-                if 'sw_ap' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ap']:
-                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
-
-            # standard class input
-            else:
-                self.input = cp.deepcopy(model_input)
-
-    def load_yaml_dict(self,yaml_dict):
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                for keydata,value in data.items():
-                    self.__dict__[keydata] = value
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            #elif key == 'sources':
-            #    self.__dict__[key] = data
-            elif key == 'out':
-                # lets convert it to a list of dictionaries
-                dictouttemp = pd.DataFrame(data).to_dict('list')
-            else: 
-                 warnings.warn("Key '"+key+"' is be implemented.")
-            #     self.__dict__[key] = data
-
-
-        self.tsteps = len(dictouttemp['h'])
-        self.out = model_output(self.tsteps)
-        for keydictouttemp in dictouttemp.keys():
-            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
-
-
-  
-    def run(self):
-        # initialize model variables
-        self.init()
-  
-        # time integrate model 
-        #for self.t in range(self.tsteps):
-        while self.t < self.tsteps:
-          
-            # time integrate components
-            self.timestep()
-  
-        # delete unnecessary variables from memory
-        self.exitmodel()
-    
-    def init(self):
-        # assign variables from input data
-        # initialize constants
-        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
-        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        self.rho        = 1.2                   # density of air [kg m-3]
-        self.k          = 0.4                   # Von Karman constant [-]
-        self.g          = 9.81                  # gravity acceleration [m s-2]
-        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-        self.bolz       = 5.67e-8               # Bolzman constant [-]
-        self.rhow       = 1000.                 # density of water [kg m-3]
-        self.S0         = 1368.                 # solar constant [W m-2]
-
-        # A-Gs constants and settings
-        # Plant type:       -C3-     -C4-
-        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
-        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
-        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
-        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
-        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
-        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
-        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
-        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
-        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
-        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
-        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
-
-        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
-        self.mair       =  28.9;                # molecular weight air [g mol -1]
-        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
-
-        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
-        self.wmax       =  0.55;                # upper reference value soil water [-]
-        self.wmin       =  0.005;               # lower reference value soil water [-]
-        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
-        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
-
-        # Read switches
-        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
-        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
-        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
-        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
-        self.sw_sl      = self.input.sw_sl      # surface layer switch
-        self.sw_rad     = self.input.sw_rad     # radiation switch
-        self.sw_ls      = self.input.sw_ls      # land surface switch
-        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
-        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
-
-        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
-        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
-        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-  
-        # initialize mixed-layer
-        self.h          = self.input.h          # initial ABL height [m]
-        self.Ps         = self.input.Ps         # surface pressure [Pa]
-        self.sp         = self.input.sp         # This is also surface pressure
-                                                #but derived from the global data [Pa]
-        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
-        self.ws         = None                  # large-scale vertical velocity [m s-1]
-        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
-        self.we         = -1.                   # entrainment velocity [m s-1]
-       
-         # Temperature 
-        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
-        
-        
-        self.substep    = False
-        self.substeps   = 0
-
-
-
-        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
-        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
-        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
-        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
-        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
- 
-        self.wstar      = 0.                    # convective velocity scale [m s-1]
- 
-        # 2m diagnostic variables 
-        self.T2m        = None                  # 2m temperature [K]
-        self.q2m        = None                  # 2m specific humidity [kg kg-1]
-        self.e2m        = None                  # 2m vapor pressure [Pa]
-        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
-        self.u2m        = None                  # 2m u-wind [m s-1]
-        self.v2m        = None                  # 2m v-wind [m s-1]
- 
-        # Surface variables 
-        self.thetasurf  = self.input.theta      # surface potential temperature [K]
-        self.thetavsurf = None                  # surface virtual potential temperature [K]
-        self.qsurf      = None                  # surface specific humidity [g kg-1]
-
-        # Mixed-layer top variables
-        self.P_h        = None                  # Mixed-layer top pressure [pa]
-        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
-        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
-        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
-        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
-        self.dz_h       = None                  # Transition layer thickness [-]
-        self.lcl        = None                  # Lifting condensation level [m]
-
-        # Virtual temperatures and fluxes
-        self.thetav     = None                  # initial mixed-layer potential temperature [K]
-        self.dthetav    = None                  # initial virtual temperature jump at h [K]
-        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
-        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
-       
-        
-        
-        
-        
-        
-        # Moisture 
-        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
-
-        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
-        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
-        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
-  
-        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
-        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
-        self.e          = None                  # mixed-layer vapor pressure [Pa]
-        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
-        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
-      
-        
-        
-        # CO2
-        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
-        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
-        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
-        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
-        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
-        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
-        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
-        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
-        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
-        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
-       
-        # Wind 
-        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
-        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
-        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = self.input.advu       # advection of u-wind [m s-2]
-        
-        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
-        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = self.input.advv       # advection of v-wind [m s-2]
-         
-  # BEGIN -- HW 20170606
-        # z-coordinate for vertical profiles of stratification above the mixed-layer height
-
-        if self.sw_ac:
-        # this is the data frame with the grided profile on the L60 grid
-        # (subsidence, and advection) 
-            self.air_ac      = self.input.air_ac  # full level air circulation
-                                                  # forcing
-            # self.air_ach     = self.input.air_ach # half level air circulation
-            #                                       # forcing
-            
-
-        if self.sw_ap:
-        # this is the data frame with the fitted profile (including HAGL,
-        # THTA,WSPD, SNDU,WNDV PRES ...)
-            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
-
-            # just for legacy reasons...
-            if 'z' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
-            if 'p' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
-
-            indexh = np.where(self.air_ap.z.values == self.h)
-            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
-                raise ValueError("Error input profile consistency: mixed- \
-                                 layer height needs to be equal to the second \
-                                 and third \
-                                 level of the vertical profile input!")
-            # initialize q from its profile when available
-            p_old = self.Ps
-            p_new = self.air_ap.p[indexh[0][0]]
-            
-            if ((p_old is not None) & (p_old != p_new)):
-                print("Warning: Ps input was provided ("+str(p_old)+\
-                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
-                    +str(p_new)+"Pa).")
-                                    
-            self.Ps = p_new
-            # these variables/namings are more convenient to work with in the code
-            # we will update the original variables afterwards
-            #self.air_ap['q'] = self.air_ap.QABS/1000.
-
-            self.air_ap = \
-                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
-            # we require the temperature fields, since we need to consider
-            # advection
-            # if self.sw_ac:
-            #     #self.air_ap['theta'] = self.air_ap['t'] *
-
-            #     # we consider self.sp in case of air-circulation input (for
-            #     # consistence)
-            #     self.air_ap['t'] = \
-            #                 self.air_ap.theta *  \
-            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
-            # else:
-            # we consider self.Ps in case of balloon input only 
-            self.air_ap = self.air_ap.assign(t = lambda x: \
-                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
-
-            #self.air_ap['theta'] = self.air_ap.THTA
-            if 'u' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
-            if 'v' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
-
-            for var in ['theta','q','u','v']:
-
-                
-                if self.air_ap[var][1] != self.air_ap[var][0]:
-                    raise ValueError("Error input profile consistency: two \
-                                     lowest profile levels for "+var+" should \
-                                     be equal.")
-                
-                # initialize the value from its profile when available
-                value_old = self.__dict__[var]
-                value_new = self.air_ap[var][indexh[0][0]]
-                
-                if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided \
-                                     ("+str(value_old)+ "kg kg-1), \
-                                     but it is now overwritten by the first \
-                                     level (index 0) of air_ap]var\ which is \
-                                     different (" +str(value_new)+"K).")
-                                        
-                self.__dict__[var] = value_new
-
-                # make a profile of the stratification 
-                # please note that the stratification between z_pro[i] and
-                # z_pro[i+1] is given by air_ap.GTHT[i]
-
-                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
-                # np.gradient(self.z_pro)
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
-
-
-                self.__dict__['gamma'+var] = \
-                    self.air_ap['gamma'+var][np.where(self.h >= \
-                                                     self.air_ap.z)[0][-1]]
-
-
-
-        # the variable p_pro is just for diagnosis of lifted index
-            
-            
-
-            # input Ph is wrong, so we correct it according to hydrostatic equation
-            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-
-            #if self.sw_ac:
-                # note that we use sp as surface pressure, which is determined
-                # from era-interim instead of the observations. This is to
-                # avoid possible failure of the interpolation routine
-                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
-                #                          + \
-                #                          list(self.air_ap.p[3:]))
-
-            # else:
-                # in the other case, it is updated at the time of calculting
-                # the statistics 
-
-# END -- HW 20170606      
-        #print(self.air_ap)
-
-        if self.sw_ac and not self.sw_ap:
-            raise ValueError("air circulation switch only possible when air \
-                             profiles are given")
-        
-        if self.sw_ac:
-
-            # # # we comment this out, because subsidence is calculated
-            # according to advection
-            # #interpolate subsidence towards the air_ap height coordinate
-            # self.air_ap['w'] = np.interp(self.air_ap.p,\
-            #                               self.air_ac.p,\
-            #                               self.air_ac.w) 
-            # #subsidence at the mixed-layer top
-            # self.w = self.air_ap.w[1]
-        
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-                # in case we didn't find any points, we just take the lowest one.
-                # actually, this can happen if ERA-INTERIM pressure levels are
-                # inconsistent with 
-                if in_ml.sum() == 0:
-                    warnings.warn(" no circulation points in the mixed layer \
-                                  found. We just take the bottom one.")
-                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-
-                for var in ['t','q','u','v']:
-    
-                   # calculation of the advection variables for the mixed layer
-                   # we weight by the hydrostatic thickness of each layer and
-                   # divide by the total thickness
-                   self.__dict__['adv'+var] = \
-                            ((self.air_ac['adv'+var+'_x'][in_ml] \
-                             + \
-                             self.air_ac['adv'+var+'_y'][in_ml])* \
-                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                            self.air_ac['delpdgrav'][in_ml].sum()
-
-                   # calculation of the advection variables for the profile above
-                   # (lowest 3 values are not used by class)
-                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
-                   self.air_ap['adv'+var] = \
-                           np.interp(self.air_ap.p,\
-                                     self.air_ac.p,\
-                                     self.air_ac['adv'+var+'_x']) \
-                           + \
-                           np.interp(self.air_ap.p, \
-                                       self.air_ac.p, \
-                                       self.air_ac['adv'+var+'_y'])
-
-                # as an approximation, we consider that advection of theta in the
-                # mixed layer is equal to advection of t. This is a sufficient
-                # approximation since theta and t are very similar at the surface
-                # pressure.
-                self.__dict__['advtheta'] = self.__dict__['advt']
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # self.wrho = np.interp(self.P_h,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) 
-            # self.ws   = self.air_ap.w.iloc[1]
-
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                self.air_ap = self.air_ap.assign(wp = 0.)
-                self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                              self.air_ac.p, \
-                                              self.air_ac['wp'])
-                self.air_ap = self.air_ap.assign(R = 0.)
-                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                     self.Rv*self.air_ap.q)
-                self.air_ap = self.air_ap.assign(rho = 0.)
-                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-                
-                self.air_ap = self.air_ap.assign(w = 0.)
-                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-                #print('hello w ini')
-
-                # Note: in case of sw_ac is False, we update it from prescribed
-                # divergence
-                self.ws   = self.air_ap.w[1]
-
-                # self.ws   = self.wrho/self.rho
-                # self.ws   = self.wrho/(self.P_h/ \
-                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
-                #                         self.theta) # this should be T!!!
-
-                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-                #                         + \
-                #                         self.air_ac['divU_y'][in_ml])* \
-                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                #             self.air_ac['delpdgrav'][in_ml].sum() \
-        
-
-        # Tendencies 
-        self.htend      = None                  # tendency of CBL [m s-1]
-        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
-        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
-        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
-        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
-        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
-        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
-        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
-        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
-        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
-        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
-        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
-  
-        # initialize surface layer
-        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
-        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
-        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
-        self.z0m        = self.input.z0m        # roughness length for momentum [m]
-        self.z0h        = self.input.z0h        # roughness length for scalars [m]
-        self.Cm         = 1e12                  # drag coefficient for momentum [-]
-        self.Cs         = 1e12                  # drag coefficient for scalars [-]
-        self.L          = None                  # Obukhov length [m]
-        self.Rib        = None                  # bulk Richardson number [-]
-        self.ra         = None                  # aerodynamic resistance [s m-1]
-  
-        # initialize radiation
-        self.lat        = self.input.lat        # latitude [deg]
-        #self.fc         = self.input.fc         # coriolis parameter [s-1]
-        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
-        self.lon        = self.input.lon        # longitude [deg]
-        self.doy        = self.input.doy        # day of the year [-]
-        self.tstart     = self.input.tstart     # time of the day [-]
-        self.cc         = self.input.cc         # cloud cover fraction [-]
-        self.Swin       = None                  # incoming short wave radiation [W m-2]
-        self.Swout      = None                  # outgoing short wave radiation [W m-2]
-        self.Lwin       = None                  # incoming long wave radiation [W m-2]
-        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
-        self.Q          = self.input.Q          # net radiation [W m-2]
-        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
-  
-        # initialize land surface
-        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
-        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
-        self.T2         = self.input.T2         # temperature deeper soil layer [K]
-                           
-        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
-        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
-        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
-        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
-                           
-        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
-        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
-        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
-                           
-        self.C1sat      = self.input.C1sat      
-        self.C2ref      = self.input.C2ref      
-
-        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
-        
-        self.LAI        = self.input.LAI        # leaf area index [-]
-        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
-        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = self.input.alpha      # surface albedo [-]
-  
-        self.rs         = 1.e6                  # resistance transpiration [s m-1]
-        self.rssoil     = 1.e6                  # resistance soil [s m-1]
-                           
-        self.Ts         = self.input.Ts         # surface temperature [K]
-                           
-        self.cveg       = self.input.cveg       # vegetation fraction [-]
-        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
-        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
-        self.cliq       = None                  # wet fraction [-]
-                          
-        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
-  
-        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
-        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
-        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
-  
-        self.H          = None                  # sensible heat flux [W m-2]
-        self.LE         = None                  # evapotranspiration [W m-2]
-        self.LEliq      = None                  # open water evaporation [W m-2]
-        self.LEveg      = None                  # transpiration [W m-2]
-        self.LEsoil     = None                  # soil evaporation [W m-2]
-        self.LEpot      = None                  # potential evaporation [W m-2]
-        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
-        self.G          = None                  # ground heat flux [W m-2]
-
-        # initialize A-Gs surface scheme
-        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
-
-        # initialize cumulus parameterization
-        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
-        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
-        self.ac         = 0.                    # Cloud core fraction [-]
-        self.M          = 0.                    # Cloud core mass flux [m s-1] 
-        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
-  
-        # initialize time variables
-        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
-        self.dt     = self.input.dt
-        self.dtcur      = self.dt
-        self.firsttime = True
-        self.t      = 0
- 
-        # Some sanity checks for valid input
-        if (self.c_beta is None): 
-            self.c_beta = 0                     # Zero curvature; linear response
-        assert(self.c_beta >= 0 or self.c_beta <= 1)
-
-        # initialize output
-        self.out = model_output(self.tsteps)
- 
-        self.statistics()
-  
-        # calculate initial diagnostic variables
-        if(self.sw_rad):
-            self.run_radiation()
- 
-        if(self.sw_sl):
-            for i in range(10): 
-                self.run_surface_layer()
-  
-        if(self.sw_ls):
-            self.run_land_surface()
-
-        if(self.sw_cu):
-            self.run_mixed_layer()
-            self.run_cumulus()
-        
-        if(self.sw_ml):
-            self.run_mixed_layer()
-
-    def timestep(self):
-
-        self.dtmax = +np.inf
-        self.logger.debug('before stats') 
-        self.statistics()
-
-        # run radiation model
-        self.logger.debug('before rad') 
-        if(self.sw_rad):
-            self.run_radiation()
-  
-        # run surface layer model
-        if(self.sw_sl):
-            self.logger.debug('before surface layer') 
-            self.run_surface_layer()
-        
-        # run land surface model
-        if(self.sw_ls):
-            self.logger.debug('before land surface') 
-            self.run_land_surface()
- 
-        # run cumulus parameterization
-        if(self.sw_cu):
-            self.logger.debug('before cumulus') 
-            self.run_cumulus()
-   
-        self.logger.debug('before mixed layer') 
-        # run mixed-layer model
-        if(self.sw_ml):
-            self.run_mixed_layer()
-        self.logger.debug('after mixed layer') 
- 
-        #get first profile data point above mixed layer
-        if self.sw_ap:
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                # here we correct for the fact that the upper profile also
-                # shifts in the vertical.
-
-                diffhtend = self.htend - self.air_ap.w[zidx_first]
-                if diffhtend > 0:
-                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            else:
-                if self.htend > 0:
-                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            #print(self.h,zidx_first,self.ws,self.air_ap.z)
-
-        
-        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
-        self.logger.debug('before store') 
-        self.substep =  (self.dtcur > self.dtmax)
-        if self.substep:
-            dtnext = self.dtcur - self.dtmax
-            self.dtcur = self.dtmax
-
-        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
-
-        # HW: this will be done multiple times in case of a substep is needed
-        # store output before time integration
-        if self.firsttime:
-            self.store()
-  
-        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
-        # time integrate land surface model
-        if(self.sw_ls):
-            self.integrate_land_surface()
-        self.logger.debug('before integrate mixed layer') 
-        # time integrate mixed-layer model
-        if(self.sw_ml):
-            self.integrate_mixed_layer() 
-        self.logger.debug('after integrate mixed layer') 
-        if self.substep:
-            self.dtcur = dtnext
-            self.firsttime = False
-            self.substeps += 1
-        else:
-            self.dtcur = self.dt
-            self.t += 1 
-            self.firsttime = True
-            self.substeps = 0
-        self.logger.debug('going to next step')
-        
-        
-  
-    def statistics(self):
-        # Calculate virtual temperatures 
-        self.thetav   = self.theta  + 0.61 * self.theta * self.q
-        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
-        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
-        # Mixed-layer top properties
-        self.P_h    = self.Ps - self.rho * self.g * self.h
-        # else:
-            # in the other case, it is updated at the time that the profile is
-            # updated (and at the initialization
-
-        self.T_h    = self.theta - self.g/self.cp * self.h
-
-        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
-        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
-
-        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
-
-        # Find lifting condensation level iteratively
-        if(self.t == 0):
-            self.lcl = self.h
-            RHlcl = 0.5
-        else:
-            RHlcl = 0.9998 
-
-        itmax = 30
-        it = 0
-        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
-            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
-            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
-        else:
-            self.q2_h   = 0.
-            self.CO22_h = 0.
-
-        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
-        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
-        self.M      = self.ac * self.wstar
-        self.wqM    = self.M * self.q2_h**0.5
-
-        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
-        if(self.dCO2 < 0):
-            self.wCO2M  = self.M * self.CO22_h**0.5
-        else:
-            self.wCO2M  = 0.
-
-    def run_mixed_layer(self):
-        if(not self.sw_sl):
-            # decompose ustar along the wind components
-            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
-            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
-
-
-
-        # calculate large-scale vertical velocity (subsidence)
-        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
-            self.ws = -self.divU * self.h
-        # else:
-        #     in case the air circulation switch is turned on, subsidence is
-        #     calculated from the circulate profile at the initialization and
-        #     in the integrate_mixed_layer routine
-              
-        # calculate compensation to fix the free troposphere in case of subsidence 
-        if(self.sw_fixft):
-            w_th_ft  = self.gammatheta * self.ws
-            w_q_ft   = self.gammaq     * self.ws
-            w_CO2_ft = self.gammaCO2   * self.ws 
-        else:
-            w_th_ft  = 0.
-            w_q_ft   = 0.
-            w_CO2_ft = 0. 
-      
-        # calculate mixed-layer growth due to cloud top radiative divergence
-        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
-       
-        # calculate convective velocity scale w* 
-        if(self.wthetav > 0.):
-            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
-        else:
-            self.wstar  = 1e-6;
-      
-        # Virtual heat entrainment flux 
-        self.wthetave    = -self.beta * self.wthetav 
-        
-        # compute mixed-layer tendencies
-        if(self.sw_shearwe):
-            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
-        else:
-            self.we    = -self.wthetave / self.dthetav
-        # Don't allow boundary layer shrinking if wtheta < 0 
-        if(self.we < 0):
-            self.we = 0.
-
-        # Calculate entrainment fluxes
-        self.wthetae     = -self.we * self.dtheta
-        self.wqe         = -self.we * self.dq
-        self.wCO2e       = -self.we * self.dCO2
-        
-        htend_pre       = self.we + self.ws + self.wf - self.M
-        
-        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-        
- 
-        #print('thetatend_pre',thetatend_pre)
-        
-        #preliminary boundary-layer top chenage
-        #htend_pre = self.we + self.ws + self.wf - self.M
-        #preliminary change in temperature jump
-        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
-                          thetatend_pre + w_th_ft
-        
-        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
-        l_entrainment = True
-
-        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
-            l_entrainment = False
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! temperature jump is at the lower limit \
-                          and is not growing: entrainment is disabled for this (sub)timestep.") 
-        elif dtheta_pre < 0.1:
-            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
-            l_entrainment = True
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          " Warning! Potential temperature jump at mixed- \
-                          layer height would become too low limiting timestep \
-                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
-            self.dtmax = min(self.dtmax,dtmax_new)
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "next subtimestep, entrainment will be disabled")
-            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
-
-
-
-        # when entrainment is disabled, we just use the simplified formulation
-        # as in Wouters et al., 2013 (section 2.2.1)
-
-        self.dthetatend = l_entrainment*dthetatend_pre + \
-                        (1.-l_entrainment)*0.
-        self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
-        self.htend = l_entrainment*htend_pre + \
-                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
-        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
-        #stop
-
-
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
-
-
-        # self.qtend = l_entrainment*qtend_pre + \
-        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
-        # self.CO2tend = l_entrainment*CO2tend_pre + \
-        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-
-
-        #     # part of the timestep for which the temperature mixed-layer jump
-        #     # was changing, and for which entrainment took place. For the other
-        #     # part, we don't assume entrainment anymore, and we use the
-        #     # simplified formulation  of Wouters et al., 2013
-
-        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
-        #   
-        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
-        #                      self.dthetatend + w_th_ft) + \
-        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
-        #     self.htend = fac*self.htend + \
-        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
-        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
-        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-
-        # else:
-        #     #self.htend = htend_pre
-        #     self.dthetatend = dthetatend_pre
-        #     self.thetatend = thetatend_pre
-        
-        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
-     
-        # assume u + du = ug, so ug - u = du
-        if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
-  
-            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
-        
-        # tendency of the transition layer thickness
-        if(self.ac > 0 or self.lcl - self.h < 300):
-            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
-        else:
-            self.dztend = 0.
-
-   
-    def integrate_mixed_layer(self):
-        # set values previous time step
-        h0      = self.h
-        
-        theta0  = self.theta
-        dtheta0 = self.dtheta
-        q0      = self.q
-        dq0     = self.dq
-        CO20    = self.CO2
-        dCO20   = self.dCO2
-        
-        u0      = self.u
-        du0     = self.du
-        v0      = self.v
-        dv0     = self.dv
-
-        dz0     = self.dz_h
-  
-        # integrate mixed-layer equations
-        
-            
-
-# END -- HW 20170606        
-        self.h        = h0      + self.dtcur * self.htend
-        # print(self.h,self.htend)
-        # stop
-        self.theta    = theta0  + self.dtcur * self.thetatend
-        #print(dtheta0,self.dtcur,self.dthetatend)
-        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
-        self.q        = q0      + self.dtcur * self.qtend
-        self.dq       = dq0     + self.dtcur * self.dqtend
-        self.CO2      = CO20    + self.dtcur * self.CO2tend
-        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
-        self.dz_h     = dz0     + self.dtcur * self.dztend
-            
-        # Limit dz to minimal value
-        dz0 = 50
-        if(self.dz_h < dz0):
-            self.dz_h = dz0 
-  
-        if(self.sw_wind):
-            self.u        = u0      + self.dtcur * self.utend
-            self.du       = du0     + self.dtcur * self.dutend
-            self.v        = v0      + self.dtcur * self.vtend
-            self.dv       = dv0     + self.dtcur * self.dvtend
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-            for var in ['t','q','u','v']:
-                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
-
-            # take into account advection for the whole profile
-                
-                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
-
-            var = 'z'
-            #print(self.air_ap[var])
-                #     print(self.air_ap['adv'+var])
-
-
-
-
-            #moving the profile vertically according to the vertical wind
-                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
-
-
-            # air_apvarold = pd.Series(np.array(self.air_ap.z))
-            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
-            # stop
-
-
-                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
-                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
-
-            #As t is updated, we also need to recalculate theta (and R)
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-
-            # air_aptheta_old = pd.Series(self.air_ap['theta'])
-            self.air_ap['theta'] = \
-                        self.air_ap.t * \
-                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
-                                         self.dtcur * self.air_ap.w[zidx_first:]
-
-#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
-#            print(self.t, self.dtcur,self.dt,self.htend)
-
-            # # the pressure levels of the profiles are recalculated according to
-            # # there new height (after subsidence)
-            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
-            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
-            #         * self.dtcur *  self.air_ap.w[zidx_first:]
-
-            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
-                    self.dtcur * self.air_ap.wp[zidx_first:]
-
-            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
-        # note that theta and q itself are updatet by class itself
-
-    
-        if self.sw_ap:
-            # Just for model consistency preservation purposes, we set the
-            # theta variables of the mixed-layer to nan values, since the
-            # mixed-layer values should overwritte by the mixed-layer
-            # calculations of class.
-            self.air_ap['theta'][0:3] = np.nan 
-            self.air_ap['p'][0:3] = np.nan 
-            self.air_ap['q'][0:3] = np.nan 
-            self.air_ap['u'][0:3] = np.nan 
-            self.air_ap['v'][0:3] = np.nan 
-            self.air_ap['t'][0:3] = np.nan 
-            self.air_ap['z'][0:3] = np.nan 
-
-            # Update the vertical profiles: 
-            #   - new mixed layer properties( h, theta, q ...)
-            #   - any data points below the new ixed-layer height are removed
-
-            # Three data points at the bottom that describe the mixed-layer
-            # properties
-            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
-                                           # columns as air_ap
-            # air_ap_head['z'].iloc[0] = 2.
-            # air_ap_head['z'].iloc[1] = self.__dict__['h']
-            # air_ap_head['z'].iloc[2] = self.__dict__['h']
-            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
-                        [2.,self.__dict__['h'],self.__dict__['h']]
-            for var in ['theta','q','u','v']:
-
-                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
-                        [self.__dict__[var], \
-                         self.__dict__[var], \
-                         self.__dict__[var] + self.__dict__['d'+var]]
-                
-            #print(self.air_ap)
-
-            # This is the remaining profile considering the remaining
-            # datapoints above the mixed layer height
-            air_ap_tail = self.air_ap.iloc[3:]
-            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
-
-            # print('h',self.h)
-            # # only select samples monotonically increasing with height
-            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            # air_ap_tail = pd.DataFrame()
-            # theta_low = self.theta
-            # z_low =     self.h
-            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            # for ibottom in range(1,len(air_ap_tail_orig)):
-            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
-            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-            # make theta increase strong enough to avoid numerical
-            # instability
-            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            air_ap_tail = pd.DataFrame()
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            theta_low = self.theta
-            z_low =     self.h
-            ibottom = 0
-            itop = 0
-            # print(air_ap_tail_orig)
-            # stop
-
-            # HW: this is the lower limit that we use for gammatheta, which is
-            # there to avoid model crashes. Besides on this limit, the upper
-            # air profile is modified in a way that is still conserves total
-            # quantities of moisture and temperature. The limit is set by trial
-            # and error. The numerics behind the crash should be investigated
-            # so that a cleaner solution can be provided.
-            gammatheta_lower_limit = 0.002
-            while ((itop in range(0,1)) or (itop != ibottom)):
-                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-                if (
-                    #(z_mean > (z_low+0.2)) and \
-                    #(theta_mean > (theta_low+0.02) ) and \
-                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
-                  (itop >= (len(air_ap_tail_orig)-1)) \
-                   :
-
-                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                    ibottom = itop+1
-                    theta_low = air_ap_tail.theta.iloc[-1]
-                    z_low =     air_ap_tail.z.iloc[-1]
-    
-
-                itop +=1
-                # elif  (itop > len(air_ap_tail_orig)-10):
-                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-                #print(itop,ibottom)
-
-            if itop > 1:
-                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! Temperature profile was too steep. \
-                                  Modifying profile: "+ \
-                                  str(itop - 1)+ " measurements were dropped \
-                                  and replaced with its average \
-                                  Modifying profile. \
-                                  mean with next profile point(s).") 
-
-
-            self.air_ap = pd.concat((air_ap_head,\
-                                     air_ap_tail,\
-                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
-                                                                      axis=1)
-
-            if  self.sw_ac:
-                qvalues = \
-                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
-
-                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
-                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
-                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-                self.P_h    = self.Ps - self.rho * self.g * self.h
-                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
-                        [self.Ps,  self.P_h, self.P_h-0.1]
-
-                self.air_ap.t = \
-                            self.air_ap.theta * \
-                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
-
-
-        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
-
-
-
-
-        # else:
-            # in the other case, it is updated at the time the statistics are
-            # calculated 
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if in_ml.sum() == 0:
-                warnings.warn(" no circulation points in the mixed layer \
-                              found. We just take the bottom one.")
-                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-            for var in ['t','q','u','v']:
-
-                # calculation of the advection variables for the mixed-layer
-                # these will be used for the next timestep
-                # Warning: w is excluded for now.
-
-                self.__dict__['adv'+var] = \
-                        ((self.air_ac['adv'+var+'_x'][in_ml] \
-                         + \
-                         self.air_ac['adv'+var+'_y'][in_ml])* \
-                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                        self.air_ac['delpdgrav'][in_ml].sum()
-
-                # calculation of the advection variables for the profile above
-                # the mixed layer (also for the next timestep)
-                self.air_ap['adv'+var] = \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p,\
-                                              self.air_ac['adv'+var+'_x']) \
-                                    + \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p, \
-                                              self.air_ac['adv'+var+'_y'])
-                # if var == 't':
-                #     print(self.air_ap['adv'+var])
-                #     stop
-
-            # as an approximation, we consider that advection of theta in the
-            # mixed layer is equal to advection of t. This is a sufficient
-            # approximation since theta and t are very similar at the surface
-            # pressure.
-
-            self.__dict__['advtheta'] = self.__dict__['advt']
-
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            # update the vertical wind profile
-            self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                          self.air_ac.p, \
-                                          self.air_ac['wp'])
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-            
-            air_apwold = self.air_ap['w']
-            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-            #print('hello w upd')
-
-            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # # self.wrho = np.interp(self.P_h,\
-            # #                      self.air_ach.p,\
-            # #                      self.air_ach['wrho']) \
-
-
-
-            # Also update the vertical wind at the mixed-layer height
-            # (subsidence)
-            self.ws   = self.air_ap.w[1]
-        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
-
-            ## Finally, we update he 
-            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-            #                        + \
-            #                        self.air_ac['divU_y'][in_ml])* \
-            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-            #            self.air_ac['delpdgrav'][in_ml].sum() 
-            
-
-        if self.sw_ap:
-            for var in ['theta','q','u','v']:
-
-                # update of the slope (gamma) for the different variables, for
-                # the next timestep!
-
-                # there is an warning message that tells about dividing through
-                # zero, which we ignore
-
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                    # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap['gamma'+var] = gammavar
-
-                # Based on the above, update the gamma value at the mixed-layer
-                # top
-                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
-                                                                     self.air_ap.z)[0][-1]]
-
-            
-    def run_radiation(self):
-        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
-        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
-        sinlea = max(sinlea, 0.0001)
-        
-        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
-  
-        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
-  
-        self.Swin  = self.S0 * Tr * sinlea
-        self.Swout = self.alpha * self.S0 * Tr * sinlea
-        
-        
-        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
-        self.Lwout = self.bolz * self.Ts ** 4.
-          
-        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
-        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
-  
-    def run_surface_layer(self):
-        # HW: I had to raise the minimum wind speed to make the simulation with
-        # the non-iterative solution stable (this solution was a wild guess, so I don't
-        # know the exact problem of the instability in case of very low wind
-        # speeds yet)
-        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        # version of 20180730 where there are still some runs crashing. Maybe
-        # an upper limit should be set on the monin-obukhov length instead of
-        # a lower limmit on the wind speed?
-        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        
-        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
-        qsatsurf       = qsat(self.thetasurf, self.Ps)
-        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
-        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
-
-        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
-  
-        zsl       = 0.1 * self.h
-        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
-        
-
-
-        if self.sw_lit:
-            self.Rib  = min(self.Rib, 0.2)
-            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
-            self.zeta  = zsl/self.L
-            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
-            
-        
-            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
-            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
-            
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-        
-     
-            # diagnostic meteorological variables
-            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
-            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
-            
-            # diagnostic meteorological variables
-        else:
-            
-            ## circumventing any iteration with Wouters et al., 2012
-            self.zslz0m = np.max((zsl/self.z0m,10.))
-            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
-            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
-            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
-            self.L = zsl/self.zeta
-            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
-        
-            self.Cm = self.k**2.0/funm/funm
-            self.Cs = self.k**2.0/funm/funh
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-            
-            # extrapolation from mixed layer (instead of from surface) to 2meter
-            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
-            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
-            self.u2m    =                - self.uw     / self.ustar / self.k * funm
-            self.v2m    =                - self.vw     / self.ustar / self.k * funm
-        
-        
-        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
-        self.e2m    = self.q2m * self.Ps / 0.622
-     
-    def ribtol(self, Rib, zsl, z0m, z0h): 
-        if(Rib > 0.):
-            L    = 1.
-            L0   = 2.
-        else:
-            L  = -1.
-            L0 = -2.
-        #print(Rib,zsl,z0m,z0h)
-        
-        while (abs(L - L0) > 0.001):
-            L0      = L
-            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
-            Lstart  = L - 0.001*L
-            Lend    = L + 0.001*L
-            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
-                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-            L       = L - fx / fxdif
-            #print(L)
-            if(abs(L) > 1e12):
-                break
-
-        return L
-      
-    def psim(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psim = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-        return psim
-      
-    def psih(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psih  = 2. * np.log( (1. + x*x) / 2.)
-            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-        return psih
- 
-    def jarvis_stewart(self):
-        # calculate surface resistances using Jarvis-Stewart model
-        if(self.sw_rad):
-            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
-        else:
-            f1 = 1.
-  
-        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
-            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
-        else:
-            f2 = 1.e8
- 
-        # Limit f2 in case w2 > wfc, where f2 < 1
-        f2 = max(f2, 1.);
- 
-        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
-        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
-  
-        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
-
-    def factorial(self,k):
-        factorial = 1
-        for n in range(2,k+1):
-            factorial = factorial * float(n)
-        return factorial;
-
-    def E1(self,x):
-        E1sum = 0
-        for k in range(1,100):
-            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
-        return -0.57721566490153286060 - np.log(x) - E1sum
- 
-    def ags(self):
-        # Select index for plant type
-        if(self.c3c4 == 'c3'):
-            c = 0
-        elif(self.c3c4 == 'c4'):
-            c = 1
-        else:
-            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
-
-        # calculate CO2 compensation concentration
-        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
-
-        # calculate mesophyll conductance
-        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
-                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
-        gm            = gm / 1000. # conversion from mm s-1 to m s-1
-  
-        # calculate CO2 concentration inside the leaf (ci)
-        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
-        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
-  
-        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
-        D0            = (self.f0[c] - fmin) / self.ad[c]
-  
-        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
-        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
-        ci            = cfrac * (co2abs - CO2comp) + CO2comp
-  
-        # calculate maximal gross primary production in high light conditions (Ag)
-        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
-  
-        # calculate effect of soil moisture stress on gross assimilation rate
-        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
-  
-        # calculate stress function
-        if (self.c_beta == 0):
-            fstr = betaw;
-        else:
-            # Following Combe et al (2016)
-            if (self.c_beta < 0.25):
-                P = 6.4 * self.c_beta
-            elif (self.c_beta < 0.50):
-                P = 7.6 * self.c_beta - 0.3
-            else:
-                P = 2**(3.66 * self.c_beta + 0.34) - 1
-            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
-  
-        # calculate gross assimilation rate (Am)
-        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
-        Rdark        = (1. / 9.) * Am
-        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
-  
-        # calculate  light use efficiency
-        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
-  
-        # calculate gross primary productivity
-        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
-  
-        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
-        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
-        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
-  
-        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
-        a1           = 1. / (1. - self.f0[c])
-        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
-  
-        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
-  
-        # calculate surface resistance for moisture and carbon dioxide
-        self.rs      = 1. / (1.6 * gcco2)
-        rsCO2        = 1. / gcco2
-  
-        # calculate net flux of CO2 into the plant (An)
-        An           = -(co2abs - ci) / (self.ra + rsCO2)
-  
-        # CO2 soil surface flux
-        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
-        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
-  
-        # CO2 flux
-        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
-        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
-        self.wCO2    = self.wCO2A + self.wCO2R
- 
-    def run_land_surface(self):
-        # compute ra
-        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
-        #print('ueff',self.u,self.v,self.wstar)
-
-        if(self.sw_sl):
-          self.ra = (self.Cs * ueff)**-1.
-        else:
-          self.ra = ueff / max(1.e-3, self.ustar)**2.
-
-        #print('ra',self.ra,self.ustar,ueff)
-
-        # first calculate essential thermodynamic variables
-        self.esat    = esat(self.theta)
-        self.qsat    = qsat(self.theta, self.Ps)
-        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
-        self.dqsatdT = 0.622 * desatdT / self.Ps
-        self.e       = self.q * self.Ps / 0.622
-
-        if(self.ls_type == 'js'): 
-            self.jarvis_stewart() 
-        elif(self.ls_type == 'ags'):
-            self.ags()
-        else:
-            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
-
-        # recompute f2 using wg instead of w2
-        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
-          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
-        else:
-          f2        = 1.e8
-        self.rssoil = self.rssoilmin * f2 
- 
-        Wlmx = self.LAI * self.Wmax
-        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
-        self.cliq = min(1., self.Wl / Wlmx) 
-     
-        # calculate skin temperature implictly
-        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
-            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
-            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
-            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
-
-        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
-        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
-        #print('Ts',self.rs)
-
-        esatsurf      = esat(self.Ts)
-        self.qsatsurf = qsat(self.Ts, self.Ps)
-
-        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-  
-        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
-  
-        self.LE     = self.LEsoil + self.LEveg + self.LEliq
-        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
-        #print('H',self.ra,self.Ts,self.theta)
-        self.G      = self.Lambda * (self.Ts - self.Tsoil)
-        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
-        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
-        
-        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
-  
-        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
-   
-        d1          = 0.1
-        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
-        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
-        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
-        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
-  
-        # calculate kinematic heat fluxes
-        self.wtheta   = self.H  / (self.rho * self.cp)
-        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
-        self.wq       = self.LE / (self.rho * self.Lv)
- 
-    def integrate_land_surface(self):
-        # integrate soil equations
-        Tsoil0        = self.Tsoil
-        wg0           = self.wg
-        Wl0           = self.Wl
-  
-        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
-        self.wg       = wg0     + self.dtcur * self.wgtend
-        self.Wl       = Wl0     + self.dtcur * self.Wltend
-  
-    # store model output
-    def store(self):
-        t                      = self.t
-        
-        self.out.time[t]          = t * self.dt / 3600. + self.tstart
-
-        # in case we are at the end of the simulation, we store the vertical
-        # profiles to the output
-        
-        # if t == (len(self.out.time) - 1):
-        #     self.out.air_ac = self.air_ac
-        #     self.out.air_ap = self.air_ap
-
-        
-        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
-        #  for key in self.out.__dict__.keys():
-        #      if key in self.__dict__:
-        #          self.out.__dict__[key][t]  = self.__dict__[key]
-        
-        self.out.h[t]          = self.h
-        
-        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
-        
-        self.out.gammatheta[t] = self.gammatheta
-        self.out.gammau[t]     = self.gammau
-        self.out.gammav[t]     = self.gammav
-        self.out.gammaq[t]     = self.gammaq
-        self.out.theta[t]      = self.theta
-        self.out.thetav[t]     = self.thetav
-        self.out.dtheta[t]     = self.dtheta
-        self.out.dthetav[t]    = self.dthetav
-        self.out.wtheta[t]     = self.wtheta
-        self.out.wthetav[t]    = self.wthetav
-        self.out.wthetae[t]    = self.wthetae
-        self.out.wthetave[t]   = self.wthetave
-        
-        self.out.q[t]          = self.q
-        self.out.dq[t]         = self.dq
-        self.out.wq[t]         = self.wq
-        self.out.wqe[t]        = self.wqe
-        self.out.wqM[t]        = self.wqM
-      
-        self.out.qsat[t]       = self.qsat
-        self.out.e[t]          = self.e
-        self.out.esat[t]       = self.esat
-      
-        fac = (self.rho*self.mco2)/self.mair
-        self.out.CO2[t]        = self.CO2
-        self.out.dCO2[t]       = self.dCO2
-        self.out.wCO2[t]       = self.wCO2  * fac
-        self.out.wCO2e[t]      = self.wCO2e * fac
-        self.out.wCO2R[t]      = self.wCO2R * fac
-        self.out.wCO2A[t]      = self.wCO2A * fac
-
-        self.out.u[t]          = self.u
-        self.out.du[t]         = self.du
-        self.out.uw[t]         = self.uw
-        
-        self.out.v[t]          = self.v
-        self.out.dv[t]         = self.dv
-        self.out.vw[t]         = self.vw
-        
-        self.out.T2m[t]        = self.T2m
-        self.out.q2m[t]        = self.q2m
-        self.out.u2m[t]        = self.u2m
-        self.out.v2m[t]        = self.v2m
-        self.out.e2m[t]        = self.e2m
-        self.out.esat2m[t]     = self.esat2m
-
-
-        self.out.Tsoil[t]      = self.Tsoil
-        self.out.T2[t]         = self.T2
-        self.out.Ts[t]         = self.Ts
-        self.out.wg[t]         = self.wg
-        
-        self.out.thetasurf[t]  = self.thetasurf
-        self.out.thetavsurf[t] = self.thetavsurf
-        self.out.qsurf[t]      = self.qsurf
-        self.out.ustar[t]      = self.ustar
-        self.out.Cm[t]         = self.Cm
-        self.out.Cs[t]         = self.Cs
-        self.out.L[t]          = self.L
-        self.out.Rib[t]        = self.Rib
-  
-        self.out.Swin[t]       = self.Swin
-        self.out.Swout[t]      = self.Swout
-        self.out.Lwin[t]       = self.Lwin
-        self.out.Lwout[t]      = self.Lwout
-        self.out.Q[t]          = self.Q
-  
-        self.out.ra[t]         = self.ra
-        self.out.rs[t]         = self.rs
-        self.out.H[t]          = self.H
-        self.out.LE[t]         = self.LE
-        self.out.LEliq[t]      = self.LEliq
-        self.out.LEveg[t]      = self.LEveg
-        self.out.LEsoil[t]     = self.LEsoil
-        self.out.LEpot[t]      = self.LEpot
-        self.out.LEref[t]      = self.LEref
-        self.out.G[t]          = self.G
-
-        self.out.zlcl[t]       = self.lcl
-        self.out.RH_h[t]       = self.RH_h
-
-        self.out.ac[t]         = self.ac
-        self.out.M[t]          = self.M
-        self.out.dz[t]         = self.dz_h
-        self.out.substeps[t]   = self.substeps
-  
-    # delete class variables to facilitate analysis in ipython
-    def exitmodel(self):
-        del(self.Lv)
-        del(self.cp)
-        del(self.rho)
-        del(self.k)
-        del(self.g)
-        del(self.Rd)
-        del(self.Rv)
-        del(self.bolz)
-        del(self.S0)
-        del(self.rhow)
-  
-        del(self.t)
-        del(self.dt)
-        del(self.tsteps)
-         
-        del(self.h)          
-        del(self.Ps)        
-        del(self.fc)        
-        del(self.ws)
-        del(self.we)
-        
-        del(self.theta)
-        del(self.dtheta)
-        del(self.gammatheta)
-        del(self.advtheta)
-        del(self.beta)
-        del(self.wtheta)
-    
-        del(self.T2m)
-        del(self.q2m)
-        del(self.e2m)
-        del(self.esat2m)
-        del(self.u2m)
-        del(self.v2m)
-        
-        del(self.thetasurf)
-        del(self.qsatsurf)
-        del(self.thetav)
-        del(self.dthetav)
-        del(self.thetavsurf)
-        del(self.qsurf)
-        del(self.wthetav)
-        
-        del(self.q)
-        del(self.qsat)
-        del(self.dqsatdT)
-        del(self.e)
-        del(self.esat)
-        del(self.dq)
-        del(self.gammaq)
-        del(self.advq)
-        del(self.wq)
-        
-        del(self.u)
-        del(self.du)
-        del(self.gammau)
-        del(self.advu)
-        
-        del(self.v)
-        del(self.dv)
-        del(self.gammav)
-        del(self.advv)
-  
-        del(self.htend)
-        del(self.thetatend)
-        del(self.dthetatend)
-        del(self.qtend)
-        del(self.dqtend)
-        del(self.utend)
-        del(self.dutend)
-        del(self.vtend)
-        del(self.dvtend)
-     
-        del(self.Tsoiltend) 
-        del(self.wgtend)  
-        del(self.Wltend) 
-  
-        del(self.ustar)
-        del(self.uw)
-        del(self.vw)
-        del(self.z0m)
-        del(self.z0h)        
-        del(self.Cm)         
-        del(self.Cs)
-        del(self.L)
-        del(self.Rib)
-        del(self.ra)
-  
-        del(self.lat)
-        del(self.lon)
-        del(self.doy)
-        del(self.tstart)
-   
-        del(self.Swin)
-        del(self.Swout)
-        del(self.Lwin)
-        del(self.Lwout)
-        del(self.cc)
-  
-        del(self.wg)
-        del(self.w2)
-        del(self.cveg)
-        del(self.cliq)
-        del(self.Tsoil)
-        del(self.T2)
-        del(self.a)
-        del(self.b)
-        del(self.p)
-        del(self.CGsat)
-  
-        del(self.wsat)
-        del(self.wfc)
-        del(self.wwilt)
-  
-        del(self.C1sat)
-        del(self.C2ref)
-  
-        del(self.LAI)
-        del(self.rs)
-        del(self.rssoil)
-        del(self.rsmin)
-        del(self.rssoilmin)
-        del(self.alpha)
-        del(self.gD)
-  
-        del(self.Ts)
-  
-        del(self.Wmax)
-        del(self.Wl)
-  
-        del(self.Lambda)
-        
-        del(self.Q)
-        del(self.H)
-        del(self.LE)
-        del(self.LEliq)
-        del(self.LEveg)
-        del(self.LEsoil)
-        del(self.LEpot)
-        del(self.LEref)
-        del(self.G)
-  
-        del(self.sw_ls)
-        del(self.sw_rad)
-        del(self.sw_sl)
-        del(self.sw_wind)
-        del(self.sw_shearwe)
-
-# class for storing mixed-layer model output data
-class model_output:
-    def __init__(self, tsteps):
-        self.time          = np.zeros(tsteps)    # time [s]
-
-        # mixed-layer variables
-        self.h          = np.zeros(tsteps)    # ABL height [m]
-        
-        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammau     = np.zeros(tsteps)
-        self.gammav     = np.zeros(tsteps)
-        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
-        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
-        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
-        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
-        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
-        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
-        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
-        
-        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
-        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
-        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
-        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
-
-        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
-        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
-        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
-
-        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
-        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
-        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
-        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
-        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
-        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
-        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
-        
-        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
-        
-        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
-
-        # diagnostic meteorological variables
-        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
-        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
-        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
-        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
-        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
-        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
-
-        # ground variables
-        self.Tsoil       = np.zeros(tsteps)
-        self.T2          = np.zeros(tsteps)
-        self.Ts          = np.zeros(tsteps)
-        self.wg          = np.zeros(tsteps)
-
-        # surface-layer variables
-        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
-        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
-        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
-        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
-        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
-        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
-        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
-        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
-        self.L          = np.zeros(tsteps)    # Obukhov length [m]
-        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
-
-        # radiation variables
-        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
-        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
-        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
-        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
-        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
-
-        # land surface variables
-        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
-        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
-        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
-        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
-        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
-        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
-        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
-        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
-        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
-        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
-
-        # Mixed-layer top variables
-        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
-        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
-
-        # cumulus variables
-        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
-        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
-        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
-        
-        
-        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
-
-# class for storing mixed-layer model input data
-class model_input:
-    def __init__(self):
-
-        # # comment not valid
-        # we comment out the initialization, because there is a problem when
-        # inheriting values from one the another class4gl_iput. We also expect
-        # that the user specifies all the required parmameters (if not, an error
-        # is raised). 
-
-        # general model variables
-        self.runtime    = None  # duration of model run [s]
-        self.dt         = None  # time step [s]
-
-        # mixed-layer variables
-        self.sw_ml      = None  # mixed-layer model switch
-        self.sw_shearwe = None  # Shear growth ABL switch
-        self.sw_fixft   = None  # Fix the free-troposphere switch
-        self.h          = None  # initial ABL height [m]
-        self.Ps         = None  # surface pressure [Pa]
-        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
-        self.fc         = None  # Coriolis parameter [s-1]
-        
-        self.theta      = None  # initial mixed-layer potential temperature [K]
-        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
-
-        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
-
-        self.dtheta     = None  # initial temperature jump at h [K]
-        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = None  # advection of heat [K s-1]
-        self.beta       = None  # entrainment ratio for virtual heat [-]
-        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
-        
-        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
-        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
-        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
-
-        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = None  # advection of moisture [kg kg-1 s-1]
-        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
-
-        self.CO2        = None  # initial mixed-layer potential temperature [K]
-        self.dCO2       = None  # initial temperature jump at h [K]
-        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advCO2     = None  # advection of heat [K s-1]
-        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
-        
-        self.sw_wind    = None  # prognostic wind switch
-        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.du         = None  # initial u-wind jump at h [m s-1]
-        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = None  # advection of u-wind [m s-2]
-
-        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = None  # initial u-wind jump at h [m s-1]
-        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = None  # advection of v-wind [m s-2]
-
-        # surface layer variables
-        self.sw_sl      = None  # surface layer switch
-        self.ustar      = None  # surface friction velocity [m s-1]
-        self.z0m        = None  # roughness length for momentum [m]
-        self.z0h        = None  # roughness length for scalars [m]
-        self.Cm         = None  # drag coefficient for momentum [-]
-        self.Cs         = None  # drag coefficient for scalars [-]
-        self.L          = None  # Obukhov length [-]
-        self.Rib        = None  # bulk Richardson number [-]
-
-        # radiation parameters
-        self.sw_rad     = None  # radiation switch
-        self.lat        = None  # latitude [deg]
-        self.lon        = None  # longitude [deg]
-        self.doy        = None  # day of the year [-]
-        self.tstart     = None  # time of the day [h UTC]
-        self.cc         = None  # cloud cover fraction [-]
-        self.Q          = None  # net radiation [W m-2] 
-        self.dFz        = None  # cloud top radiative divergence [W m-2] 
-
-        # land surface parameters
-        self.sw_ls      = None  # land surface switch
-        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
-        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = None  # temperature top soil layer [K]
-        self.T2         = None  # temperature deeper soil layer [K]
-        
-        self.a          = None  # Clapp and Hornberger retention curve parameter a
-        self.b          = None  # Clapp and Hornberger retention curve parameter b
-        self.p          = None  # Clapp and Hornberger retention curve parameter p 
-        self.CGsat      = None  # saturated soil conductivity for heat
-        
-        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
-        self.wfc        = None  # volumetric water content field capacity [-]
-        self.wwilt      = None  # volumetric water content wilting point [-]
-        
-        self.C1sat      = None 
-        self.C2ref      = None
-
-        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
-        
-        self.LAI        = None  # leaf area index [-]
-        self.gD         = None  # correction factor transpiration for VPD [-]
-        self.rsmin      = None  # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = None  # surface albedo [-]
-        
-        self.Ts         = None  # initial surface temperature [K]
-        
-        self.cveg       = None  # vegetation fraction [-]
-        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
-        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
-        
-        self.Lambda     = None  # thermal diffusivity skin layer [-]
-
-        # A-Gs parameters
-        self.c3c4       = None  # Plant type ('c3' or 'c4')
-
-        # Cumulus parameters
-        self.sw_cu      = None  # Cumulus parameterization switch
-        self.dz_h       = None  # Transition layer thickness [m]
-        
-# BEGIN -- HW 20171027
-        # self.cala       = None      # soil heat conductivity [W/(K*m)]
-        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
-# END -- HW 20171027
diff --git a/class4gl.py b/class4gl.py
deleted file mode 100644
index 7baaa51..0000000
--- a/class4gl.py
+++ /dev/null
@@ -1,1611 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-
-Created on Mon Jan 29 12:33:51 2018
-
-Module file for class4gl, which  extents the class-model to be able to take
-global air profiles as input. It exists of:
-
-CLASSES:
-    - an input object, namely class4gl_input. It includes:
-        - a function to read Wyoming sounding data from a yyoming stream object
-        - a function to read global data from a globaldata library object 
-    - the model object: class4gl
-    - ....    
-
-DEPENDENCIES:
-    - xarray
-    - numpy
-    - data_global
-    - Pysolar
-    - yaml
-
-@author: Hendrik Wouters
-
-"""
-
-
-
-""" Setup of envirnoment """
-
-# Standard modules of the stand class-boundary-layer model
-from model import model
-from model import model_output as class4gl_output
-from model import model_input
-from model import qsat
-#from data_soundings import wyoming 
-import Pysolar
-import yaml
-import logging
-import warnings
-import pytz
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-
-# Generic Python Packages
-import numpy as np
-import datetime as dt
-import pandas as pd
-import xarray as xr
-import io
-#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
-from data_global import data_global
-grav = 9.81
-
-# this is just a generic input object
-class generic_input(object):
-    def __init__(self):
-        self.init = True
-
-
-# all units from all variables in CLASS(4GL) should be defined here!
-units = {
-         'h':'m',
-         'theta':'K', 
-         'q':'kg/kg',
-         'cc': '-',
-         'cveg': '-',
-         'wg': 'm3 m-3',
-         'w2': 'm3 m-3',
-         #'wg': 'kg/kg',
-         'Tsoil': 'K',
-         'T2': 'K',
-         'z0m': 'm',
-         'alpha': '-',
-         'LAI': '-',
-         'dhdt':'m/h',
-         'dthetadt':'K/h',
-         'dqdt':'kg/kg/h',
-         'BR': '-',
-         'EF': '-',
-}
-
-class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
-
-    def __init__(self,set_pars_defaults=True,debug_level=None):
-
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        print('hello')
-        self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
-     
-        # # add ch to logger
-        # self.logger.addHandler(ch)
-
-        # """ end set up logger """
-
-
-
-        # these are the standard model input single-value parameters for class
-        self.pars = model_input()
-
-        # diagnostic parameters of the initial profile
-        self.diag = dict()
-
-        # In this variable, we keep track of the different parameters from where it originates from. 
-        self.sources = {}
-
-        if set_pars_defaults:
-            self.set_pars_defaults()
-
-    def set_pars_defaults(self):
-
-        """ 
-        Create empty model_input and set up case
-        """
-        defaults = dict( 
-        dt         = 60.    , # time step [s] 
-        runtime    = 6*3600 ,  # total run time [s]
-        
-        # mixed-layer input
-        sw_ml      = True   ,  # mixed-layer model switch
-        sw_shearwe = False  ,  # shear growth mixed-layer switch
-        sw_fixft   = False  ,  # Fix the free-troposphere switch
-        h          = 200.   ,  # initial ABL height [m]
-        Ps         = 101300.,  # surface pressure [Pa]
-        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
-        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
-        
-        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
-        dtheta     = 1.     ,  # initial temperature jump at h [K]
-        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
-        advtheta   = 0.     ,  # advection of heat [K s-1]
-        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
-        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
-        
-        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
-        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
-        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
-        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
-        
-        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
-        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
-        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
-        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
-        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
-        sw_wind    = True  ,  # prognostic wind switch
-        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
-        du         = 0.     ,  # initial u-wind jump at h [m s-1]
-        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
-        advu       = 0.     ,  # advection of u-wind [m s-2]
-        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
-        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
-        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
-        advv       = 0.     ,  # advection of v-wind [m s-2]
-        sw_sl      = True   , # surface layer switch
-        ustar      = 0.3    ,  # surface friction velocity [m s-1]
-        z0m        = 0.02   ,  # roughness length for momentum [m]
-        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
-        sw_rad     = True   , # radiation switch
-        lat        = 51.97  ,  # latitude [deg]
-        lon        = -4.93  ,  # longitude [deg]
-        doy        = 268.   ,  # day of the year [-]
-        tstart     = 6.8    ,  # time of the day [h UTC]
-        cc         = 0.0    ,  # cloud cover fraction [-]
-        Q          = 400.   ,  # net radiation [W m-2] 
-        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
-        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
-        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
-        cveg       = 0.85   ,  # vegetation fraction [-]
-        Tsoil      = 295.   ,  # temperature top soil layer [K]
-        Ts         = 295.   ,    # initial surface temperature [K]
-        T2         = 296.   ,  # temperature deeper soil layer [K]
-        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
-        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
-        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
-        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
-        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
-        wfc        = 0.323  ,  # volumetric water content field capacity [-]
-        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
-        C1sat      = 0.132  ,  
-        C2ref      = 1.8    ,
-        LAI        = 2.     ,  # leaf area index [-]
-        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
-        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
-        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
-        alpha      = 0.25   ,  # surface albedo [-]
-        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
-        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
-        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
-        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
-        sw_cu      = False  ,  # Cumulus parameterization switch
-        dz_h       = 150.   ,  # Transition layer thickness [m]
-        cala       = None   ,  # soil heat conductivity [W/(K*m)]
-        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
-        sw_ls      = True   ,
-        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
-        sw_lit     = False,
-        )
-        pars = model_input()
-        for key in defaults:
-            pars.__dict__[key] = defaults[key]
-        
-        self.update(source='defaults',pars=pars)
-        
-    def clear(self):
-        """ this procudure clears the class4gl_input """
-
-        for key in list(self.__dict__.keys()):
-            del(self.__dict__[key])
-        self.__init__()
-
-    def dump(self,file):
-        """ this procedure dumps the class4gl_input object into a yaml file
-            
-            Input: 
-                - self.__dict__ (internal): the dictionary from which we read 
-            Output:
-                - file: All the parameters in self.__init__() are written to
-                the yaml file, including pars, air_ap, sources etc.
-        """
-        file.write('---\n')
-        index = file.tell()
-        file.write('# CLASS4GL input; format version: 0.1\n')
-
-        # write out the position of the current record
-        yaml.dump({'index':index}, file, default_flow_style=False)
-
-        # we do not include the none values
-        for key,data in self.__dict__.items():
-            #if ((type(data) == model_input) or (type(class4gl_input):
-            if key == 'pars':
-
-                pars = {'pars' : self.__dict__['pars'].__dict__}
-                parsout = {}
-                for key in pars.keys():
-                    if pars[key] is not None:
-                        parsout[key] = pars[key]
-
-                yaml.dump(parsout, file, default_flow_style=False)
-            elif type(data) == dict:
-                if key == 'sources':
-                    # in case of sources, we want to have a
-                    # condensed list format as well, so we leave out
-                    # 'default_flow_style=False'
-                    yaml.dump({key : data}, file)
-                else: 
-                    yaml.dump({key : data}, file,
-                              default_flow_style=False)
-            elif type(data) == pd.DataFrame:
-                # in case of dataframes (for profiles), we want to have a
-                # condensed list format as well, so we leave out
-                # 'default_flow_style=False'
-                yaml.dump({key: data.to_dict(orient='list')},file)
-
-                # # these are trials to get it into a more human-readable
-                # fixed-width format, but it is too complex
-                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
-                #file.write(stream)
-                
-                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
-                #file.write(key+': !!str |\n')
-                #file.write(str(data)+'\n')
-       
-    def load_yaml_dict(self,yaml_dict,reset=True):
-        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
-            
-            Input: 
-                - yaml_dict: the dictionary from which we read 
-                - reset: reset data before reading        
-            Output:
-                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
-        """
-        
-        if reset:
-            for key in list(self.__dict__.keys()):
-                del(self.__dict__[key])
-            self.__init__()
-
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                self.__dict__[key] = model_input()
-                self.__dict__[key].__dict__ = data
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            elif key == 'sources':
-                self.__dict__[key] = data
-            elif key == 'diag':
-                self.__dict__[key] = data
-            else: 
-                warnings.warn("Key '"+key+"' may not be implemented.")
-                self.__dict__[key] = data
-
-    def update(self,source,**kwargs):
-        """ this procedure is to make updates of input parameters and tracking
-        of their source more convenient. It implements the assignment of
-        parameter source/sensitivity experiment IDs ('eg.,
-        'defaults', 'sounding balloon', any satellite information, climate
-        models, sensitivity tests etc.). These are all stored in a convenient
-        way with as class4gl_input.sources.  This way, the user can always consult with
-        from where parameters data originates from.  
-        
-        Input:
-            - source:    name of the underlying dataset
-            - **kwargs: a dictionary of data input, for which the key values
-            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
-            the values is a again a dictionary/dataframe of datakeys/columns
-            ('wg','PRES','datetime', ...) and datavalues (either single values,
-            profiles ...), eg., 
-
-                pars = {'wg': 0.007  , 'w2', 0.005}
-                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
-                                     300.,...]}
-            
-        Output:
-            - self.__dict__[datatype] : object to which the parameters are
-                                        assigned. They can be consulted with
-                                        self.pars, self.profiles, etc.
-                                        
-            - self.sources[source] : It supplements the overview overview of
-                                     data sources can be consulted with
-                                     self.sources. The structure is as follows:
-                                     as:
-                self.sources = { 
-                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
-                'GLEAM' :  ['pars:wg','pars:w2', ...],
-                 ...
-                }
-        
-        """
-
-        #print(source,kwargs)
-
-        for key,data in kwargs.items():
-
-            #print(key)
-            # if the key is not in class4gl_input object, then just add it. In
-            # that case, the update procedures below will just overwrite it 
-            if key not in self.__dict__:
-                self.__dict__[key] = data
-
-
-            
-
-            #... we do an additional check to see whether there is a type
-            # match. I not then raise a key error
-            if (type(data) != type(self.__dict__[key]) \
-                # we allow dict input for model_input pars
-                and not ((key == 'pars') and (type(data) == dict) and \
-                (type(self.__dict__[key]) == model_input))):
-
-                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
-
-
-            # This variable keeps track of the added data that is supplemented
-            # by the current source. We add this to class4gl_input.sources
-            datakeys = []
-
-            #... and we update the class4gl_input data, and this depends on the
-            # data type
-
-            if type(self.__dict__[key]) == pd.DataFrame:
-                # If the data type is a dataframe, then we update the columns
-                for column in list(data.columns):
-                    #print(column)
-                    self.__dict__[key][column] = data[column]
-                    datakeys.append(column)
-                    
-
-            elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
-                # dictionary of parameters
-                if type(data) == model_input:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data.__dict__}
-                    datakeys = list(data.__dict__.keys())
-                elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
-                    datakeys = list(data.keys())
-                else:
-                    raise TypeError('input key '+key+' is not of the same type\
-                                    as the one in the class4gl_object')
-
-            elif type(self.__dict__[key]) == dict:
-                # if the data type is a dictionary, we update the
-                # dictionary 
-                self.__dict__[key] = {self.__dict__[key] , data}
-                datakeys = list(data.keys())
-
-
-            # if source entry is not existing yet, we add it
-            if source not in self.sources.keys():
-                self.sources[source] = []
-
-
-            # self.logger.debug('updating section "'+\
-            #                  key+' ('+' '.join(datakeys)+')'\
-            #                  '" from source \
-            #                  "'+source+'"')
-
-            # Update the source dictionary: add the provided data keys to the
-            # specified source list
-            for datakey in datakeys:
-                # At first, remove the occurences of the keys in the other
-                # source lists
-                for sourcekey,sourcelist in self.sources.items():
-                    if key+':'+datakey in sourcelist:
-                        self.sources[sourcekey].remove(key+':'+datakey)
-                # Afterwards, add it to the current source list
-                self.sources[source].append(key+':'+datakey)
-
-
-        # # in case the datatype is a class4gl_input_pars, we update its keys
-        # # according to **kwargs dictionary
-        # if type(self.__dict__[datatype]) == class4gl_input_pars:
-        #     # add the data parameters to the datatype object dictionary of the
-        #     # datatype
-        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
-        #                                        **kwargs}
-        # # in case, the datatype reflects a dataframe, we update the columns according
-        # # to the *args list
-        # elif type(self.__dict__[datatype]) == pd.DataFrame:
-        #     for dataframe in args:
-        #         for column in list(dataframe.columns):
-        #             self.__dict__[datatype][column] = dataframe[column]
-        
-
-    def get_profile(self,IOBJ, *args, **argv):
-        # if type(IOBJ) == wyoming:
-        self.get_profile_wyoming(IOBJ,*args,**argv)
-        # else:
-        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
-        
-    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
-        """ 
-            Purpose: 
-                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
-
-            Input:
-                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
-                function will take the profile at the stream's current
-                position. 
-                2. air_ap_mode: which air profile do we take? 
-                    - b : best
-                    - l : according to lower limit for the mixed-layer height
-                            estimate
-                    - u : according to upper limit for the mixed-layer height
-                            estimate
-
-
-            Output:
-                1. all single-value parameters are stored in the
-                   class4gl_input.pars object
-                2. the souding profiles are stored in the in the
-                   class4gl_input.air_balloon dataframe
-                3. modified sounding profiles for which the mixed layer height
-                   is fitted
-                4. ...
-
-        """
-
-
-        # Raise an error in case the input stream is not the correct object
-        # if type(wy_strm) is not wyoming:
-        #    raise TypeError('Not a wyoming type input stream')
-
-        # Let's tell the class_input object that it is a Wyoming fit type
-        self.air_ap_type = 'wyoming'
-        # ... and which mode of fitting we apply
-        self.air_ap_mode = air_ap_mode
-
-        """ Temporary variables used for output """
-        # single value parameters derived from the sounding profile
-        dpars = dict()
-        # profile values
-        air_balloon = pd.DataFrame()
-        # fitted profile values
-        air_ap = pd.DataFrame()
-        
-        string = wy_strm.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = wy_strm.current.find_next('pre').find_next('pre').text
-        
-        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
-        dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
-               }
-        
-        # we get weird output when it's a numpy Timestamp, so we convert it to
-        # pd.datetime type
-
-        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
-        dpars['STNID'] = dpars['Station number']
-
-        # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
-        # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
-                              / \
-                             (air_balloon.MIXR/1000.+1.)
-        # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-        
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
-
-        
-
-        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-
-        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
-
-
-        # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        #is_valid = (air_balloon.z >= 0)
-        # # this is an alternative pipe/numpy method
-        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
-
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
-
-        if len(valid_indices) > 0:
-            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            
-            # the final mixed-layer height that will be used by class. We round it
-            # to 1 decimal so that we get a clean yaml output format
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
-            dpars['Ps'] = np.nan
-
-
-
-
-        if ~np.isnan(dpars['h']):
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u 
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-            
-
-
-
-        # First 3 data points of the mixed-layer fit. We create a empty head
-        # first
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-        
-        #calculate mixed-layer jump ( this should be larger than 0.1)
-        
-        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
-        
-        # make a row object for defining the jump
-        jump = air_ap_head.iloc[0] * np.nan
-            
-        if air_ap_tail.shape[0] > 1:
-
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = dpars['theta']
-        z_low =     dpars['h']
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                (z_mean > (z_low+10.)) and \
-                (theta_mean > (theta_low+0.2) ) and \
-                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-
-
-
-
-
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        #print(air_ap['PRES'].iloc[0])
-
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-
-        
-        dpars['lat'] = dpars['Station latitude']
-        dpars['latitude'] = dpars['lat']
-        
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        dpars['longitude'] = dpars['Station longitude']
-        
-        dpars['ldatetime'] = dpars['datetime'] \
-                            + \
-                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-
-        # # we make a pars object that is similar to the destination object
-        # pars = model_input()
-        # for key,value in dpars.items():
-        #     pars.__dict__[key] = value
-
-
-        # we round the columns to a specified decimal, so that we get a clean
-        # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
-# 
-        for column,decimal in decimals.items():
-            air_balloon[column] = air_balloon[column].round(decimal)
-            air_ap[column] = air_ap[column].round(decimal)
-
-        self.update(source='wyoming',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-
-        
-    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
-    
-        """
-        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
-                 according to the position (lat lon) and the class datetime and timespan
-                 globaldata should be a globaldata multifile object
-        
-        Input: 
-            - globaldata: this is the library object
-            - only_keys: only extract specified keys
-            - exclude_keys: do not inherit specified keys
-        """
-        classdatetime      = np.datetime64(self.pars.datetime_daylight)
-        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
-                                           + \
-                                           dt.timedelta(seconds=self.pars.runtime)\
-                                          )
-
-
-        # # list of variables that we get from global ground data
-        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
-        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
-        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
-        #                 'texture', 'itex', 'isoil', 'BR',
-        #                 'b', 'cveg',
-        #                 'C1sat', 
-        #                 'C2ref', 'p', 'a',
-        #                 ] #globaldata.datasets.keys():
-
-        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
-        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
-
-
-        if type(globaldata) is not data_global:
-            raise TypeError("Wrong type of input library") 
-
-        # by default, we get all dataset keys
-        keys = list(globaldata.datasets.keys())
-
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
-
-        # # In case there is surface pressure, we also calculate the half-level
-        # # and full-level pressure fields
-        # if ('sp' in keys):
-        #     keys.append('pfull')
-        #     keys.append('phalf')
-
-        # If specified, we only take the keys that are in only_keys
-        if only_keys is not None:
-            for key in keys:
-                if key not in only_keys:
-                    keys.remove(key)
-                
-        # If specified, we take out keys that are in exclude keys
-        if exclude_keys is not None:
-            for key in keys:
-                if key in exclude_keys:
-                    keys.remove(key)
-
-        # we set everything to nan first in the pars section (non-profile parameters
-        # without lev argument), so that we can check afterwards whether the
-        # data is well-fetched or not.
-        for key in keys:
-            if not ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None) and \
-                ('lev' in globaldata.datasets[key].page[key].dims)):
-                self.update(source='globaldata',pars={key:np.nan})
-            # # we do not check profile input for now. We assume it is
-            # # available
-            #else:
-            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
-
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
-
-        for key in keys:
-            # If we find it, then we obtain the variables
-            if ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None)):
-
-                # check first whether the dataset has a height coordinate (3d space)
-                if 'lev' in globaldata.datasets[key].page[key].dims:
-
-                    # first, we browse to the correct file that has the current time
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-                        globaldata.datasets[key].browse_page(time=classdatetime)
-
-                    
-                    if (globaldata.datasets[key].page is not None):
-                        # find longitude and latitude coordinates
-                        ilats = (np.abs(globaldata.datasets[key].page.lat -
-                                        self.pars.latitude) < 0.5)
-                        ilons = (np.abs(globaldata.datasets[key].page.lon -
-                                        self.pars.longitude) < 0.5)
-                        
-                        # if we have a time dimension, then we look up the required timesteps during the class simulation
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
-
-                            # In case we didn't find any correct time, we take the
-                            # closest one.
-                            if np.sum(itimes) == 0.:
-
-
-                                classdatetimemean = \
-                                    np.datetime64(self.pars.datetime_daylight + \
-                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
-                                                ))
-
-                                dstimes = globaldata.datasets[key].page.time
-                                time = dstimes.sel(time=classdatetimemean,method='nearest')
-                                itimes = (globaldata.datasets[key].page.time ==
-                                          time)
-                                
-                        else:
-                            # we don't have a time coordinate so it doesn't matter
-                            # what itimes is
-                            itimes = 0
-
-                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
-
-                        # over which dimensions we take a mean:
-                        dims = globaldata.datasets[key].page[key].dims
-                        namesmean = list(dims)
-                        namesmean.remove('lev')
-                        idxmean = [dims.index(namemean) for namemean in namesmean]
-                        
-                        value = \
-                        globaldata.datasets[key].page[key].isel(time=itimes,
-                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
-
-                        # Ideally, source should be equal to the datakey of globaldata.library 
-                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
-                        #  but therefore the globaldata class requires a revision to make this work
-                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
-
-                else:
-                    # this procedure is for reading the ground fields (2d space). 
-                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-    
-                       # first, we browse to the correct file
-                       #print(key)
-                       globaldata.datasets[key].browse_page(time=classdatetime)
-    
-                    if globaldata.datasets[key].page is not None:
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - self.pars.latitude))
-                        ilat = np.where((DIST) == np.min(DIST))[0][0]
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - self.pars.longitude))
-                        ilon = np.where((DIST) == np.min(DIST))[0][0]
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - (self.pars.latitude + 0.5)))
-                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmax = ilat
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - (self.pars.longitude  + 0.5)))
-                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmax = ilon
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lat.values\
-                                - (self.pars.latitude - 0.5)))
-                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmin = ilat
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lon.values\
-                                - (self.pars.longitude  - 0.5)))
-                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmin = ilon        
-                        
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
-                        else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
-                            
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                            
-                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
-                            
-                            classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
-                                                             dt.timedelta(seconds=self.pars.runtime)\
-                                                            ) 
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
-                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
-                                idatetimeend -= 1
-                            idatetime = np.min((idatetime,idatetimeend))
-                            #for gleam, we take the previous day values
-                            if key in ['wg', 'w2']:
-                                idatetime = idatetime - 1
-                                idatetimeend = idatetimeend - 1
-
-                            # in case of soil temperature, we take the exact
-                            # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
-                                idatetimeend = idatetime
-                            
-                            idts = range(idatetime,idatetimeend+1)
-                            
-                            count = 0
-                            self.__dict__[key] = 0.
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    for iidts in idts:
-                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
-                                        count += 1
-                            value = value/count
-                            self.update(source='globaldata',pars={key:value.item()})
-                                
-                        else:
-                                
-                            count = 0
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
-                                    count += 1
-                            value = value/count                        
-
-                            self.update(source='globaldata',pars={key:value.item()})
-
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            self.logger.debug('also update LAI based on LAIpixel and cveg') 
-            # I suppose LAI pixel is already determined in the previous
-            # procedure. Anyway...
-            key = 'LAIpixel'
-
-            if globaldata.datasets[key].page is not None:
-                # first, we browse to the correct file that has the current time
-                if 'time' in list(globaldata.datasets[key].page[key].dims):
-                    globaldata.datasets[key].browse_page(time=classdatetime)
-            
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - self.pars.latitude))
-                ilat = np.where((DIST) == np.min(DIST))[0][0]
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - self.pars.longitude))
-                ilon = np.where((DIST) == np.min(DIST))[0][0]
-                 
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude + 0.5)))
-                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmax = ilat
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values \
-                        - (self.pars.longitude  + 0.5)))
-                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmax = ilon
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude - 0.5)))
-                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmin = ilat
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - (self.pars.longitude  - 0.5)))
-                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmin = ilon        
-                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                
-                
-                if ilatmin < ilatmax:
-                    ilatrange = range(ilatmin,ilatmax+1)
-                else:
-                    ilatrange = range(ilatmax,ilatmin+1)
-                    
-                if ilonmin < ilonmax:
-                    ilonrange = range(ilonmin,ilonmax+1)
-                else:
-                    ilonrange = range(ilonmax,ilonmin+1)           
-                
-                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
-                LAIpixel = 0.
-                count = 0
-                for iilat in [ilat]: #ilatrange
-                    for iilon in [ilon]: #ilonrange
-                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
-                        
-                                        
-                        # if np.isnan(tarray[idatetime]):
-                        #     print("interpolating GIMMS LAIpixel nan value")
-                        #     
-                        #     mask = np.isnan(tarray)
-                        #     
-                        #     #replace each nan value with a interpolated value
-                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-                        #         
-                        #     else:
-                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
-                    
-                        #         tarray *= np.nan 
-                        
-                        count += 1
-                        #tarray_res += tarray
-                LAIpixel = LAIpixel/count
-                
-                count = 0
-                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
-  
-                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
-                #print('LAIpixel:',self.__dict__['LAIpixel'])
-                #print('cveg:',self.__dict__['cveg'])
-                
-                # finally, we rescale the LAI according to the vegetation
-                # fraction
-                value = 0. 
-                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
-                   value =self.pars.LAIpixel/self.pars.cveg
-                else:
-                    # in case of small vegetation fraction, we take just a standard 
-                    # LAI value. It doesn't have a big influence anyway for
-                    # small vegetation
-                    value = 2.
-                #print('LAI:',self.__dict__['LAI'])
-                self.update(source='globaldata',pars={'LAI':value}) 
-
-
-        # in case we have 'sp', we also calculate the 3d pressure fields at
-        # full level and half level
-        if ('sp' in keys) and ('sp' in self.pars.__dict__):
-            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
-
-            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # hydrostatic thickness of each model layer
-            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
-            # # dz = rhodz/(R * T / pfull)
-
-
-            # # subsidence multiplied by density. We calculate the subsidence of
-            # # the in class itself
-            # wrho = np.zeros_like(phalf)
-            # wrho[-1] = 0. 
-
-            # for ihlev in range(0,wrho.shape[0]-1):
-            #     # subsidence multiplied by density is the integral of
-            #     # divergences multiplied by the layer thicknessies
-            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
-            #                     self.air_ac['divU_y'][ihlev:]) * \
-            #                    delpdgrav[ihlev:]).sum()
-
-
-            
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'p':list(pfull)}))
-            self.update(source='globaldata',\
-                        air_ach=pd.DataFrame({'p':list(phalf)}))
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
-            # self.update(source='globaldata',\
-            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
-
-    def check_source(self,source,check_only_sections=None):
-        """ this procedure checks whether data of a specified source is valid.
-
-        INPUT:
-            source: the data source we want to check
-            check_only_sections: a string or list with sections to be checked
-        OUTPUT:
-            returns True or False
-        """
-
-        # we set source ok to false as soon as we find a invalid input
-        source_ok = True
-
-        # convert to a single-item list in case of a string
-        check_only_sections_def = (([check_only_sections]) if \
-                                   type(check_only_sections) is str else \
-                                    check_only_sections)
-                                  
-        if source not in self.sources.keys():
-            self.logger.info('Source '+source+' does not exist')
-            source_ok = False
-
-        for sectiondatakey in self.sources[source]:                             
-            section,datakey = sectiondatakey.split(':')                         
-            if ((check_only_sections_def is None) or \
-                (section in check_only_sections_def)):                          
-                checkdatakeys = []
-                if type(self.__dict__[section]) is pd.DataFrame:
-                    checkdata = self.__dict__[section]
-                elif type(self.__dict__[section]) is model_input:
-                    checkdata = self.__dict__[section].__dict__
-
-                if (datakey not in checkdata):                              
-                    # self.logger.info('Expected key '+datakey+\
-                    #                  ' is not in parameter input')                        
-                    source_ok = False                                           
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
-        
-                    # self.logger.info('Key value of "'+datakey+\
-                    #                  '" is invalid: ('+ \
-                    # str(self.__dict__[section].__dict__[datakey])+')')         
-                    source_ok = False
-
-        return source_ok
-
-    def check_source_globaldata(self):
-        """ this procedure checks whether all global parameter data is
-        available, according to the keys in the self.sources"""
-
-        source_globaldata_ok = True
-
-        #self.get_values_air_input()
-
-        # and now we can get the surface values
-        #class_settings = class4gl_input()
-        #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
-        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
-            source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
-        else:
-            # we only check the ground parameter data (pars section). The 
-            # profile data (air_ap section) are supposed to be valid in any 
-            # case.
-            source_ok = self.check_source(source='globaldata',\
-                                          check_only_sections=['air_ac',\
-                                                               'air_ap',\
-                                                               'pars'])
-            if not source_ok:
-                source_globaldata_ok = False
-        
-            # Additional check: we exclude desert-like
-            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
-                source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
-                source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
-            elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
-                source_globaldata_ok = False
-
-        return source_globaldata_ok
-
-
-class c4gli_iterator():
-    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
-    
-        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
-    """
-    def __init__(self,file):
-        # take file as IO stream
-        self.file = file
-        self.yaml_generator = yaml.load_all(file)
-        self.current_dict = {}
-        self.current_class4gl_input = class4gl_input()
-        separator = self.file.readline() # this is just dummy
-        self.header = file.readline()
-        if self.header != '# CLASS4GL record; format version: 0.1\n':
-            raise NotImplementedError("Wrong format version: '"+self.header+"'")
-    def __iter__(self):
-        return self
-    def __next__(self):
-        self.current_dict = self.yaml_generator.__next__()
-        self.current_class4gl_input.load_yaml_dict(self.current_dict)
-        return self.current_class4gl_input
-
-
-
-#get_cape and lift_parcel are adapted from the SkewT package
-    
-class gl_dia(object):
-    def get_lifted_index(self,timestep=-1):
-        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
-    
-#from SkewT
-#def get_lcl(startp,startt,startdp,nsteps=101):
-#    from numpy import interp
-#    #--------------------------------------------------------------------
-#    # Lift a parcel dry adiabatically from startp to LCL.
-#    # Init temp is startt in K, Init dew point is stwrtdp,
-#    # pressure levels are in Pa    
-#    #--------------------------------------------------------------------
-#
-#    assert startdp<=startt
-#
-#    if startdp==startt:
-#        return np.array([startp]),np.array([startt]),np.array([startdp]),
-#
-#    # Pres=linspace(startp,60000.,nsteps)
-#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
-#
-#    # Lift the dry parcel
-#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
-#    # Mixing ratio isopleth
-#    starte=VaporPressure(startdp)
-#    startw=MixRatio(starte,startp)
-#    e=Pres*startw/(.622+startw)
-#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
-#
-#    # Solve for the intersection of these lines (LCL).
-#    # interp requires the x argument (argument 2)
-#    # to be ascending in order!
-#    P_lcl=interp(0.,T_iso-T_dry,Pres)
-#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
-#
-#    # # presdry=linspace(startp,P_lcl)
-#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
-#
-#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
-#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
-#
-#    return P_lcl,T_lcl
-
-
-
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
-
-
-
-#from class
-def get_lcl(startp,startt,startqv):
-        # Find lifting condensation level iteratively
-    lcl = 20.
-    RHlcl = 0.5
-    
-    itmax = 30
-    it = 0
-    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it
Date: Tue, 21 Aug 2018 22:03:49 +0200
Subject: [PATCH 011/129] test

---
 interface_multi.py | 2061 --------------------------------------------
 ribtol.cpp         |   81 --
 ribtol.pyx         |   48 --
 ribtol_hw.py       |  165 ----
 4 files changed, 2355 deletions(-)
 delete mode 100644 interface_multi.py
 delete mode 100644 ribtol.cpp
 delete mode 100644 ribtol.pyx
 delete mode 100644 ribtol_hw.py

diff --git a/interface_multi.py b/interface_multi.py
deleted file mode 100644
index 83148e5..0000000
--- a/interface_multi.py
+++ /dev/null
@@ -1,2061 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-# from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-cdictpres = {'blue': (\
-                   (0.,    0.,  0.),
-                   (0.25,  0.25, 0.25),
-                   (0.5,  .70, 0.70),
-                   (0.75, 1.0, 1.0),
-                   (1,     1.,  1.),
-                   ),
-       'green': (\
-                   (0. ,   0., 0.0),
-                   (0.25,  0.50, 0.50),
-                   (0.5,  .70, 0.70),
-                   (0.75,  0.50, 0.50),
-                   (1  ,    0,  0.),
-                   ),
-       'red':  (\
-                  (0 ,  1.0, 1.0),
-                  (0.25 ,  1.0, 1.0),
-                   (0.5,  .70, 0.70),
-                  (0.75 , 0.25, 0.25),
-                  (1,    0., 0.),
-                  )}
-
-statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-os.system('module load Ruby')
-
-class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
-        """ creates an interactive interface for analysing class4gl experiments
-
-        INPUT:
-            path_exp : path of the experiment output
-            path_obs : path of the observations 
-            globaldata: global data that is being shown on the map
-            refetch_stations: do we need to build the list of the stations again?
-        OUTPUT:
-            the procedure returns an interface object with interactive plots
-
-        """
-        
-        # set the ground
-        self.globaldata = globaldata
-
- 
-        self.path_exp = path_exp
-        self.path_obs = path_obs
-        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
-
-        # # get the list of stations
-        # stationsfile = self.path_exp+'/stations_list.csv'
-        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
-        #     stations = pd.read_csv(stationsfile)
-        # else:
-        #     stations = get_stations(self.path_exp)
-        #     stations.to_csv(stationsfile)
-
-        # stations = stations.set_index('STNID')
-
-        self.frames = {}
-
-        self.frames['stats'] = {}
-        self.frames['worldmap'] = {}
-                
-        self.frames['profiles'] = {}
-        self.frames['profiles'] = {}
-        self.frames['profiles']['DT'] = None
-        self.frames['profiles']['STNID'] = None
-
-        #self.frames['worldmap']['stationsfile'] = stationsfile
-        self.frames['worldmap']['stations'] = stations(self.path_exp, \
-                                                       suffix='ini',\
-                                                       refetch_stations=refetch_stations)
-
-        # Initially, the stats frame inherets the values/iterators of
-        # worldmap
-        for key in self.frames['worldmap'].keys():
-            self.frames['stats'][key] = self.frames['worldmap'][key]
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_ini'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='ini',\
-                                           refetch_records=refetch_records
-                                           )
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_mod'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='mod',\
-                                           refetch_records=refetch_records
-                                           )
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_obs_afternoon'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_obs,\
-                                           subset='afternoon',\
-                                           refetch_records=refetch_records
-                                           )
-
-        self.frames['stats']['records_all_stations_mod'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['records_all_stations_ini']['dates'] = \
-            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
-
-
-        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
-
-        self.frames['stats']['records_all_stations_obs_afternoon'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['viewkeys'] = ['h','theta','q']
-        print('Calculating table statistics')
-        self.frames['stats']['records_all_stations_mod_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_mod'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-
-        self.frames['stats']['inputkeys'] = inputkeys
-        
-        # self.frames['stats']['inputkeys'] = \
-        #     [ key for key in \
-        #       self.globaldata.datasets.keys() \
-        #       if key in \
-        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
-
-
-        # get units from the class4gl units database
-        self.units = dict(units)
-        # for those that don't have a definition yet, we just ask a question
-        # mark
-        for var in self.frames['stats']['inputkeys']:
-            self.units[var] = '?'
-
-        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
-        self.frames['stats']['records_all_stations_ini_pct'] = \
-                  pct(self.frames['stats']['records_all_stations_ini'], \
-                      columns = self.frames['stats']['inputkeys'])
-
-        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
-        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-        #     mod['
-
-        # 
-        # 
-        # \
-        #        self.frames['stats']['records_all_stations_mod'], \
-
-
-
-        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
-        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
-        #               columns = [ 'd'+key+'dt' for key in \
-        #                           self.frames['stats']['viewkeys']], \
-        #              )
-
-        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
-        #               obs = self.frames['stats']['records_all_stations_ini'], \
-        #               columns = self.frames['stats']['viewkeys'], \
-        #              )
-        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
-        
-        print('filtering pathological data')
-        # some observational sounding still seem problematic, which needs to be
-        # investigated. In the meantime, we filter them
-        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
-
-        # we filter ALL data frames!!!
-        for key in self.frames['stats'].keys():
-            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
-               (self.frames['stats'][key].index.names == indextype):
-                self.frames['stats'][key] = self.frames['stats'][key][valid]
-        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
-
-        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
-
-
-        print("filtering stations from interface that have no records")
-        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
-            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                    == STNID).sum() == 0):
-                print("dropping", STNID)
-                self.frames['worldmap']['stations'].table = \
-                        self.frames['worldmap']['stations'].table.drop(STNID)
-                    
-        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-        
-        # TO TEST: should be removed, since it's is also done just below
-        self.frames['stats']['stations_iterator'] = \
-            self.frames['worldmap']['stations_iterator'] 
-
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
-        self.next_station()
-
-        # self.goto_datetime_worldmap(
-        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-        #     'after')
-    def sel_station(self,STNID=None,rownumber=None):
-
-        if (STNID is not None) and (rownumber is not None):
-            raise ValueError('Please provide either STNID or rownumber, not both.')
-
-        if (STNID is None) and (rownumber is None):
-            raise ValueError('Please provide either STNID or rownumber.')
-            
-        if STNID is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
-            print(
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-            )
-            self.update_station()
-        elif rownumber is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
-            self.update_station()
-
-
-
-    def next_station(self,event=None,jump=1):
-        with suppress(StopIteration):
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-                = self.frames['worldmap']['stations_iterator'].__next__(jump)
-            # self.frames['worldmap']['stations_iterator'].close()
-            # del(self.frames['worldmap']['stations_iterator'])
-            # self.frames['worldmap']['stations_iterator'] = \
-            #                 selfself.frames['worldmap']['stations'].iterrows()
-            # self.frames['worldmap']['STNID'],\
-            # self.frames['worldmap']['current_station'] \
-            #     = self.frames['worldmap']['stations_iterator'].__next__()
-
-        self.update_station()
-
-    def prev_station(self,event=None):
-        self.next_station(jump = -1,event=event)
-    def update_station(self):
-        for key in ['STNID','current_station','stations_iterator']: 
-            self.frames['stats'][key] = self.frames['worldmap'][key] 
-
-
-
-        # generate index of the current station
-        self.frames['stats']['records_current_station_index'] = \
-            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-             == \
-             self.frames['stats']['current_station'].name)
-
-        # create the value table of the records of the current station
-        tab_suffixes = \
-                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        for tab_suffix in tab_suffixes:
-            self.frames['stats']['records_current_station'+tab_suffix] = \
-                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-        # go to first record of current station
-        self.frames['stats']['records_iterator'] = \
-                        records_iterator(self.frames['stats']['records_current_station_mod'])
-        (self.frames['stats']['STNID'] , \
-        self.frames['stats']['current_record_chunk'] , \
-        self.frames['stats']['current_record_index']) , \
-        self.frames['stats']['current_record_mod'] = \
-                        self.frames['stats']['records_iterator'].__next__()
-
-        for key in self.frames['stats'].keys():
-            self.frames['profiles'][key] = self.frames['stats'][key]
-
-        STNID = self.frames['profiles']['STNID']
-        chunk = self.frames['profiles']['current_record_chunk']
-        if 'current_station_file_ini' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_ini'].close()
-        self.frames['profiles']['current_station_file_ini'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-        if 'current_station_file_mod' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_mod'].close()
-        self.frames['profiles']['current_station_file_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_afternoon'].close()
-        self.frames['profiles']['current_station_file_afternoon'] = \
-            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-        self.frames['profiles']['records_iterator'] = \
-                        records_iterator(self.frames['profiles']['records_current_station_mod'])
-        (self.frames['profiles']['STNID'] , \
-        self.frames['profiles']['current_record_chunk'] , \
-        self.frames['profiles']['current_record_index']) , \
-        self.frames['profiles']['current_record_mod'] = \
-                        self.frames['profiles']['records_iterator'].__next__()
-
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-
-        self.update_record()
-
-    def next_record(self,event=None,jump=1):
-        with suppress(StopIteration):
-            (self.frames['profiles']['STNID'] , \
-            self.frames['profiles']['current_record_chunk'] , \
-            self.frames['profiles']['current_record_index']) , \
-            self.frames['profiles']['current_record_mod'] = \
-                      self.frames['profiles']['records_iterator'].__next__(jump)
-        # except (StopIteration):
-        #     self.frames['profiles']['records_iterator'].close()
-        #     del( self.frames['profiles']['records_iterator'])
-        #     self.frames['profiles']['records_iterator'] = \
-        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
-        #     (self.frames['profiles']['STNID'] , \
-        #     self.frames['profiles']['current_record_index']) , \
-        #     self.frames['profiles']['current_record_mod'] = \
-        #                     self.frames['profiles']['records_iterator'].__next__()
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        self.update_record()
-
-    def prev_record(self,event=None):
-        self.next_record(jump=-1,event=event)
-
-    def update_record(self):
-        self.frames['profiles']['current_record_ini'] =  \
-            self.frames['profiles']['records_current_station_ini'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'],\
-                  self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon'] =  \
-            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'] , \
-                  self.frames['profiles']['current_record_index'])]
-
-        self.frames['profiles']['current_record_mod_stats'] = \
-                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
-                    self.frames['profiles']['STNID'], \
-                    self.frames['profiles']['current_record_chunk'], \
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_ini_pct'] = \
-                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        # frame
-        # note that the current station, record is the same as the stats frame for initialization
-
-        # select first 
-        #self.frames['profiles']['current_record_index'], \
-        #self.frames['profiles']['record_yaml_mod'] = \
-        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
-        #                   self.frames['stats']['current_record_index'])
-        self.frames['profiles']['record_yaml_mod'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_mod'], \
-               self.frames['profiles']['current_record_mod'].index_start,
-               self.frames['profiles']['current_record_mod'].index_end,
-               mode='mod')
-                                
-        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_ini'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_ini'], \
-               record_ini.index_start,
-               record_ini.index_end,
-                mode='ini')
-
-        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_obs_afternoon'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_afternoon'], \
-               record_afternoon.index_start,
-               record_afternoon.index_end,
-                mode='ini')
-
-
-        key = self.frames['worldmap']['inputkey']
-        # only redraw the map if the current world map has a time
-        # dimension
-        if 'time' in self.globaldata.datasets[key].page[key].dims:
-            self.goto_datetime_worldmap(
-                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                'after')
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap',
-                                                  'profiles'])
-        else:
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap_stations',
-                                                  'profiles'])
-
-    def abline(self,slope, intercept,axis):
-        """Plot a line from slope and intercept"""
-        #axis = plt.gca()
-        x_vals = np.array(axis.get_xlim())
-        y_vals = intercept + slope * x_vals
-        axis.plot(x_vals, y_vals, 'k--')
-
-    def plot(self):
-        import pylab as pl
-        from matplotlib.widgets import Button
-        import matplotlib.pyplot as plt
-        import matplotlib as mpl
-        '''
-        Definition of the axes for the sounding table stats
-        '''
-        
-        fig = pl.figure(figsize=(14,9))
-        axes = {} #axes
-        btns = {} #buttons
-
-        # frames, which sets attributes for a group of axes, buttens, 
-        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
-            label = 'stats_'+str(key)
-            axes[label] = fig.add_subplot(\
-                            len(self.frames['stats']['viewkeys']),\
-                            5,\
-                            5*ikey+1,label=label)
-            # Actually, the axes should be a part of the frame!
-            #self.frames['stats']['axes'] = axes[
-
-            # pointer to the axes' point data
-            axes[label].data = {}
-
-            # pointer to the axes' color fields
-            axes[label].fields = {}
-
-
-        fig.tight_layout()
-        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
-
-        label ='stats_colorbar'
-        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
-        axes[label].fields = {}
-
-        from matplotlib.colors import LinearSegmentedColormap
-        cdictpres = {'blue': (\
-                           (0.,    0.,  0.),
-                           (0.25,  0.25, 0.25),
-                           (0.5,  .70, 0.70),
-                           (0.75, 1.0, 1.0),
-                           (1,     1.,  1.),
-                           ),
-               'green': (\
-                           (0. ,   0., 0.0),
-                           (0.25,  0.50, 0.50),
-                           (0.5,  .70, 0.70),
-                           (0.75,  0.50, 0.50),
-                           (1  ,    0,  0.),
-                           ),
-               'red':  (\
-                          (0 ,  1.0, 1.0),
-                          (0.25 ,  1.0, 1.0),
-                           (0.5,  .70, 0.70),
-                          (0.75 , 0.25, 0.25),
-                          (1,    0., 0.),
-                          )}
-        
-        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-        label = 'times'
-               
-        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-
-
-        label = 'worldmap'
-               
-        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-        axes[label].lat = None
-        axes[label].lon = None
-
-        label = 'worldmap_colorbar'
-        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
-        axes[label].fields = {}
-
-        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
-        label = 'worldmap_stations'
-        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
-        axes[label].data = {}
-
-        fig.canvas.mpl_connect('pick_event', self.on_pick)
-        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
-
-
-        """ buttons definitions """
-        
-        label = 'bprev_dataset'
-        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous dataset')
-        btns[label].on_clicked(self.prev_dataset)
-
-        label = 'bnext_dataset'
-        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next dataset')
-        btns[label].on_clicked(self.next_dataset)
-
-        label = 'bprev_datetime'
-        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous datetime')
-        btns[label].on_clicked(self.prev_datetime)
-
-        label = 'bnext_datetime'
-        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next datetime')
-        btns[label].on_clicked(self.next_datetime)
-
-
-        label = 'bprev_station'
-        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous station')
-        btns[label].on_clicked(self.prev_station)
-
-        label = 'bnext_station'
-        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next station')
-        btns[label].on_clicked(self.next_station)
-
-        label = 'bprev_record'
-        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous record')
-        btns[label].on_clicked(self.prev_record)
-
-        label = 'bnext_record'
-        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next record')
-        btns[label].on_clicked(self.next_record)
-
-
-        # self.nstatsview = nstatsview
-        # self.statsviewcmap = statsviewcmap
-        self.fig = fig
-        self.axes = axes
-        self.btns = btns
-        self.tbox = {}
-        # self.hover_active = False
-
-        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
-        #                                transform=plt.gcf().transFigure)
-
-        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
-                                          transform=plt.gcf().transFigure)
-
-        label = 'air_ap:theta'
-        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
-
-        label = 'air_ap:q'
-        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
-
-        label = 'out:h'
-        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
-
-        label = 'out:theta'
-        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
-
-        label = 'out:q'
-        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
-
-        label = 'SEB'
-        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
-
-
-        self.hover_active = False
-        self.fig = fig
-        self.fig.show()
-        self.fig.canvas.draw()
-        self.refresh_plot_interface()
-
-
-    # def scan_stations(self):
-    #     blabla
-        
-
-
-    # def get_records(current_file):
-    #     records = pd.DataFrame()
-
-    #     # initial position
-    #     next_record_found = False
-    #     while(not next_record_found):
-    #         next_record_found = (current_file.readline() == '---\n')
-    #     next_tell = current_file.tell() 
-    #     end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #     while not end_of_file:
-    #         current_tell = next_tell
-    #         next_record_found = False
-    #         current_file.seek(current_tell)
-    #         while ( (not next_record_found) and (not end_of_file)):
-    #             current_line = current_file.readline()
-    #             next_record_found = (currentline == '---\n')
-    #             end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #         # we store the position of the next record
-    #         next_tell = current_file.tell() 
-    #         
-    #         # we get the current record. Unfortunately we need to reset the
-    #         # yaml record generator first.
-    #         current_yamlgen.close()
-    #         current_yamlgen = yaml.load_all(current_file)
-    #         current_file.seek(current_tell)
-    #         current_record_mod = current_yamlgen.__next__()
-    #     current_yamlgen.close()
-
-    #     return records
-
-       #      next_record_found = False
-       #      while(not record):
-       #          next_record_found = (self.current_file.readline() == '---\n')
-       #      self.current_tell0 = self.current_file.tell() 
-
-       #  
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell0 = self.current_file.tell() 
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell1 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell0)
-       #  self.r0 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell1)
-       #  next_record_found = False
-       #  while ( (not next_record_found) and (not end_of_file):
-       #      current_line = self.current_file.readline()
-       #      next_record_found = (currentline == '---\n')
-       #      end_of_file = (currentline == '') # an empty line means we are at the end
-
-       #  self.current_tell2 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell1)
-       #  self.r1 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell2)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell3 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell2)
-       #  self.r2 = self.current_yamlgen.__next__()
-
-       #  # go to position of next record in file
-       #  self.current_file.seek(self.current_tell3)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell4 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell3)
-       #  self.r3 = self.current_yamlgen.__next__()
- 
-       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
-
-    def goto_datetime_worldmap(self,DT,shift=None):
-        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
-            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
-            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
-            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
-                self.frames['worldmap']['iDT'] += 1
-            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
-                self.frames['worldmap']['iDT'] -= 1 
-            # for gleam, we take the values of the previous day
-            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
-                self.frames['worldmap']['iDT'] -= 2 
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-        #else:
-        #    self.frames['worldmap'].pop('DT')
-
-    def next_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def prev_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def next_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-    def prev_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-
-
-    def sel_dataset(self,inputkey):
-        self.frames['worldmap']['inputkey'] = inputkey
-        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
-        self.goto_datetime_worldmap(
-            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-            'after')# get nearest datetime of the current dataset to the profile
-        if "fig" in self.__dict__.keys():
-            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
-       
-    # def prev_station(self,event=None):
-    #     self.istation = (self.istation - 1) % self.stations.shape[0]
-    #     self.update_station()
-
-
-
-
-    #def update_datetime(self):
-    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
-    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
-    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
-    #        print(self.worldmapfocus['DT'])
-    #        self.refresh_plot_interface(only='worldmap')
-
-    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
-
-        #print('r1')
-        for argkey in args.keys():
-            self.__dict__[arg] = args[argkey]
-
-        axes = self.axes
-        tbox = self.tbox
-        frames = self.frames
-        fig = self.fig
- 
-        if (only is None) or ('worldmap' in only):
-            globaldata = self.globaldata
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
-            else:
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
-            keystotranspose = ['lat','lon']
-            for key in dict(datasetxr.dims).keys():
-                if key not in keystotranspose:
-                    keystotranspose.append(key)
-
-            datasetxr = datasetxr.transpose(*keystotranspose)
-            datasetxr = datasetxr.sortby('lat',ascending=False)
-
-            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
-            lonleft = lonleft - 360.
-            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
-            label = 'worldmap'
-            axes[label].clear()
-            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
-            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
-
-        if (only is None) or ('worldmap' in only):
-            #if 'axmap' not in self.__dict__ :
-            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
-            #else:
-
-            #stations = self.stations
-
-
-            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
-            #     resolution = 'l', 
-            # area_thresh = 0.1,
-            #     llcrnrlon=-180., llcrnrlat=-90.0,
-            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
-            # 
-            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
-            # self.gmap.drawcountries(color='white',linewidth=0.3)
-            # #self.gmap.fillcontinents(color = 'gray')
-            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
-            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
-            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
-            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # #self.ax5.shadedrelief()
-
-           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
-
-
-            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
-            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
-
-            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
-            if 'lev' in field.dims:
-                field = field.isel(lev=-1)
-
-            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
-            axes[label].axis('off')
-
-            from matplotlib import cm
-            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
-            
-            
-            title=frames['worldmap']['inputkey']
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
-            axes[label].set_title(title)
-
-            label ='worldmap_colorbar'
-            axes[label].clear()
-            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
-
-
-            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
-            # x,y = self.gmap(lons,lats)
-            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
-            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
-
-        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
-
-            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
-            store_xlim = {}
-            store_ylim = {}
-            for ikey, key in enumerate(statskeys_out):
-                if (only is not None) and ('stats_lightupdate' in only):
-                    store_xlim[key] = axes['stats_'+key].get_xlim()
-                    store_ylim[key] = axes['stats_'+key].get_ylim()
-                self.axes['stats_'+key].clear()    
-
-            label = 'times'
-            self.axes[label].clear()
-
-            key = 'dthetadt'
-            x = self.frames['stats']['records_all_stations_ini']['datetime']
-            #print(x)
-            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-            #print(y)
-            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            #print(z)
-
-            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
-            self.axes[label].data[label] = self.axes[label].scatter(x.values,
-                                                                    y.values,
-                                                                    c=z.values,
-                                                                    cmap=self.statsviewcmap,
-                                                                    s=2,
-                                                                    vmin=0.,
-                                                                    vmax=1.,
-                                                                    alpha=alpha_cloud_pixels)
-
-            
-            x = self.frames['stats']['records_current_station_ini']['datetime']
-            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-
-            x = self.frames['profiles']['records_current_station_ini']['datetime']
-            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
-            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
-
-            for ikey, key in enumerate(statskeys_out):
-
-                # show data of all stations
-                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_all_stations_mod_stats'][key]
-                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                qvalmax = x.quantile(0.999)
-                qvalmin = x.quantile(0.001)
-                print('applying extra filter over extreme values for plotting stats')
-                selx = (x >= qvalmin) & (x < qvalmax)
-                sely = (x >= qvalmin) & (x < qvalmax)
-                x = x[selx & sely]
-                y = y[selx & sely]
-                z = z[selx & sely]
-                self.axes['stats_'+key].data['stats_'+key] = \
-                       self.axes['stats_'+key].scatter(x,y, c=z,\
-                                cmap=self.statsviewcmap,\
-                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
-
-                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_current_station_mod_stats'][key]
-                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['profiles']['records_current_station_mod_stats'][key]
-                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
-
-                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
-                y = self.frames['stats']['current_record_mod_stats'][key]
-                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
-                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
-                    axes['stats_'+key].annotate(text, \
-                                               xy=(x,y),\
-                                               xytext=(0.05,0.05),\
-                                               textcoords='axes fraction',\
-                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
-                                               color='white',\
-                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
-                # self.axes['stats_'+key].data[key+'_current_record'] = \
-                #        self.axes['stats_'+key].scatter(x,y, c=z,\
-                #                 cmap=self.statsviewcmap,\
-                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
-
-                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
-                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
-                # # highlight data for curent station
-                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
-
-                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
-
-                if ikey == len(statskeys_out)-1:
-                    self.axes['stats_'+key].set_xlabel('external')
-                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
-                axes['stats_'+key].set_ylabel('model')
-
-
-                if (only is not None) and ('stats_lightupdate' in only):
-                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
-                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
-                else:
-                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
-                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
-                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
-                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
-                self.abline(1,0,axis=self.axes['stats_'+key])
-
-        if (only is None) or ('stats_colorbar' in only):
-            label ='stats_colorbar'
-            axes[label].clear()
-            import matplotlib as mpl
-            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
-            self.axes[label].fields[label] = \
-             mpl.colorbar.ColorbarBase(self.axes[label],\
-                        orientation='horizontal',\
-                        label="percentile of "+self.frames['worldmap']['inputkey'],
-                        alpha=1.,
-                                cmap=self.statsviewcmap,\
-                                       norm=norm
-                         )
-
-        #print('r1')
-        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
-            #print('r2')
-            label = 'worldmap_stations'
-            axes[label].clear()
-            
-            stations = self.frames['worldmap']['stations'].table
-            globaldata = self.globaldata
-            
-            key = label
-
-            #print('r3')
-            if (stations is not None):
-                xlist = []
-                ylist = []
-                #print('r4')
-                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
-            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    xlist.append(x)
-                    ylist.append(y)
-                #picker is needed to make it clickable (pick_event)
-                axes[label].data[label] = axes[label].scatter(xlist,ylist,
-                                                              c='r', s=15,
-                                                              picker = 15,
-                                                              label=key,
-                                                              edgecolor='k',
-                                                              linewidth=0.8)
-
-            # cb.set_label('Wilting point [kg kg-3]')
-                #print('r5')
-
-                
-            #     xseries = []
-            #     yseries = []
-            #     for iSTN,STN in stations.iterrows():
-            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
-            #         xseries.append(x)                    
-            #         yseries.append(y)
-            #         
-            #         
-            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
-                    
-                if ('current_station' in frames['worldmap']):
-                    #print('r5')
-                    STN = frames['stats']['current_station']
-                    STNID = frames['stats']['STNID']
-                    #print('r5')
-
-                    x,y = len(axes['worldmap'].lon)* \
-                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
-                          len(axes['worldmap'].lat)* \
-                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    #print('r6')
-                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
-                    #                          == \
-                    #                          self.frames['worldmap']['STNID'])\
-                    #                         & \
-                    #                         (self.seltablestats['DT'] \
-                    #                          == self.axes['statsview0].focus['DT']) \
-                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
-                    #print('r7')
-                    text = 'STNID: '+ format(STNID,'10.0f') + \
-                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
-                            ', LON: '+format(STN['longitude'],'3.3f')+ \
-                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
-
-                            #+', VAL: '+format(VAL,'.3e')
-
-                    axes[label].scatter(x,y, c='r', s=30,\
-                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
-                    #print('r8')
-            
-                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
-                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
-                    #colorstation = max((min((1.,colorstation)),0.))
-                    colorstation =0.2
-                    from matplotlib import cm
-                    axes[label].annotate(text,
-                                         xy=(x,y),
-                                         xytext=(0.05,0.05),
-                                         textcoords='axes fraction', 
-                                         bbox=dict(boxstyle="round",
-                                         fc = cm.viridis(colorstation)),
-                                         arrowprops=dict(arrowstyle="->",
-                                                         linewidth=1.1),
-                                         color='white' if colorstation < 0.5 else 'black')
-                    #print('r9')
-
-                    # #pos = sc.get_offsets()[ind["ind"][0]]
-                    # 
-                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
-                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
-                    # axes[label].data[label+'statannotate'].set_text(text)
-                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
-                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
-            #print('r9')
-            axes[label].axis('off')
-            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
-            axes[label].set_ylim((len(axes['worldmap'].lat),0))
-            #print('r10')
-
-        if (only is None) or ('profiles' in only): 
-            #print('r11')
-
-            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
-            # # self.update_station(goto_first_sounding=False)
-            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
-            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
-            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
-            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
-
-            label = 'air_ap:theta'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-                # +\
-                # ' -> '+ \
-                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-            
-            
-            
-            
-            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-            #print('r12')
-
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
-            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
-            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
-            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            #print('r13')
-            # 
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r14')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-
-            #print('r15')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-                          
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r16')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r17')
-            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
-            print(hmax)
-            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
-            if valid_mod:
-
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="mod "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                                 +'LT')
-
-            #print('r18')
-            axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('theta [K]')
-
-            label = 'air_ap:q'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-
-            #print('r19')
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            if valid_mod:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            else:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            # 
-            #print('r20')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r21')
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            #print('r23')
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r24')
-            if valid_mod:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="fit ")#+\
-                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
-                             #+'LT')
-            #print('r25')
-            #axes[label].legend()
-
-            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            #axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('q [kg/kg]')
-
-            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-
-            # #pl.subplots_adjust(right=0.6)
-
-            # label = 'q_pro'
-            # axes[label].clear()
-
-            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
-            # 
-            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
-            # 
-            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
-
-            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
-            # #pl.subplots_adjust(right=0.6)
-            # axes[label].set_xlabel('specific humidity [kg/kg]')
- 
-
-            #print('r26')
-            time = self.frames['profiles']['record_yaml_mod'].out.time
-            for ilabel,label in enumerate(['h','theta','q']):
-                axes["out:"+label].clear()
-                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
-                axes["out:"+label].set_ylabel(label)
-                if ilabel == 2:
-                    axes["out:"+label].set_xlabel('local sun time [h]')
-                
-            #print('r27')
-            label = 'SEB'
-            axes[label].clear()
-            
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
-            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
-            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
-            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
-                
-            #print('r28')
-            
-            axes[label].legend()
-            
-            #         for ax in self.fig_timeseries_axes:
-#             ax.clear()
-#         
-#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
-#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
-#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
-#         #print(self.morning_sounding.c4gl.out.Swin)
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
-#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
-#         self.fig_timeseries_axes[3].legend()
-#         self.fig.canvas.draw()
-            
-
-
-
-
-
-
-        #self.ready()
-        #print('r29')
-        fig.canvas.draw()
-        #fig.show()
-
-        self.axes = axes
-        self.tbox = tbox
-        self.fig = fig
-
-    def on_pick(self,event):
-        #print("HELLO")
-        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
-        #self.axes['theta_pro'].clear()
-        #self.axes['q_pro'].clear()
-        
-
-        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
-        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
-        keys_to_axes = {}
-        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
-
-        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
-        keys_to_axes['worldmap'] = 'worldmap'
-        
-        axes = self.axes
-        #nstatsview = self.nstatsview
-        #statsviewcmap = self.statsviewcmap
-        stations = self.frames['worldmap']['stations'].table
-
-
-        #print("p1")
-        current = event
-        artist = event.artist
-        
-        selkey = artist.get_label()
-        
-        #print(keys_to_axes)
-        
-        label = keys_to_axes[selkey]
-        #print("HELLO",selkey,label)
-
-        # # Get to know in which axes we are
-        # label = None
-        # for axeskey in axes.keys():
-        #     if event.inaxes == axes[axeskey]:
-        #         label = axeskey
-        #         
-
-        # cont, pos = None, None
-        
-        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
-        ind = event.ind
-        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
-        d = axes[label].collections[0]
-        #d.set_offset_position('data')
-        xy = d.get_offsets()
-        x, y =  xy[:,0],xy[:,1]
-        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
-
-        #print("p2")
-        if len(ind) > 0:
-            #print("p3")
-            pos = x[ind[0]], y[ind[0]]
-
-            #if label[:-1] == 'statsview':
-            #    #seltablestatsstdrel = self.seltablestatsstdrel
-            #    #seltablestatspct = self.seltablestatspct
-
-            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-            #    
-            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
-            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-            #    
-            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
-            #el
-            if (label == 'worldmap') or (label == 'worldmap_stations'):
-                self.hover_active = False
-                if (self.frames['worldmap']['STNID'] !=
-                    self.frames['profiles']['STNID']):
-                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
-                # so we just need to perform update_station
-                    self.update_station()
-            elif (label[:5] == 'stats'):
-
-                self.hover_active = False
-                if (self.frames['stats']['STNID'] !=
-                self.frames['profiles']['STNID']) or \
-                   (self.frames['stats']['current_record_chunk'] != 
-                    self.frames['profiles']['current_record_chunk']) or \
-                   (self.frames['stats']['current_record_index'] != 
-                    self.frames['profiles']['current_record_index']):
-
-
-
-                    for key in ['STNID','current_station','stations_iterator']: 
-                        self.frames['worldmap'][key] = self.frames['stats'][key] 
-
-                    for key in self.frames['stats'].keys():
-                        self.frames['profiles'][key] = self.frames['stats'][key]
-
-                    STNID = self.frames['profiles']['STNID']
-                    chunk = self.frames['profiles']['current_record_chunk']
-                    if 'current_station_file_ini' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_ini'].close()
-                    self.frames['profiles']['current_station_file_ini'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-                    if 'current_station_file_mod' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_mod'].close()
-                    self.frames['profiles']['current_station_file_mod'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_afternoon'].close()
-                    self.frames['profiles']['current_station_file_afternoon'] = \
-                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-                    # go to hovered record of current station
-                    self.frames['profiles']['records_iterator'] = \
-                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # ... and go to the record of the profile window (last one that
-                    # was picked by the user)
-                    found = False
-                    EOF = False
-                    while (not found) and (not EOF):
-                        try:
-                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
-                            #print("hello*")
-                            #print(self.frames['profiles']['current_record_index'])
-                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
-                               (index == self.frames['profiles']['current_record_index']) and \
-                               (STNID == self.frames['profiles']['STNID']):
-                                #print('found!')
-                                found = True
-                        except StopIteration:
-                            EOF = True
-                    if found:
-                        self.frames['stats']['current_record_mod'] = record
-                        self.frames['stats']['current_record_chunk'] = chunk
-                        self.frames['stats']['current_record_index'] = index
-                    # # for the profiles we make a distinct record iterator, so that the
-                    # # stats iterator can move independently
-                    # self.frames['profiles']['records_iterator'] = \
-                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # (self.frames['profiles']['STNID'] , \
-                    # self.frames['profiles']['current_record_index']) , \
-                    # self.frames['profiles']['current_record_mod'] = \
-                    #                 self.frames['profiles']['records_iterator'].__next__()
-
-
-                    # for the profiles we make a distinct record iterator, so that the
-                    # stats iterator can move independently
-
-                    self.update_record()
-
-
-
-    def on_plot_hover(self,event):
-        axes = self.axes
-        #print('h1')
-
-        # Get to know in which axes we are
-        label = None
-        for axeskey in axes.keys():
-            if event.inaxes == axes[axeskey]:
-                label = axeskey
-                
-        #print('h2')
-
-        cont, pos = None, None
-        #print (label)
-        
-        if label is not None:
-            if  ('data' in axes[label].__dict__.keys()) and \
-                (label in axes[label].data.keys()) and \
-                (axes[label].data[label] is not None):
-                
-                #print('h3')
-                cont, ind =  axes[label].data[label].contains(event)
-                selkey = axes[label].data[label].get_label()
-                if len(ind["ind"]) > 0:
-                    #print('h4')
-                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
-                    #print('pos',pos,selkey)
-
-
-                    #if label[:-1] == 'statsview':
-                    #    seltablestatsstdrel = self.seltablestatsstdrel
-                    #    seltablestatspct = self.seltablestatspct
-
-                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
-                    #    self.hover_active = True
-                    #    
-                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
-                    #    
-                    #el
-                    #print(label[:5])
-                    if (label[:5] == 'stats') or (label == 'times'):
-                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
-                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
-                        
-
-                        if label[:5] == 'stats':
-                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                            (self.frames['stats']['STNID'] ,
-                             self.frames['stats']['current_record_chunk'], 
-                             self.frames['stats']['current_record_index']) = \
-                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-                        # elif label[:5] == 'stats':
-                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
-                        #     (self.frames['stats']['STNID'] ,
-                        #      self.frames['stats']['current_record_chunk'], 
-                        #      self.frames['stats']['current_record_index']) = \
-                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-
-
-                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-                        
-                        # # TO TEST: should be removed, since it's is also done just below
-                        # self.frames['stats']['stations_iterator'] = \
-                        #     self.frames['worldmap']['stations_iterator'] 
-                
-                
-                        # self.goto_datetime_worldmap(
-                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-                        #     'after')
-
-
-                        # scrolling to the right station
-                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                        EOF = False
-                        found = False
-                        while (not found and not EOF):
-                            if (STNID == self.frames['stats']['STNID']):
-                                   found = True 
-                            if not found:
-                                try:
-                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                                except (StopIteration):
-                                    EOF = True
-                        if found:
-                        #    self.frames['stats']['STNID'] = STNID
-                            self.frames['stats']['current_station'] =  station
-
-                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
-                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
-
-
-                        # generate index of the current station
-                        self.frames['stats']['records_current_station_index'] = \
-                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                             == self.frames['stats']['STNID'])
-
-
-                        tab_suffixes = \
-                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            self.frames['stats']['records_current_station'+tab_suffix] = \
-                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-
-                        # go to hovered record of current station
-                        self.frames['stats']['records_iterator'] = \
-                                        records_iterator(self.frames['stats']['records_current_station_mod'])
-
-
-                        # ... and go to the record of the profile window (last one that
-                        # was picked by the user)
-                        found = False
-                        EOF = False
-                        while (not found) and (not EOF):
-                            try:
-                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                                #print("hello*")
-                                #print(self.frames['profiles']['current_record_index'])
-                                if (index == self.frames['stats']['current_record_index']) and \
-                                   (chunk == self.frames['stats']['current_record_chunk']) and \
-                                   (STNID == self.frames['stats']['STNID']):
-                                    #print('found!')
-                                    found = True
-                            except StopIteration:
-                                EOF = True
-                        if found:
-                            #print('h5')
-                            self.frames['stats']['current_record_mod'] = record
-                            self.frames['stats']['current_record_chunk'] = chunk
-                            self.frames['stats']['current_record_index'] = index
-
-                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
-                        tab_suffixes = \
-                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            #print(tab_suffix)
-                            #print(self.frames['stats']['records_current_station'+tab_suffix])
-                            self.frames['stats']['current_record'+tab_suffix] =  \
-                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                      (self.frames['stats']['STNID'] , \
-                                       self.frames['stats']['current_record_chunk'] , \
-                                       self.frames['stats']['current_record_index'])]
-
-
-                        self.hover_active = True
-                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                        # print('h13')
-                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
-                        #     self.goto_datetime_worldmap(
-                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                        #         'after')
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap',
-                        #                                           'profiles'])
-                        # else:
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap_stations',
-                        #                                           'profiles'])
-
-
-
-                    elif label in ['worldmap_stations','worldmap']:
-                        #print('h5')
-
-                        if (self.axes['worldmap'].lat is not None) and \
-                           (self.axes['worldmap'].lon is not None):
-
-
-                            #self.loading()
-                            self.fig.canvas.draw()
-                            self.fig.show()
-
-
-                            # get position of 
-                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
-                                                                 self.axes['worldmap'].lat[0]) + \
-                                           self.axes['worldmap'].lat[0],4)
-                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
-                                                                 self.axes['worldmap'].lon[0]) + \
-                                           self.axes['worldmap'].lon[0],4)
-                        
-                            stations = self.frames['worldmap']['stations'].table
-                            #print('h7')
-                        
-                            #reset stations iterator:
-                            # if 'stations_iterator' in self.frames['worldmap'].keys():
-                            #     self.frames['worldmap']['stations_iterator'].close()
-                            #     del(self.frames['worldmap']['stations_iterator'])
-                            # if 'stations_iterator' in self.frames['stats'].keys():
-                            #     self.frames['stats']['stations_iterator'].close()
-                            #     del(self.frames['stats']['stations_iterator'])
-                            self.frames['worldmap']['stations_iterator'] =\
-                               stations_iterator(self.frames['worldmap']['stations'])
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                            EOF = False
-                            found = False
-                            while (not found and not EOF):
-                                #print('h8',station.latitude,latmap)
-                                #print('h8',station.longitude,lonmap)
-                                if (round(station.latitude,3) == round(latmap,3)) and \
-                                    (round(station.longitude,3) == round(lonmap,3)):
-                                       found = True 
-                                if not found:
-                                    try:
-                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                                    except (StopIteration):
-                                        EOF = True
-                            if found:
-                                self.frames['worldmap']['STNID'] = STNID
-                                self.frames['worldmap']['current_station'] = \
-                                        station
-                        
-                            self.frames['stats']['stations_iterator'] = \
-                                self.frames['worldmap']['stations_iterator'] 
-                            #print('h8')
-                            # inherit station position for the stats frame...
-                            for key in self.frames['worldmap'].keys():
-                                self.frames['stats'][key] = self.frames['worldmap'][key]
-                                
-                            ## fetch records of current station...
-                            #self.frames['stats']['records_current_station_mod'] =\
-                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                            # ... and their indices
-                            self.frames['stats']['records_current_station_index'] = \
-                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                                     == \
-                                     self.frames['stats']['current_station'].name)
-
-
-                            tab_suffixes = \
-                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['records_current_station'+tab_suffix] = \
-                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-                            # ... create a record iterator ...
-                            #self.frames['stats']['records_iterator'].close()
-                            del(self.frames['stats']['records_iterator'])
-                            self.frames['stats']['records_iterator'] = \
-                                self.frames['stats']['records_current_station_mod'].iterrows()
-
-
-
-                        
-                            #print('h9')
-                            # ... and go to to the first record of the current station
-                            (self.frames['stats']['STNID'] , \
-                             self.frames['stats']['current_record_chunk'] , \
-                             self.frames['stats']['current_record_index']) , \
-                            self.frames['stats']['current_record_mod'] = \
-                                self.frames['stats']['records_iterator'].__next__()
-                        
-
-
-
-                            #print('h10')
-                            # cash the current record
-                            tab_suffixes = \
-                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['current_record'+tab_suffix] =  \
-                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                          (self.frames['stats']['STNID'] , \
-                                           self.frames['stats']['current_record_chunk'] , \
-                                           self.frames['stats']['current_record_index'])]
-
-                            #print('h11')
-                            
-                            self.hover_active = True
-                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                            #print('h13')
-
-                        
-
-            #if (stations is not None):
-            #    for iSTN,STN in stations.iterrows():
-            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
-            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
-
-        # self.fig.show()
- 
-        # we are hovering on nothing, so we are going back to the position of
-        # the profile sounding
-        if pos is None:
-            if self.hover_active == True:
-                #print('h1*')
-                
-                #self.loading()
-                # to do: reset stations iterators
-
-                # get station and record index from the current profile
-                for key in ['STNID', 'current_station']:
-                    self.frames['stats'][key] = self.frames['profiles'][key]
-
-                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
-                self.frames['stats']['current_station'] = \
-                        self.frames['profiles']['current_station']
-                #print('h3a*')
-                self.frames['stats']['records_current_station_mod'] = \
-                        self.frames['profiles']['records_current_station_mod']
-                #print('h3b*')
-
-                # the next lines recreate the records iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-
-                # reset stations iterator...
-                #self.frames['stats']['records_iterator'].close()
-                del(self.frames['stats']['records_iterator'])
-                self.frames['stats']['records_iterator'] = \
-                    self.frames['stats']['records_current_station_mod'].iterrows()
-                #print('h4*')
-
-                # ... and go to the record of the profile window (last one that
-                # was picked by the user)
-                found = False
-                EOF = False
-                while (not found) and (not EOF):
-                    try:
-                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                        #print("hello*")
-                        #print(self.frames['profiles']['current_record_index'])
-                        #print(self.frames['profiles']['STNID'])
-                        #print(STNID,index)
-                        if (index == self.frames['profiles']['current_record_index']) and \
-                            (chunk == self.frames['profiles']['current_record_chunk']) and \
-                            (STNID == self.frames['profiles']['STNID']):
-                            #print('found!')
-                            found = True
-                    except StopIteration:
-                        EOF = True
-                if found:
-                    #print('h5*')
-                    self.frames['stats']['current_record_mod'] = record
-                    self.frames['stats']['current_record_chunk'] = chunk
-                    self.frames['stats']['current_record_index'] = index
-
-                #print('h6*')
-
-
-
-                # # fetch records of current station...
-                # self.frames['stats']['records_current_station_mod'] =\
-                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                # ... and their indices
-                self.frames['stats']['records_current_station_index'] = \
-                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                         == \
-                         self.frames['stats']['current_station'].name)
-
-
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['records_current_station'+tab_suffix] = \
-                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-                
-
-                # cash the records of the current stations
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['current_record'+tab_suffix] =  \
-                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                              (self.frames['stats']['STNID'] , \
-                               self.frames['stats']['current_record_chunk'] , \
-                               self.frames['stats']['current_record_index'])]
-
-
-                # the next lines recreate the stations iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-                #print('h7*')
-
-                # reset the stations iterators
-                for framekey in ['stats','worldmap']:
-                    ##print(framekey)
-                    if 'stations_iterator' in self.frames[framekey]:
-                        #self.frames[framekey]['stations_iterator'].close()
-                        del(self.frames[framekey]['stations_iterator'])
-
-                self.frames['worldmap']['current_station'] = \
-                        self.frames['profiles']['current_station']
-
-                #recreate the stations iterator for the worldmap...
-                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-
-                # ... and go the position of the profile
-                #print('h8*')
-                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                EOF = False
-                found = False
-                while (not found and not EOF):
-                    if STNID == self.frames['profiles']['STNID'] :
-                        found = True 
-                    if not found:
-                        try:
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                        except (StopIteration):
-                            EOF = True
-                if found:
-                    self.frames['worldmap']['current_station'] = station
-                    self.frames['worldmap']['STNID'] = STNID
-                #print('h9*')
-                self.frames['stats']['stations_iterator'] = \
-                    self.frames['worldmap']['stations_iterator'] 
-
-                # the stats window now inherits the current station from the
-                # worldmap
-                for key in ['STNID','current_station','stations_iterator']: 
-                    self.frames['stats'][key] = self.frames['worldmap'][key] 
-                #print('h10*')
-
-                # # we now only need inherit station position and go to first record
-                # for key in self.frames['worldmap'].keys():
-                #     self.frames['stats'][key] = self.frames['worldmap'][key]
-
-                # self.frames['stats']['records_current_station'] =\
-                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
-
-                # #print(self.frames['stats']['records_current_station'])
-                # self.frames['stats']['records_iterator'] = \
-                #                 self.frames['stats']['records_current_station'].iterrows()
-                # (self.frames['stats']['STNID'] , \
-                # self.frames['stats']['current_record_index']) , \
-                # self.frames['stats']['current_record_mod'] = \
-                #                 self.frames['stats']['records_iterator'].__next__()
-                
-
-
-
-
-
-
-                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
-                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
-                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
-                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-                self.hover_active = False
-                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
-    # def loading(self):
-    #     self.tbox['loading'].set_text('Loading...')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-    #     sleep(0.1)
-    # def ready(self):
-    #     self.tbox['loading'].set_text('Ready')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-
-
-
diff --git a/ribtol.cpp b/ribtol.cpp
deleted file mode 100644
index 148b0d3..0000000
--- a/ribtol.cpp
+++ /dev/null
@@ -1,81 +0,0 @@
-// fast conversion of bulk Richardson number to Obukhov length
-
-#include 
-#include 
-#include 
-using namespace std;
-
-inline double psim(double zeta)
-{
-  double psim;
-  double x;
-  if(zeta <= 0.)
-  {
-    //x     = (1. - 16. * zeta) ** (0.25)
-    //psim  = 3.14159265 / 2. - 2. * arctan(x) + log( (1.+x) ** 2. * (1. + x ** 2.) / 8.)
-    x    = pow(1. + pow(3.6 * abs(zeta),2./3.), -0.5);
-    psim = 3. * log( (1. + 1. / x) / 2.);
-  }
-  else
-  {
-    psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35;
-  }
-  return psim;
-}
-    
-inline double psih(double zeta)
-{
-  double psih;
-  double x;
-  if(zeta <= 0.)
-  {
-    // x     = (1. - 16. * zeta) ** (0.25)
-    // psih  = 2. * log( (1. + x ** 2.) / 2. )
-    x     = pow(1. + pow(7.9 * abs(zeta), (2./3.)), -0.5);
-    psih  = 3. * log( (1. + 1. / x) / 2.);
-  }
-  else
-  {
-    psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - pow(1. + (2./3.) * zeta, 1.5) - (10./3.) / 0.35 + 1.;
-  }
-  return psih;
-}
-
-
-double ribtol(double Rib, double zsl, double z0m, double z0h)
-{
-  double L, L0;
-  double Lstart, Lend;
-  double fx, fxdif;
-
-  if(Rib > 0.)
-  {
-    L    = 1.;
-    L0   = 2.;
-  }
-  else
-  {
-    L  = -1.;
-    L0 = -2.;
-  }
-    
-  while (abs(L - L0) > 0.001)
-  {
-    L0      = L;
-    fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / pow(log(zsl / z0m) - psim(zsl / L) + psim(z0m / L), 2.);
-    Lstart  = L - 0.001 * L;
-    Lend    = L + 0.001 * L;
-    fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / pow(log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart), 2.)) - (-zsl / Lend * (log(zsl / z0h) - psih(zsl / Lend) + psih(z0h / Lend)) / pow(log(zsl / z0m) - psim(zsl / Lend) + psim(z0m / Lend), 2.)) ) / (Lstart - Lend);
-    L       = L - fx / fxdif;
-  }
-  
-  return L;
-
-}
-
-BOOST_PYTHON_MODULE(ribtol)
-{
-    using namespace boost::python;
-    def("ribtol", ribtol);
-}
-
diff --git a/ribtol.pyx b/ribtol.pyx
deleted file mode 100644
index e11a147..0000000
--- a/ribtol.pyx
+++ /dev/null
@@ -1,48 +0,0 @@
-#cython: boundscheck=False
-#cython: wraparound=False
-
-from libc.math cimport atan, log, exp, fabs
-
-cdef double psim(double zeta):
-    cdef double x, psim
-
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * atan(x) + log((1. + x)**2. * (1. + x**2.) / 8.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-      
-cdef double psih(double zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * log( (1. + x*x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
-
-def ribtol(double Rib, double zsl, double z0m, double z0h): 
-    cdef double L, L0, fx, Lstart, Lend, fxdif
-
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    
-    while (fabs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-
-        if(fabs(L) > 1e15):
-            break
-
-    return L
diff --git a/ribtol_hw.py b/ribtol_hw.py
deleted file mode 100644
index 1946cc8..0000000
--- a/ribtol_hw.py
+++ /dev/null
@@ -1,165 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jan 12 10:46:20 2018
-
-@author: vsc42247
-"""
-
-
-
-# purpose of calc_cm_ch: calculate momentum and thermal turbulent diffusion coefficients of the surface layer with a non-iterative procedure (Wouters et al., 2012)
-
-# input:
-
-# zrib = bulk Richardson number = (g/T)* DT * z/(ua^2)
-#   with:
-#     g = 9.81 m/s2 the gravitational acceleration
-#     z = height (in meters) of the surface layer under consideration 
-#     T = (reference) temperature (in Kelvin) at height z 
-#     DT = (T - T_s) = temperature (in Kelvin) gradient between the surface and height z 
-#     u_a^2 = u^2 +  v^2 is the squared horizontal absolute wind speed 
-# zzz0m = ratio z/z0 between the height z and the momentum roughness length z0m
-# zkbm = ln(z0m/z0h), with z0m, z0h the momentum and thermal roughness length, respectively.
-
-# output: diffusion coefficients (CM and CH) which cna be used to determine surface-layer turbulent transport
-# u'w' = - CM ua^2.
-# w'T' = - CH ua DT 
-
-
-# Reference:
-# Wouters, H., De Ridder, K., and Lipzig, N. P. M.: Comprehensive
-# Parametrization of Surface-Layer Transfer Coefficients for Use
-# in Atmospheric Numerical Models, Bound.-Lay. Meteorol., 145,
-# 539–550, doi:10.1007/s10546-012-9744-3, 2012.
-
-import numpy as np
-
-def calc_cm_ch (zeta,zzz0m,zkbm):
-    krm = 0.4
-
-    #ZETA = zeta_hs2(zrib,zzz0m,zkbm)
-    FUNM,FUNH = funcsche(ZETA,zzz0m,zkbm)
-    CM = krm**2.0/FUNM/FUNM
-    CH = krm**2.0/FUNM/FUNH
-
-    # FUNMn,FUNHn = funcsche(0.,zzz0m,zkbm)
-    # CMn = krm**2.0/FUNMn/FUNMn
-    # CHn = krm**2.0/FUNMn/FUNHn
-
-    # print ZETA,FUNM,FUNH
-    # print 'CMCMN',CM/CMn
-    # print 'CHCHN',CH/CHn
-
-    return CM,CH
-
-
-def zeta_hs2(RiB,zzz0m,kBmin1):
-    #print(RiB,zzz0m,kBmin1)
-    mum=2.59
-    muh=0.95
-    nu=0.5
-    lam=1.5
-
-    betah = 5.0
-
-    zzz0h = zzz0m*np.exp(kBmin1)
-    zzzs = zzz0m*0.06 # to be changed!! r. 101 nog bekijken!!
-
-    L0M = np.log(zzz0m)
-    L0H = np.log(zzz0h)
-    facM = np.log(1.+lam/mum/zzzs)*np.exp(-mum*zzzs)/lam
-    facH = np.log(1.+lam/muh/zzzs)*np.exp(-muh*zzzs)/lam
-    L0Ms = L0M + facM 
-    L0Hs = L0H + facH
-
-    if RiB < 0.:
-        p = np.log(1.-RiB)
-        Q = -0.486 +0.219*p - 0.0331*p**2-4.93*np.exp(-L0H) - 3.65/L0H +\
-            0.38*p/L0H+ 14.8/L0H/L0H-0.946*p/L0H/L0H-10.0/L0H**3+ \
-            0.392*L0M/L0H-0.084*p*L0M/L0H+0.368*L0M/L0H/L0H
-        # print 'p: ',p
-        # print 'Q: ',Q
-        zeta = (1. + p*Q)* L0Ms**2/L0Hs * RiB
-    else:
-        betam = 4.76+7.03/zzz0m +0.24*zzz0m/zzz0h # to be changed
-        # betam = 5.0 + 1.59*10.**(-5.)*(np.exp(13.0-L0M)-1.0) \
-        #         +0.24*(np.exp(-kBmin1)-1.0) # to be changed!!
-        # print('betam',betam)
-        lL0M = np.log(L0M)
-        S0Ms = 1.-1./zzz0m + (1.+nu/mum/zzzs)*facM
-        S0Hs = 1.-1./zzz0h + (1.+nu/muh/zzzs)*facH
-        zetat = -0.316-0.515*np.exp(-L0H) + 25.8 *np.exp(-2.*L0H) + 4.36/L0H \
-                -6.39/L0H/L0H+0.834*lL0M - 0.0267*lL0M**2
-        # print('zetat',zetat)
-        RiBt = zetat *(L0Hs+ S0Hs*betah*zetat)/(L0Ms+S0Ms*betam*zetat)**2 
-        # print('RiBt',RiBt)
-
-        if (RiB > RiBt):
-            D = (L0Ms+S0Ms*betam*zetat)**3/\
-                (L0Ms*L0Hs+zetat*(2.*S0Hs * betah * L0Ms - S0Ms*betam*L0Hs))
-            zeta = zetat + D*(RiB-RiBt)
-        else:
-            r = RiB - S0Hs*betah/(S0Ms*betam)**2
-            B = S0Ms*betam*L0Hs- 2.*S0Hs*betah*L0Ms
-            C = 4.*(S0Ms*betam)**2 * L0Ms *(S0Hs*betah*L0Ms-S0Ms*betam*L0Hs)
-            zeta = - L0Ms / S0Ms/betam - B*C/(4.*(S0Ms*betam)**3 *(B**2+abs(C*r)))
-            if r != 0:
-                zeta = zeta + (B-np.sqrt(B**2+C*r) + B*C*r/(2.*(B**2+abs(C*r))))/(2.*(S0Ms*betam)**3*r)
-    # print('zeta',zeta)
-    return zeta
-
-def funcsche(zeta,zzz0,kBmin1):
-
-
-    mum=2.5
-    muh=0.9
-    nu=0.5
-    lam=1.5
-    
-    p2=3.141592/2.
-    
-    lnzzz0=np.log(zzz0)
-    zzzs=zzz0*0.06
-    zetamcorr=(1.+nu/(mum*zzzs))*zeta
-    zetam0=zeta/zzz0
-    zetahcorr=(1.+nu/(muh*zzzs))*zeta
-    zetah0=zeta/(zzz0*np.exp(kBmin1))
-    
-    if (zeta <= 0.):
-    
-        gamma=15.2
-        alfam=0.25
-        xx=(1.-gamma*zeta)**alfam
-        psim=2.*np.log((1.+xx)/2.)+np.log((1.+xx**2.)/2.)-2.*np.arctan(xx)+p2
-        xx0=(1.-gamma*zetam0)**alfam
-        psim0=2.*np.log((1.+xx0)/2.)+np.log((1.+xx0**2.)/2.)-2.*np.arctan(xx0)+p2
-        phimcorr=(1.-gamma*zetamcorr)**(-alfam)
-        
-        alfah=0.5
-        yy=(1.-gamma*zeta)**alfah
-        psih=2.*np.log((1.+yy)/2.)
-        yy0=(1.-gamma*zetah0)**alfah
-        psih0=2.*np.log((1.+yy0)/2.)
-        phihcorr=(1.-gamma*zetahcorr)**(-alfah)
-    else: 
-    
-        aa=6.1
-        bb=2.5
-        psim=-aa*np.log(zeta+(1.+zeta**bb)**(1./bb))
-        psim0=-aa*np.log(zetam0+(1.+zetam0**bb)**(1./bb))
-        phimcorr=1.+aa*(zetamcorr+zetamcorr**bb*(1.+zetamcorr**bb)**((1.-bb)/bb))/(zetamcorr+(1.+zetamcorr**bb)**(1./bb))
-        
-        cc=5.3
-        dd=1.1
-        psih=-cc*np.log(zeta+(1.+zeta**dd)**(1./dd))
-        psih0=-cc*np.log(zetah0+(1.+zetah0**dd)**(1./dd))
-        phihcorr=1.+cc*(zetahcorr+zetahcorr**dd*(1.+zetahcorr**dd)**((1.-dd)/dd))/(zetahcorr+(1.+zetahcorr**dd)**(1./dd))
-    
-    psistrm=phimcorr*(1./lam)*np.log(1.+lam/(mum*zzzs))*np.exp(-mum*zzzs)
-    psistrh=phihcorr*(1./lam)*np.log(1.+lam/(muh*zzzs))*np.exp(-muh*zzzs)
-    
-    funm=lnzzz0-psim+psim0 +psistrm
-    funh=lnzzz0+kBmin1-psih+psih0 +psistrh
-    return funm,funh
-

From adfa5b63589c2880a80b9d4baf321d48603b3937 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 21 Aug 2018 22:05:44 +0200
Subject: [PATCH 012/129]  almost initial version

---
 Makefile                                      |    8 -
 MakefileMac                                   |    9 -
 data_air.py                                   |  473 ----
 data_global.py                                |  936 -------
 examples/run_soundings/batch_run_soundings.py |   76 -
 examples/run_soundings/run.py                 |  264 --
 examples/run_soundings/run_iter.py            |  364 ---
 examples/run_soundings/run_iter_test.py       |  367 ---
 examples/run_soundings/trash/run_test.py      |  241 --
 examples/setup_soundings/setup_bllast.py      |  719 ------
 examples/setup_soundings/setup_global.py      |  310 ---
 examples/setup_soundings/setup_goamazon.py    |  740 ------
 examples/setup_soundings/setup_humppa.py      |  732 ------
 .../setup_soundings/trash/setup_global_old.py |  284 ---
 interface_functions.py                        |  506 ----
 lib/class4gl.py                               | 1611 ------------
 lib/data_air.py                               |  473 ----
 lib/data_global.py                            |  936 -------
 lib/interface_functions.py                    |  506 ----
 lib/interface_multi.py                        | 2061 ---------------
 lib/model.py                                  | 2214 -----------------
 lib/ribtol/Makefile                           |    8 -
 lib/ribtol/MakefileMac                        |    9 -
 lib/ribtol/ribtol.cpp                         |   81 -
 lib/ribtol/ribtol.pyx                         |   48 -
 lib/ribtol/ribtol_hw.py                       |  165 --
 lib/ribtol/setup.py                           |   12 -
 model.py                                      | 2214 -----------------
 runmodel.py                                   |  130 -
 29 files changed, 16497 deletions(-)
 delete mode 100644 Makefile
 delete mode 100644 MakefileMac
 delete mode 100644 data_air.py
 delete mode 100644 data_global.py
 delete mode 100644 examples/run_soundings/batch_run_soundings.py
 delete mode 100644 examples/run_soundings/run.py
 delete mode 100644 examples/run_soundings/run_iter.py
 delete mode 100644 examples/run_soundings/run_iter_test.py
 delete mode 100644 examples/run_soundings/trash/run_test.py
 delete mode 100644 examples/setup_soundings/setup_bllast.py
 delete mode 100644 examples/setup_soundings/setup_global.py
 delete mode 100644 examples/setup_soundings/setup_goamazon.py
 delete mode 100644 examples/setup_soundings/setup_humppa.py
 delete mode 100644 examples/setup_soundings/trash/setup_global_old.py
 delete mode 100644 interface_functions.py
 delete mode 100644 lib/class4gl.py
 delete mode 100644 lib/data_air.py
 delete mode 100644 lib/data_global.py
 delete mode 100644 lib/interface_functions.py
 delete mode 100644 lib/interface_multi.py
 delete mode 100644 lib/model.py
 delete mode 100644 lib/ribtol/Makefile
 delete mode 100644 lib/ribtol/MakefileMac
 delete mode 100644 lib/ribtol/ribtol.cpp
 delete mode 100644 lib/ribtol/ribtol.pyx
 delete mode 100644 lib/ribtol/ribtol_hw.py
 delete mode 100644 lib/ribtol/setup.py
 delete mode 100644 model.py
 delete mode 100644 runmodel.py

diff --git a/Makefile b/Makefile
deleted file mode 100644
index e23e3e1..0000000
--- a/Makefile
+++ /dev/null
@@ -1,8 +0,0 @@
-ribtol.so : ribtol.o
-	g++ -O3 -shared -Wl -z -def -o ribtol.so -lpython2.6 -lboost_python ribtol.o
-
-ribtol.o : ribtol.cpp
-	g++ -c -O3 -fPIC ribtol.cpp -I/usr/include/python2.6
-
-clean : 
-	rm -rf ribtol.o ribtol.so
diff --git a/MakefileMac b/MakefileMac
deleted file mode 100644
index bf34ea8..0000000
--- a/MakefileMac
+++ /dev/null
@@ -1,9 +0,0 @@
-# Note: boost-python needs to be installed: brew install boost-python -with-python3 -without-python
-ribtol.so : ribtol.o
-	clang++ -O3 -shared -o ribtol.so -L/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib -lpython3.6m -L/usr/local/lib -lboost_python3-mt -lpython ribtol.o
-
-ribtol.o : ribtol.cpp
-	clang++ -c -O3 -fPIC ribtol.cpp -I/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/include/python3.6m -I/usr/local/include
-
-clean : 
-	rm -rf ribtol.o ribtol.so
diff --git a/data_air.py b/data_air.py
deleted file mode 100644
index 1c51deb..0000000
--- a/data_air.py
+++ /dev/null
@@ -1,473 +0,0 @@
-import numpy as np
-
-from bs4 import BeautifulSoup
-import pandas as pd
-import datetime as dt
-#import pylab as pl
-import io
-import os
-import calendar
-import Pysolar
-import Pysolar.util
-
-
-
-#from urllib import request
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       #self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_air_input()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        #print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_air_input()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_air_input()
-       
-
-
-    def get_values_air_input(self,latitude=None,longitude=None):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        BLHV = np.max((BLHV,10.))
-        BLHVu = np.max((BLHVu,10.))
-        BLHVd = np.max((BLHVd,10.))
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
-                
-                # get index of lowest valid observation. This seems to vary
-                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
-                if len(idxvalid) > 0:
-                    #print('idxvalid',idxvalid)
-                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
-                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
-                    else:
-                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
-                    #print(col,meanabl)
-               
-                
-                # if col == 'PRES':
-                #     meanabl =  
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV',]:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
-                        dtheta = np.max((0.1,dtheta_pre))
-                        #meanabl = meanabl - (dtheta - dtheta_pre)
-                        #print('dtheta_pre',dtheta_pre)
-                        #print('dtheta',dtheta)
-                        #print('meanabl',meanabl)
-                        #stop
-                        
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
-            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-            
-            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
-        
-            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
-            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
-
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-
-        try:
-            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
-        except:
-            print("could not convert latitude coordinate")
-            PARAMS.insert(0,'latitude', np.nan)
-            PARAMS.insert(0,'lat', np.nan)
-        try:
-            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
-            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
-            PARAMS.insert(0,'lon', 0.)
-        except:
-            print("could not convert longitude coordinate")
-            PARAMS.insert(0,'longitude', np.nan)
-            PARAMS.insert(0,'lon', 0.)
-
-        if latitude is not None:
-            print('overwriting latitude with specified value')
-            PARAMS['latitude'] = np.float(latitude)
-            PARAMS['lat'] = np.float(latitude)
-        if longitude is not None:
-            print('overwriting longitude with specified value')
-            PARAMS['longitude'] = np.float(longitude)
-        try:
-            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
-            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
-            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            # This is the nearest datetime when sun is up (for class)
-            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
-            # apply the same time shift for UTC datetime
-            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
-            
-        except:
-            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
-            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
-            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
-            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
-
-        
-
-        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
-        # as we are forcing lon equal to zero this is is expressed in local suntime
-        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
-
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
-        elif self.mode == 'b': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNb
-            BLCOLUMN = ONE_COLUMNb
-        elif self.mode == 'u': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNu
-            BLCOLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNd
-            BLCOLUMN = ONE_COLUMNd
-        else:
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb
-
-        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
-        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
-        # print(BLCOLUMN['HAGL'][lt6000])
-        # print(BLCOLUMN['HAGL'][lt2500])
-        # 
-        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
-
-        #print(BLCOLUMN['HAGL'][lt2500])
-        PARAMS.insert(0,'OK',
-                      ((BLHVe < 200.) and 
-                       ( len(np.where(lt6000)[0]) > 5) and
-                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
-                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
-                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
-                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
-                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
-                      )
-                     )
-
-        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
-        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
-        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
-        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
-        
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = USE_ONECOLUMN
-        # if self.mode == 'o': #original 
-        #     self.ONE_COLUMN = ONE_COLUMN
-        # elif self.mode == 'b': # best BLH
-        #     self.ONE_COLUMN = ONE_COLUMNb
-        # elif self.mode == 'u':# upper BLH
-        #     self.ONE_COLUMN = ONE_COLUMNu
-        # elif self.mode == 'd': # lower BLH
-        #     self.ONE_COLUMN=ONE_COLUMNd
-        # else:
-        #     self.ONE_COLUMN = ONE_COLUMN
-
diff --git a/data_global.py b/data_global.py
deleted file mode 100644
index 9c3d9b5..0000000
--- a/data_global.py
+++ /dev/null
@@ -1,936 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: Hendrik Wouters
-
-Purpose: provides class routines for ground and atmosphere conditions used for
-the CLASS miced-layer model
-
-Usage:
-    from data_global import data_global
-    from class4gl import class4gl_input
-    from data_soundings import wyoming
-
-    # create a data_global object and load initial data pages
-    globaldata = data_global()
-    globaldata.load_datasets()
-    # create a class4gl_input object
-    c4gli = class4gl_input()
-    # Initialize it with profile data. We need to do this first. Actually this
-    # will set the coordinate parameters (datetime, latitude, longitude) in
-    # class4gl_input.pars.__dict__, which is required to read point data from
-    # the data_global object.
-
-    # open a Wyoming stream for a specific station
-    wy_strm = wyoming(STNM=91376)
-    # load the first profile
-    wy_strm.find_first()
-    # load the profile data into the class4gl_input object
-    c4gli.get_profile_wyoming(wy_strm)
-    
-    # and finally, read the global input data for this profile
-    c4gli.get_global_input(globaldata)
-
-
-"""
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-#import pynacolada as pcd
-import pandas as pd
-import xarray as xr
-import os
-import glob
-import sys
-import errno
-import warnings
-import logging
-
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-class book(object):
-    """ this is a class for a dataset spread over multiple files. It has a
-    similar purpose  open_mfdataset, but only 1 file (called current 'page')
-    one is loaded at a time. This saves precious memory.  """
-    def __init__(self,fn,concat_dim = None,debug_level=None):
-        self.logger = logging.getLogger('book')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # filenames are expanded as a list and sorted by filename
-        self.pages = glob.glob(fn); self.pages.sort()
-        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
-        if len(self.pages) == 0:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
-        self.ipage = -1; self.page = None
-        self.renames = {} # each time when opening a file, a renaming should be done.
-        self.set_page(0)
-
-        # we consider that the outer dimension is the one we concatenate
-        self.concat_dim = concat_dim
-        if self.concat_dim is None:
-            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
-
-    # this wraps the xarray sel-commmand
-    def sel(*args, **kwargs):
-        for dim in kwargs.keys():
-            if dim == self.concat_dim:
-                self.browse_page(**{dim: kwargs[dim]})
-        return page.sel(*args,**kwargs)
-
-
-    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
-    #def __getattr__(self,attr):
-    #    orig_attr = self.page.__getattribute__(attr)
-    #    if callable(orig_attr):
-    #        def hooked(*args, **kwargs):
-    #            for dim in kwargs.keys():
-    #                if dim == self.concat_dim:
-    #                    self.browse_page(**{dim: kwargs[dim]})
-    #
-    #            result = orig_attr(*args, **kwargs)
-    #            # prevent wrapped_class from becoming unwrapped
-    #            if result == self.page:
-    #                return self
-    #            self.post()
-    #            return result
-    #        return hooked
-    #    else:
-    #        return orig_attr
-
-    def set_renames(self,renames):
-        #first, we convert back to original names, and afterwards, we apply the update of the renames.
-        reverse_renames = dict((v,k) for k,v in self.renames.items())
-        self.renames = renames
-        if self.page is not None:
-            self.page = self.page.rename(reverse_renames)
-            self.page = self.page.rename(self.renames)
-
-    def set_page(self,ipage,page=None):
-        """ this sets the right page according to ipage:
-                - We do not switch the page if we are already at the right one
-                - we set the correct renamings (level -> lev, latitude -> lat,
-                etc.)
-                - The dataset is also squeezed.
-        """
-
-        if ((ipage != self.ipage) or (page is not None)):
-
-            if self.page is not None:
-                self.page.close()
-
-            self.ipage = ipage
-            if page is not None:
-                self.page = page
-            else:
-                if self.ipage == -1:
-                   self.page = None
-                else:
-                    #try:
-
-                    self.logger.info("Switching to page "+str(self.ipage)+': '\
-                                     +self.pages[self.ipage])
-                    self.page = xr.open_dataset(self.pages[self.ipage])
-
-
-            # do some final corrections to the dataset to make them uniform
-            if self.page is not None:
-               if 'latitude' in self.page.dims:
-#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
-               if 'level' in self.page.dims:
-                   self.page = self.page.rename({'level':'lev'})
-
-               self.page = self.page.rename(self.renames)
-               self.page = self.page.squeeze(drop=True)
-
-    def browse_page(self,rewind=2,**args):
-
-        # at the moment, this is only tested with files that are stacked according to the time dimension.
-        dims = args.keys()
-
-
-        if self.ipage == -1:
-            self.set_page(0)
-
-        found = False
-        iipage = 0
-        startipage = self.ipage - rewind
-        while (iipage < len(self.pages)) and not found:
-            ipage = (iipage+startipage) % len(self.pages)
-            for dim in args.keys():
-                this_file = True
-
-                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
-                if 'dims' not in self.__dict__:
-                    self.dims = {}
-                if dim not in self.dims.keys():
-                    self.dims[dim] = [None]*len(self.pages)
-
-                if self.dims[dim][ipage] is None:
-                    self.logger.info('Loading coordinates of dimension "'+dim+\
-                                     '" of page "' +str(ipage)+'".')
-                    self.set_page(ipage)
-                    # print(ipage)
-                    # print(dim)
-                    # print(dim,self.page[dim].values)
-                    self.dims[dim][ipage] = self.page[dim].values
-
-                # determine current time range of the current page
-                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
-                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
-
-                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
-                    this_file = False
-
-            if this_file:
-                found = True
-                self.set_page(ipage)
-            else:
-
-                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
-                #    iipage = len(self.pages) # we stop searching
-
-                iipage += 1
-
-        if not found:
-            self.logger.info("Page not found. Setting to page -1")
-            #iipage = len(self.pages) # we stop searching further
-            self.set_page(-1)
-
-        if self.ipage != -1:
-            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
-        else:
-            self.logger.debug("I'm now at page "+ str(self.ipage))
-
-
-class data_global(object):
-    def __init__(self,sources= {
-        # # old gleam
-        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
-        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
-        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
-        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
-        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
-        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
-        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
-        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
-        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
-        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
-        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
-        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
-        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
-        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
-        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
-        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
-        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
-        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
-        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
-        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
-        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
-        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
-        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
-        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
-        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
-        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
-        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
-        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
-        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
-        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
-        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
-        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
-        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
-        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
-        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
-        },debug_level=None):
-        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
-        self.sources = sources
-        self.datarefs = {}
-        self.datasets = {}
-        self.datetime = dt.datetime(1981,1,1)
-
-        self.logger = logging.getLogger('data_global')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-        self.debug_level = debug_level
-
-        warnings.warn('omitting pressure field p and advection')
-
-    def in_library(self,fn):
-        if fn not in self.library.keys():
-            return False
-        else:
-            print("Warning: "+fn+" is already in the library.")
-            return True
-
-    def add_to_library(self,fn):
-        if not self.in_library(fn):
-            print("opening: "+fn)
-            self.library[fn] = \
-                book(fn,concat_dim='time',debug_level=self.debug_level)
-
-            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
-            #if 'latitude' in self.library[fn].variables:
-            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-
-    # default procedure for loading datasets into the globaldata library
-    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
-        if type(varssource) is str:
-            varssource = [varssource]
-        if type(varsdest) is str:
-            varsdest = [varsdest]
-
-        self.add_to_library(input_fn)
-
-        if varssource is None:
-            varssource = []
-            for var in self.sources[input_fn].variables:
-                avoid = \
-                ['lat','lon','latitude','longitude','time','lev','level']
-                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
-                    varssource.append(var)
-
-        if varsdest is None:
-            varsdest = varssource
-
-        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        for ivar,vardest in enumerate(varsdest):
-            varsource = varssource[ivar]
-            print('setting '+vardest+' as '+varsource+' from '+input_fn)
-
-            if vardest in self.datarefs.keys():
-                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
-            #self.add_to_library(fn,varsource,vardest)
-            if vardest != varsource:
-                libkey = input_fn+'.'+varsource+'.'+vardest
-                if libkey not in self.library.keys():
-                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
-                    self.library[libkey] = book(input_fn,\
-                                                debug_level=self.debug_level)
-                    self.library[libkey].set_renames({varsource: vardest})
-
-                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-            else:
-                self.datarefs[vardest] = input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-
-            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
-            #     print('Warning: '+ vardest "not in " + input_fn)
-
-
-
-    def load_datasets(self,sources = None,recalc=0):
-
-        if sources is None:
-            sources = self.sources
-        for key in sources.keys():
-            #datakey,vardest,*args = key.split(':')
-            datakey,vardest = key.split(':')
-            #print(datakey)
-
-            fnvarsource = sources[key].split(':')
-            if len(fnvarsource) > 2:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource,fnargs = fnvarsource
-                fnargs = [fnargs]
-            elif len(fnvarsource) > 1:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource = fnvarsource
-                fnargs = []
-            else:
-                fn = sources[key]
-                varsource = vardest
-            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
-
-    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
-            # the default way of loading a 2d dataset
-            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
-                self.load_dataset_default(fn,varsource,vardest)
-            elif datakey == 'IGBPDIS':
-                if vardest == 'alpha':
-                    ltypes = ['W','B','H','TC']
-                    for ltype in ltypes:
-                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
-                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
-
-
-                    # landfr = {}
-                    # for ltype in ['W','B','H','TC']:
-                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
-
-
-
-                    keytemp = 'alpha'
-                    fnkeytemp = fn+':IGBPDIS:alpha'
-                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
-                        self.library[fnkeytemp]  = book(fnkeytemp,
-                                                        debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-                    else:
-                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
-                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
-                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
-                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
-                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
-                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
-                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
-
-                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-
-                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
-                        for ltype in ltypes:
-                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
-
-                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
-                        print('writing file to: '+fnkeytemp)
-                        os.system('rm '+fnkeytemp)
-                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
-                        self.library[fnkeytemp].close()
-
-
-                        self.library[fnkeytemp]  = \
-                            book(fnkeytemp,debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-
-
-                else:
-                    self.load_dataset_default(fn,varsource,vardest)
-
-
-            elif datakey == 'GLAS':
-                self.load_dataset_default(fn,varsource,vardest)
-                if vardest == 'z0m':
-                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
-                elif vardest == 'z0h':
-                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
-            elif datakey == 'DSMW':
-
-
-                # Procedure of the thermal properties:
-                # 1. determine soil texture from DSMW/10.
-                # 2. soil type with look-up table (according to DWD/EXTPAR)
-                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
-                #    with parameter look-up table from Noilhan and Planton (1989).
-                #    Note: The look-up table is inspired on DWD/COSMO
-
-                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
-
-
-
-                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
-                self.load_dataset_default(fn,'DSMW')
-                print('calculating texture')
-                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
-                TEMP  = {}
-                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
-                TEMP3 = {}
-                for SPKEY in SPKEYS:
-
-
-                    keytemp = SPKEY+'_values'
-                    fnoutkeytemp = fnout+':DSMW:'+keytemp
-                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                    else:
-                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
-                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
-                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-                        # for faster computation, we need to get it to memory out of Dask.
-                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
-                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
-
-                # yes, I know I only check the last file.
-                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
-                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
-                        print('idx',idx,SPKEY)
-                        SEL = (TEMP2 == idx)
-                    #     print(idx,len(TEMP3))
-                        for SPKEY in SPKEYS:
-                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
-
-                    for SPKEY in SPKEYS:
-                        keytemp = SPKEY+'_values'
-                        fnoutkeytemp = fnout+':DSMW:'+keytemp
-                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
-                        os.system('rm '+fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].close()
-
-
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                keytemp = 'texture'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-                else:
-                    self.library[fn+':DSMW:texture'] = xr.Dataset()
-                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
-                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
-                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
-                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-
-                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
-
-                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
-                    zundef[zundef < 0] = np.nan
-                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
-                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
-
-                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-
-
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-                print('calculating texture type')
-
-
-
-                keytemp = 'itex'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-                else:
-                    self.library[fnoutkeytemp] = xr.Dataset()
-                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-                    X = self.datasets['texture'].page['texture'].values*100
-                    X[pd.isnull(X)] = -9
-
-
-                    self.datasets[keytemp][keytemp].values = X
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
-                    self.datasets['itex'].close()
-
-
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-
-                keytemp = 'isoil'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                isoil_reprocessed = False
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-                else:
-                    isoil_reprocessed = True
-                    print('calculating soil type')
-                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    ITEX = self.datasets['itex'].page['itex'].values
-                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
-                    LOOKUP = [
-                              [-10 ,9],# ocean
-                              [0 ,7],# fine textured, clay (soil type 7)
-                              [20,6],# medium to fine textured, loamy clay (soil type 6)
-                              [40,5],# medium textured, loam (soil type 5)
-                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                              [80,3],# coarse textured, sand (soil type 3)
-                              [100,9],# coarse textured, sand (soil type 3)
-                            ]
-                    for iitex,iisoil in LOOKUP:
-                        ISOIL[ITEX > iitex] = iisoil
-                        print('iitex,iisoil',iitex,iisoil)
-
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    LOOKUP = [
-                              [9001, 1 ], # ice, glacier (soil type 1)
-                              [9002, 2 ], # rock, lithosols (soil type 2)
-                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                              [9,    9 ], # undefined (ocean)
-                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                              [9000, 9 ], # undefined (inland lake)
-                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                            ]
-                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
-
-                    CODE_VALUES[ITEX == 901200] = 9012
-                    for icode,iisoil in LOOKUP:
-                        ISOIL[CODE_VALUES == icode] = iisoil
-
-                    self.datasets['isoil']['isoil'].values = ISOIL
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-                    print('saved inbetween file to: '+fnoutkeytemp)
-
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                #adopted from data_soil.f90 (COSMO5.0)
-                SP_LOOKUP = {
-                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
-                  # (by index)                                           loam                    loam                                water      ice
-                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-                  # Important note: For peat, the unknown values below are set equal to that of loam
-                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
-                  #error in table 2 of NP89: values need to be multiplied by e-6
-                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
-
-                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
-                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
-                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
-                }
-
-
-                # isoil_reprocessed = False
-                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-
-                #     self.library[fn+':DSMW:isoil'] = \
-                #             book(fnoutkeytemp,debug_level=self.debug_level)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-                # else:
-                #     isoil_reprocessed = True
-                #     print('calculating soil type')
-                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-
-
-
-                # this should become cleaner in future but let's hard code it for now.
-                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
-                print('calculating soil parameter')
-                DATATEMPSPKEY = {}
-                if (recalc < 1) and (isoil_reprocessed == False): 
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        keytemp = SPKEY
-                        fnoutkeytemp=fnout+':DSMW:'+keytemp
-                        self.library[fn+':DSMW:'+SPKEY] =\
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
-                        self.datarefs[SPKEY] =fnoutkeytemp
-                else:
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-
-                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
-                    ISOIL = self.datasets['isoil'].page['isoil'].values
-                    print(np.where(ISOIL>0.))
-                    for i in range(11):
-                        SELECT = (ISOIL == i)
-                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
-
-                        os.system('rm '+fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].close()
-                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
-
-                        self.library[fn+':DSMW:'+SPKEY] = \
-                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-
-
-            else:
-                self.load_dataset_default(fn,varsource,vardest)
-
-
-
-
-
-
-#
-#                 # only print the last parameter value in the plot
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'cala'
-#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'crhoc'
-#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#     key = "CERES"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         CERES_start_date = dt.datetime(2000,3,1)
-#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-#
-#         var = 'cc'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#         print(class_settings.lat,class_settings.lon)
-#
-#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
-#
-#         input_nc.close()
-#
-
-
-#     key = "GIMMS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-#         print("Reading Leag Area Index from "+input_fn)
-#         var = 'LAI'
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-#
-#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-#
-#         if np.isnan(tarray[idatetime]):
-#             print("interpolating GIMMS cveg nan value")
-#
-#             mask = np.isnan(tarray)
-#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-#             else:
-#                 print("Warning. Could not interpolate GIMMS cveg nan value")
-#
-#         class_settings.__dict__[var] = tarray[idatetime]
-#
-#         input_nc.close()
-#
-#     key = "IGBPDIS_ALPHA"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         var = 'alpha'
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-#         print("Reading albedo from "+input_fn)
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#
-#         landfr = {}
-#         for ltype in ['W','B','H','TC']:
-#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-#
-#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-#
-#         alpha=0.
-#         for ltype in landfr.keys():
-#             alpha += landfr[ltype]*aweights[ltype]
-#
-#
-#         class_settings.__dict__[var] = alpha
-#         input_nc.close()
-#
-#
-#     key = "ERAINT_ST"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         print("Reading soil temperature from "+input_fn)
-#
-#         var = 'Tsoil'
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         var = 'T2'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-#
-#
-#         input_nc.close()
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #var = 'T2'
-#     #valold = class_settings.__dict__[var]
-#     #
-#     #class_settings.__dict__[var] = 305.
-#     #class_settings.__dict__['Tsoil'] = 302.
-#     #valnew = class_settings.__dict__[var]
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #
-#     #var = 'Lambda'
-#     #valold = class_settings.__dict__[var]
-#
-#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
-#     ## I need to ask Chiel.
-#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-#     #
-#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
-#     #class_settings.__dict__[var] = valnew
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     key = "GLAS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-#         print("Reading canopy height for determining roughness length from "+input_fn)
-#         var = 'z0m'
-#
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-#
-#         lowerlimit = 0.01
-#         if testval < lowerlimit:
-#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-#             class_settings.__dict__[var] = lowerlimit
-#         else:
-#             class_settings.__dict__[var] = testval
-#
-#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-#
-#
-#         input_nc.close()
-
-
-
-
-
diff --git a/examples/run_soundings/batch_run_soundings.py b/examples/run_soundings/batch_run_soundings.py
deleted file mode 100644
index c4fc40e..0000000
--- a/examples/run_soundings/batch_run_soundings.py
+++ /dev/null
@@ -1,76 +0,0 @@
-
-import argparse
-
-import pandas as pd
-import os
-import math
-import numpy as np
-import sys
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-df_stations = pd.read_csv(fn_stations)
-
-# if 'path-soundings' in args.__dict__.keys():
-#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-# else:
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
-    parser.add_argument('--experiments')#should be ';'-seperated list
-    parser.add_argument('--split-by',default=-1)
-    args = parser.parse_args()
-
-experiments = args.experiments.split(';')
-#SET = 'GLOBAL'
-SET = args.dataset
-print(args.experiments)
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-for expname in experiments:
-    #exp = EXP_DEFS[expname]
-    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-    os.system('rm -R '+path_exp)
-
-totalchunks = 0
-for istation,current_station in all_stations.iterrows():
-    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
-    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
-    totalchunks +=chunks_current_station
-
-#if sys.argv[1] == 'qsub':
-# with qsub
-os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
-                                       ',split_by='+str(args.split_by)+\
-                                       ',exec='+str(args.exec)+\
-                                       ',experiments='+str(args.experiments))
-# elif sys.argv[1] == 'wsub':
-#     
-#     # with wsub
-#     STNlist = list(df_stations.iterrows())
-#     NUMSTNS = len(STNlist)
-#     PROCS = NUMSTNS 
-#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-# 
-#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
-
diff --git a/examples/run_soundings/run.py b/examples/run_soundings/run.py
deleted file mode 100644
index 46eeea1..0000000
--- a/examples/run_soundings/run.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--error-handling',default='dump_on_success')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk')
-    parser.add_argument('--c4gl-path',default='')
-    args = parser.parse_args()
-
-
-
-if args.c4gl_path == '': 
-    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-else:
-    sys.path.insert(0, args.c4gl_path)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    run_stations = pd.DataFrame(all_stations)
-    if args.last_station is not None:
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    if args.first_station is not None:
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    run_station_chunk = 0
-    if args.station_chunk is not None:
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            print(records_morning_station_chunk)
-
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-
-                    if args.error_handling == 'dump_always':
-                        try:
-                            c4gl.run()
-                        except:
-                            print('run not succesfull')
-                        onerun = True
-
-                        c4gli_morning.dump(file_ini)
-                        
-                        
-                        c4gl.dump(file_mod,\
-                                  include_input=False,\
-                                  #timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    # in this case, only the file will dumped if the runs were
-                    # successful
-                    elif args.error_handling == 'dump_on_succes':
-                        try:
-                            c4gl.run()
-                            print('run not succesfull')
-                            onerun = True
-
-                            c4gli_morning.dump(file_ini)
-                            
-                            
-                            c4gl.dump(file_mod,\
-                                      include_input=False,\
-                                      #timeseries_only=timeseries_only,\
-                                     )
-                            onerun = True
-                        except:
-                            print('run not succesfull')
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/examples/run_soundings/run_iter.py b/examples/run_soundings/run_iter.py
deleted file mode 100644
index 5dfbaff..0000000
--- a/examples/run_soundings/run_iter.py
+++ /dev/null
@@ -1,364 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk',default=0)
-    args = parser.parse_args()
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if 'global_chunk' in args.__dict__.keys():
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    if 'last_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    
-    if 'first_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    if 'station_chunk' in args.__dict__.keys():
-        run_station_chunk = args.station_chunk
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-    for istation,current_station in run_stations.iterrows():
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                #if iexp == 11:
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    
-                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
-                    EFobs = c4gli_morning.pars.EF
-                    
-                    b = c4gli_morning.pars.wwilt
-                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
-                    
-                    
-                    try:
-                        #fb = f(b)
-                        c4gli_morning.pars.wg = b
-                        c4gli_morning.pars.w2 = b
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fb = EFmod - EFobs
-                        EFmodb = EFmod
-                        c4glb = c4gl
-                        c4gli_morningb = c4gli_morning
-                        
-                        #fc = f(c)
-                        c4gli_morning.pars.wg = c
-                        c4gli_morning.pars.w2 = c
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fc = EFmod - EFobs
-                        print (EFmodb,EFobs,fb)
-                        print (EFmod,EFobs,fc)
-                        c4glc = c4gl
-                        c4gli_morningc = c4gli_morning
-                        i=0
-                        
-
-                        if fc*fb > 0.:
-                            if abs(fb) < abs(fc):
-                                c4gl = c4glb
-                                c4gli_morning = c4gli_morningb
-                            else:
-                                c4gl = c4glc
-                                c4gli_morning = c4gli_morningc
-                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
-                        
-                        else:
-                            print('starting ITERATION!!!')
-                            cn  = c - fc/(fc-fb)*(c-b)
-                            
-                            
-                            #fcn = f(cn)
-                            c4gli_morning.pars.wg = np.asscalar(cn)
-                            c4gli_morning.pars.w2 = np.asscalar(cn)
-                            c4gl = class4gl(c4gli_morning)
-                            c4gl.run()
-                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                            
-                            tol = 0.02
-                            ftol = 10.
-                            maxiter = 10
-                            
-                            is1=0
-                            is1max=1
-                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
-                                if fc * fcn > 0:
-                                    temp = c
-                                    c = b
-                                    b = temp
-                                
-                                a = b
-                                fa = fb
-                                b = c
-                                fb = fc
-                                c = cn
-                                fc = fcn
-                                              
-                                print(i,a,b,c,fcn)
-                                
-                                s1 = c - fc/(fc-fb)*(c-b) 
-                                s2 = c - fc/(fc-fa)*(c-a)
-                                
-                                
-                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
-                                
-                                
-                                if (abs(s1-b) < abs(s2-b)):
-                                    is1 = 0
-                                else:
-                                    is1 +=1
-                                    
-                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
-                                if is1 < is1max:           
-                                    s = s1
-                                    print('s1')
-                                else:
-                                    is1 = 0
-                                    s = s2
-                                    print('s2')
-                                
-                                if c > b:
-                                    l = b
-                                    r = c
-                                else:
-                                    l = c
-                                    r = b
-                                
-                                m = (b+c)/2.
-                                     
-                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
-                                    cn = s
-                                    print('midpoint')
-                                else:
-                                    cn = m
-                                    print('bissection')
-                                    
-                                
-                                #fcn = f(cn)
-                                c4gli_morning.pars.wg = np.asscalar(cn)
-                                c4gli_morning.pars.w2 = np.asscalar(cn)
-                                c4gl = class4gl(c4gli_morning)
-                                c4gl.run()
-                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                                
-                            
-                                i+=1
-                                
-                            if i == maxiter:
-                                raise StopIteration('did not converge')
-
-
-
-
-                        #c4gl = class4gl(c4gli_morning)
-                        #c4gl.run()
-
-                        c4gli_morning.pars.itersteps = i
-                        c4gli_morning.dump(file_ini)
-                        
-                        
-                        c4gl.dump(file_mod,\
-                                      include_input=False,\
-                                   #   timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    except:
-                        print('run not succesfull')
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/examples/run_soundings/run_iter_test.py b/examples/run_soundings/run_iter_test.py
deleted file mode 100644
index eefd475..0000000
--- a/examples/run_soundings/run_iter_test.py
+++ /dev/null
@@ -1,367 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk')
-    args = parser.parse_args()
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    run_stations = pd.DataFrame(all_stations)
-    if args.last_station is not None:
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    if args.first_station is not None:
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    run_station_chunk = 0
-    if args.station_chunk is not None:
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-                #if iexp == 11:
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    
-                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
-                    EFobs = c4gli_morning.pars.EF
-                    
-                    b = c4gli_morning.pars.wwilt
-                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
-                    
-                    
-                    try:
-                        #fb = f(b)
-                        c4gli_morning.pars.wg = b
-                        c4gli_morning.pars.w2 = b
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fb = EFmod - EFobs
-                        EFmodb = EFmod
-                        c4glb = c4gl
-                        c4gli_morningb = c4gli_morning
-                        
-                        #fc = f(c)
-                        c4gli_morning.pars.wg = c
-                        c4gli_morning.pars.w2 = c
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fc = EFmod - EFobs
-                        print (EFmodb,EFobs,fb)
-                        print (EFmod,EFobs,fc)
-                        c4glc = c4gl
-                        c4gli_morningc = c4gli_morning
-                        i=0
-                        
-
-                        if fc*fb > 0.:
-                            if abs(fb) < abs(fc):
-                                c4gl = c4glb
-                                c4gli_morning = c4gli_morningb
-                            else:
-                                c4gl = c4glc
-                                c4gli_morning = c4gli_morningc
-                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
-                        
-                        else:
-                            print('starting ITERATION!!!')
-                            cn  = c - fc/(fc-fb)*(c-b)
-                            
-                            
-                            #fcn = f(cn)
-                            c4gli_morning.pars.wg = np.asscalar(cn)
-                            c4gli_morning.pars.w2 = np.asscalar(cn)
-                            c4gl = class4gl(c4gli_morning)
-                            c4gl.run()
-                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                            
-                            tol = 0.02
-                            ftol = 10.
-                            maxiter = 10
-                            
-                            is1=0
-                            is1max=1
-                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
-                                if fc * fcn > 0:
-                                    temp = c
-                                    c = b
-                                    b = temp
-                                
-                                a = b
-                                fa = fb
-                                b = c
-                                fb = fc
-                                c = cn
-                                fc = fcn
-                                              
-                                print(i,a,b,c,fcn)
-                                
-                                s1 = c - fc/(fc-fb)*(c-b) 
-                                s2 = c - fc/(fc-fa)*(c-a)
-                                
-                                
-                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
-                                
-                                
-                                if (abs(s1-b) < abs(s2-b)):
-                                    is1 = 0
-                                else:
-                                    is1 +=1
-                                    
-                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
-                                if is1 < is1max:           
-                                    s = s1
-                                    print('s1')
-                                else:
-                                    is1 = 0
-                                    s = s2
-                                    print('s2')
-                                
-                                if c > b:
-                                    l = b
-                                    r = c
-                                else:
-                                    l = c
-                                    r = b
-                                
-                                m = (b+c)/2.
-                                     
-                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
-                                    cn = s
-                                    print('midpoint')
-                                else:
-                                    cn = m
-                                    print('bissection')
-                                    
-                                
-                                #fcn = f(cn)
-                                c4gli_morning.pars.wg = np.asscalar(cn)
-                                c4gli_morning.pars.w2 = np.asscalar(cn)
-                                c4gl = class4gl(c4gli_morning)
-                                c4gl.run()
-                                fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
-                                
-                            
-                                i+=1
-                                
-                            if i == maxiter:
-                                raise StopIteration('did not converge')
-
-
-
-
-                        #c4gl = class4gl(c4gli_morning)
-                        #c4gl.run()
-                        onerun = True
-
-                        c4gli_morning.pars.itersteps = i
-                    except:
-                        print('run not succesfull')
-                    c4gli_morning.dump(file_ini)
-                    
-                    
-                    c4gl.dump(file_mod,\
-                                  include_input=False,\
-                               #   timeseries_only=timeseries_only,\
-                             )
-                    onerun = True
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/examples/run_soundings/trash/run_test.py b/examples/run_soundings/trash/run_test.py
deleted file mode 100644
index 767d960..0000000
--- a/examples/run_soundings/trash/run_test.py
+++ /dev/null
@@ -1,241 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-
-import argparse
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk')
-    parser.add_argument('--c4gl-path',default='')
-    args = parser.parse_args()
-
-if args.c4gl_path == '': 
-    sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-else:
-    sys.path.insert(0, args.c4gl_path)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-
-EXP_DEFS  =\
-{
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-if args.global_chunk is not None:
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
-
-        totalchunks +=chunks_current_station
-
-else:
-    run_stations = pd.DataFrame(all_stations)
-    if args.last_station is not None:
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    if args.first_station is not None:
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    run_station_chunk = 0
-    if args.station_chunk is not None:
-        run_station_chunk = args.station_chunk
-
-#print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
-records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
-
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            print(records_morning_station_chunk)
-
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
-                    
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-                    try:
-                        c4gl.run()
-                    except:
-                        print('run not succesfull')
-                    onerun = True
-
-                    c4gli_morning.dump(file_ini)
-                    
-                    
-                    c4gl.dump(file_mod,\
-                              include_input=False,\
-                              #timeseries_only=timeseries_only,\
-                             )
-                    onerun = True
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
diff --git a/examples/setup_soundings/setup_bllast.py b/examples/setup_soundings/setup_bllast.py
deleted file mode 100644
index af8c8bb..0000000
--- a/examples/setup_soundings/setup_bllast.py
+++ /dev/null
@@ -1,719 +0,0 @@
-# -*- coding: utf-8 -*-
-# Read data from BLLAST campaing and convert it to class4gl input
-
-# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
-#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : 42.971834,
-                  "longitude" : 0.3671169,
-                  "name" : "the BLLAST experiment"
-                })
-current_station.name = 90001
-
-
-
-
-
-# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
-# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
-# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
-# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
-
-HOUR_FILES = \
-{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110619_1750_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[18,'RS_20110620_1750_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110625_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110702_1655_site1_MODEM_CRA.cor.fmt']},
- dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
-}
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-def efrom_rh100_T(rh100,T):
-    return esat(T)*rh100/100.
-def qfrom_e_p(e,p):
-    return epsilon * e/(p - (1.-epsilon)*e)
-
-def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
-                                     #widths=[14]*19,
-                                     #skiprows=9,
-                                     #skipfooter=15,
-                                     #decimal='.',
-                                     #header=None,
-                                     #names = columns,
-                                     #na_values='-----')
-        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
-        print(air_balloon_in.columns)
-        rowmatches = {
-            't':      lambda x: x['TaRad']+273.15,
-            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
-            'p':      lambda x: x['Press']*100.,
-            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
-            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
-            'z':      lambda x: x['Altitude'] -582.,
-            # from virtual temperature to absolute humidity
-            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
-        }
-        
-        air_balloon = pd.DataFrame()
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon_in)
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
-        }
-        
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-        # filter data so that potential temperature always increases with
-        # height 
-        cols = []
-        for column in air_ap_tail.columns:
-            #if column != 'z':
-                cols.append(column)
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-
-
-
-        # # make theta increase strong enough to avoid numerical
-        # # instability
-        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        # air_ap_tail = pd.DataFrame()
-        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # theta_low = air_ap_head['theta'].iloc[2]
-        # z_low = air_ap_head['z'].iloc[2]
-        # ibottom = 0
-        # for itop in range(0,len(air_ap_tail_orig)):
-        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-        #     if ((theta_mean > (theta_low+0.2) ) and \
-        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
-
-        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-        #         ibottom = itop+1
-        #         theta_low = air_ap_tail.theta.iloc[-1]
-        #         z_low =     air_ap_tail.z.iloc[-1]
-        #     # elif  (itop > len(air_ap_tail_orig)-10):
-        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        # air_ap = \
-        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        # 
-        # # we copy the pressure at ground level from balloon sounding. The
-        # # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
-        # write local solar time, we need to assign the timezone to UTC (which
-        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
-        # tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise']+dt.timedelta(hours=2))\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        print('ldatetime_daylight',dpars['ldatetime_daylight'])
-        print('ldatetime',dpars['ldatetime'])
-        print('lSunrise',dpars['lSunrise'])
-        dpars['day'] = dpars['ldatetime'].day
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        print('tstart',dpars['tstart'])
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='bllast',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
-    
-    print(humpafn)
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-
-    
-    c4gli_morning.pars.sw_ac = []
-    c4gli_morning.pars.sw_ap = True
-    c4gli_morning.pars.sw_lit = False
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-
-
-# stations_for_iter = stations(path_exp)
-# for STNID,station in stations_iterator(stations_for_iter):
-#     records_current_station_index = \
-#             (records_ini.index.get_level_values('STNID') == STNID)
-#     file_current_station_mod = STNID
-# 
-#     with \
-#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-#         for (STNID,index),record_ini in records_iterator(records_ini):
-#             c4gli_ini = get_record_yaml(file_station_ini, 
-#                                         record_ini.index_start, 
-#                                         record_ini.index_end,
-#                                         mode='ini')
-#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-# 
-#             record_mod = records_mod.loc[(STNID,index)]
-#             c4gl_mod = get_record_yaml(file_station_mod, 
-#                                         record_mod.index_start, 
-#                                         record_mod.index_end,
-#                                         mode='mod')
-#             record_afternoon = records_afternoon.loc[(STNID,index)]
-#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-#                                         record_afternoon.index_start, 
-#                                         record_afternoon.index_end,
-#                                         mode='ini')
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/examples/setup_soundings/setup_global.py b/examples/setup_soundings/setup_global.py
deleted file mode 100644
index 79224d9..0000000
--- a/examples/setup_soundings/setup_global.py
+++ /dev/null
@@ -1,310 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thursday, March 29, 11:30 AM
-
-@author: Hendrik Wouters
-
-The dry-2-dry global radio sounding experiment.
-
-usage:
-    python setup_global.py 
-    where  is an integer indicating the row index of the station list
-    under odir+'/'+fn_stations (see below)
-
-this scripts should be called from the pbs script setup_global.pbs
-
-
-
-dependencies:
-    - pandas
-    - class4gl
-    - data_soundings
-
-
-"""
-
-""" import libraries """
-import pandas as pd
-import sys
-#import copy as cp
-import numpy as np
-from sklearn.metrics import mean_squared_error
-import logging
-import datetime as dt
-import os
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-
-
-#calculate the root mean square error
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from data_soundings import wyoming
-#from data_global import data_global
-
-# iniitialize global data
-globaldata = data_global()
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-# read the list of stations with valid ground data (list generated with
-# get_valid_stations.py)
-idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-
-df_stations = pd.read_csv(fn_stations)
-
-
-STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = 100
-BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-
-
-iPROC = int(sys.argv[1])
-
-
-for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
-    one_run = False
-# for iSTN,STN in STNlist[5:]:  
-    
-    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
-    
-
-    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
-    #                   for EXP in experiments.keys()])
-        
-    with open(fnout,'w') as fileout, \
-         open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
-        wy_strm.set_STNM(int(STN['ID']))
-
-        # we consider all soundings after 1981
-        wy_strm.find_first(year=1981)
-        #wy_strm.find(dt.datetime(2004,10,19,6))
-        
-        c4gli = class4gl_input(debug_level=logging.INFO)
-        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
-        # so we continue as long as we can find a new sounding
-                
-        while wy_strm.current is not None:
-            
-            c4gli.clear()
-            try:
-                c4gli.get_profile_wyoming(wy_strm)
-                #print(STN['ID'],c4gli.pars.datetime)
-                #c4gli.get_global_input(globaldata)
-
-                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
-
-                logic = dict()
-                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
-                logic['daylight'] = \
-                    ((c4gli.pars.ldatetime_daylight - 
-                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
-                
-                logic['springsummer'] = (c4gli.pars.theta > 278.)
-                
-                # we take 3000 because previous analysis (ie., HUMPPA) has
-                # focussed towards such altitude
-                le3000 = (c4gli.air_balloon.z <= 3000.)
-                logic['10measurements'] = (np.sum(le3000) >= 10) 
-
-                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
-
-                logic['mlerrlow'] = (\
-                        (len(np.where(leh)[0]) > 0) and \
-                        # in cases where humidity is not defined, the mixed-layer
-                        # values get corr
-                        (not np.isnan(c4gli.pars.theta)) and \
-                        (rmse(c4gli.air_balloon.theta[leh] , \
-                              c4gli.pars.theta,filternan_actual=True) < 1.)\
-                              )
-    
-
-                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
-                
-                print('logic:', logic)
-                # the result
-                morning_ok = np.mean(list(logic.values()))
-                print(morning_ok,c4gli.pars.ldatetime)
-
-            except:
-                morning_ok =False
-                print('obtain morning not good')
-            # the next sounding will be used either for an afternoon sounding
-            # or for the morning sounding of the next day.
-            wy_strm.find_next()
-            # If the morning is ok, then we try to find a decent afternoon
-            # sounding
-            if morning_ok == 1.:
-                print('MORNING OK!')
-                # we get the current date
-                current_date = dt.date(c4gli.pars.ldatetime.year, \
-                                       c4gli.pars.ldatetime.month, \
-                                       c4gli.pars.ldatetime.day)
-                c4gli_afternoon.clear()
-                print('AFTERNOON PROFILE CLEARED')
-                try:
-                    c4gli_afternoon.get_profile_wyoming(wy_strm)
-                    print('AFTERNOON PROFILE OK')
-
-                    if wy_strm.current is not None:
-                        current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
-                    else:
-                        # a dummy date: this will be ignored anyway
-                        current_date_afternoon = dt.date(1900,1,1)
-
-                    # we will dump the latest afternoon sounding that fits the
-                    # minimum criteria specified by logic_afternoon
-                    print(current_date,current_date_afternoon)
-                    c4gli_afternoon_for_dump = None
-                    while ((current_date_afternoon == current_date) and \
-                           (wy_strm.current is not None)):
-                        logic_afternoon =dict()
-
-                        logic_afternoon['afternoon'] = \
-                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                        logic_afternoon['daylight'] = \
-                          ((c4gli_afternoon.pars.ldatetime - \
-                            c4gli_afternoon.pars.ldatetime_daylight \
-                           ).total_seconds()/3600. <= 0.)
-
-
-                        le3000_afternoon = \
-                            (c4gli_afternoon.air_balloon.z <= 3000.)
-                        logic_afternoon['5measurements'] = \
-                            (np.sum(le3000_afternoon) >= 5) 
-
-                        # we only store the last afternoon sounding that fits these
-                        # minimum criteria
-
-                        afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                        print('logic_afternoon: ',logic_afternoon)
-                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                        if afternoon_ok == 1.:
-                            # # doesn't work :(
-                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                            
-                            # so we just create a new one from the same wyoming profile
-                            c4gli_afternoon_for_dump = class4gl_input()
-                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                        wy_strm.find_next()
-                        c4gli_afternoon.clear()
-                        c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                        if wy_strm.current is not None:
-                            current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
-                        else:
-                            # a dummy date: this will be ignored anyway
-                            current_date_afternoon = dt.date(1900,1,1)
-
-                        # Only in the case we have a good pair of soundings, we
-                        # dump them to disk
-                    if c4gli_afternoon_for_dump is not None:
-                        c4gli.update(source='pairs',pars={'runtime' : \
-                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                                 c4gli.pars.datetime_daylight).total_seconds())})
-    
-    
-                        print('ALMOST...')
-                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
-                                
-        
-                            c4gli.get_global_input(globaldata)
-                            print('VERY CLOSE...')
-                            if c4gli.check_source_globaldata() and \
-                                (c4gli.check_source(source='wyoming',\
-                                                   check_only_sections='pars')):
-                                c4gli.dump(fileout)
-                                
-                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                                
-                                
-                                # for keyEXP,dictEXP in experiments.items():
-                                #     
-                                #     c4gli.update(source=keyEXP,pars = dictEXP)
-                                #     c4gl = class4gl(c4gli)
-                                #     # c4gl.run()
-                                #     
-                                #     c4gl.dump(c4glfiles[key])
-                                
-                                print('HIT!!!')
-                                one_run = True
-                except:
-                    print('get profile failed')
-                
-    if one_run:
-        STN.name = STN['ID']
-        all_records_morning = get_records(pd.DataFrame([STN]),\
-                                      odir,\
-                                      subset='morning',
-                                      refetch_records=True,
-                                      )
-        all_records_afternoon = get_records(pd.DataFrame([STN]),\
-                                      odir,\
-                                      subset='afternoon',
-                                      refetch_records=True,
-                                      )
-    else:
-        os.system('rm '+fnout)
-        os.system('rm '+fnout_afternoon)
-
-    # for c4glfile in c4glfiles:
-    #     c4glfile.close()            
-
diff --git a/examples/setup_soundings/setup_goamazon.py b/examples/setup_soundings/setup_goamazon.py
deleted file mode 100644
index f9efe2c..0000000
--- a/examples/setup_soundings/setup_goamazon.py
+++ /dev/null
@@ -1,740 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import xarray as xr
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-import glob
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : -3.21,
-                  "longitude" : -60.6,
-                  "name" : "the GOAMAZON experiment"
-                })
-current_station.name = 90002
-
-# we define the columns ourselves because it is a mess in the file itself.
-columns =\
-['Time[min:sec]',
- 'P[hPa]',
- 'T[C]',
- 'U[%]',
- 'Wsp[m/s]',
- 'Wdir[Grd]',
- 'Lon[°]',
- 'Lat[°]',
- 'Altitude[m]',
- 'GeoPot[m]',
- 'MRI',
- 'RI',    
- 'DewPoint[C]',
- 'Virt. Temp[C]',
- 'Rs[m/min]',
- 'D[kg/m3]',
- 'Azimut[°]',
- 'Elevation[°]',
- 'Range[m]',
-]
-
-DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
-DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
-
-
-DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
-HOUR_FILES = {}
-for iDT, DT in enumerate(DTS):
-    morning_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
-    if len(possible_files)>0:
-        morning_file= possible_files[0]
-    afternoon_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
-    if len(possible_files)>0:
-        afternoon_file= possible_files[0]
-
-    if (morning_file is not None) and (afternoon_file is not None):
-        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
-                          'afternoon':[17.5,afternoon_file]}
-
-print(HOUR_FILES)
-
-# HOUR_FILES = \
-# {
-#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
-#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
-#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
-# }
-
-
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-def efrom_rh100_T(rh100,T):
-    return esat(T)*rh100/100.
-def qfrom_e_p(e,p):
-    return epsilon * e/(p - (1.-epsilon)*e)
-
-
-
-def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
-        print(balloon_file)
-        
-        xrin = balloon_file
-        air_balloon = pd.DataFrame()
-
-        air_balloon['t'] = xrin.tdry.values+273.15
-        air_balloon['p'] = xrin.pres.values*100.
-        
-        air_balloon['u'] = xrin.u_wind.values
-        air_balloon['v'] = xrin.v_wind.values
-        air_balloon['WSPD'] = xrin['wspd'].values
-        
-        print(xrin.rh.values.shape)
-        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
-        
-
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        # air_balloon_in = pd.read_fwf(balloon_file,
-        #                              widths=[14]*19,
-        #                              skiprows=9,
-        #                              skipfooter=15,
-        #                              decimal=',',
-        #                              header=None,
-        #                              names = columns,
-        #                              na_values='-----')
-    
-
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
-            'rho': lambda x: x.p /x.t / x.R ,
-        }
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        print('alt in xrin?:','alt' in xrin)
-        if 'alt' in xrin:
-            air_balloon['z'] = xrin.alt.values
-        else:
-            air_balloon['z'] = 0.
-            for irow,row in air_balloon.iloc[1:].iterrows():
-                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
-                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
-                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
-                        
-             
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-
-        # there are issues with the lower measurements in the HUMPPA campaign,
-        # for which a steady decrease of potential temperature is found, which
-        # is unrealistic.  Here I filter them away
-        ifirst = 0
-        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
-            ifirst = ifirst+1
-        print ('ifirst:',ifirst)
-        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            print(air_balloon.z.shape,air_balloon.thetav.shape,)
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='humppa',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn =pair['morning'][1]
-    print(humpafn)
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn = pair['afternoon'][1]
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-
-"""
-stations_for_iter = stations(path_exp)
-for STNID,station in stations_iterator(stations_for_iter):
-    records_current_station_index = \
-            (records_ini.index.get_level_values('STNID') == STNID)
-    file_current_station_mod = STNID
-
-    with \
-    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-        for (STNID,index),record_ini in records_iterator(records_ini):
-            c4gli_ini = get_record_yaml(file_station_ini, 
-                                        record_ini.index_start, 
-                                        record_ini.index_end,
-                                        mode='ini')
-            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-
-            record_mod = records_mod.loc[(STNID,index)]
-            c4gl_mod = get_record_yaml(file_station_mod, 
-                                        record_mod.index_start, 
-                                        record_mod.index_end,
-                                        mode='mod')
-            record_afternoon = records_afternoon.loc[(STNID,index)]
-            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-                                        record_afternoon.index_start, 
-                                        record_afternoon.index_end,
-                                        mode='ini')
-"""
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/examples/setup_soundings/setup_humppa.py b/examples/setup_soundings/setup_humppa.py
deleted file mode 100644
index ff37628..0000000
--- a/examples/setup_soundings/setup_humppa.py
+++ /dev/null
@@ -1,732 +0,0 @@
-# -*- coding: utf-8 -*-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import Pysolar
-import sys
-import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-
-globaldata = data_global()
-globaldata.load_datasets(recalc=0)
-
-Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-epsilon = Rd/Rv # or mv/md
-
-
-def replace_iter(iterable, search, replace):
-    for value in iterable:
-        value.replace(search, replace)
-        yield value
-
-from class4gl import blh,class4gl_input
-
-# definition of the humpa station
-current_station = pd.Series({ "latitude"  : 61.8448,
-                  "longitude" : 24.2882,
-                  "name" : "the HUMMPA experiment"
-                })
-current_station.name = 90000
-
-# we define the columns ourselves because it is a mess in the file itself.
-columns =\
-['Time[min:sec]',
- 'P[hPa]',
- 'T[C]',
- 'U[%]',
- 'Wsp[m/s]',
- 'Wdir[Grd]',
- 'Lon[°]',
- 'Lat[°]',
- 'Altitude[m]',
- 'GeoPot[m]',
- 'MRI',
- 'RI',    
- 'DewPoint[C]',
- 'Virt. Temp[C]',
- 'Rs[m/min]',
- 'D[kg/m3]',
- 'Azimut[°]',
- 'Elevation[°]',
- 'Range[m]',
-]
-
-
-HOUR_FILES = \
-{ dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
-  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[18,'humppa_071310_1800.txt']},
-  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
-  dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
-  dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
-  dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
-  dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
-  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[21,'humppa_071910_2100.txt']},
-#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
-  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
-  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[18,'humppa_072210_1800.txt']},
- # something is wrong with ths profile
- # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
-  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
-  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
-  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
-  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
-  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
-  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[18,'humppa_072910_1800.txt']},
-  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
-  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[15,'humppa_073110_1500.txt']},
-  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
-  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[18,'humppa_080210_1800.txt']},
-  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
-  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
-  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
-  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
-  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
-  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
-  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[18,'humppa_081010_1800.txt']},
-}
-
-
-
-
-
-
-#only include the following timeseries in the model output
-timeseries_only = \
-['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
- 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
- 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
- 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
- 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
-        #balloon_conv = replace_iter(balloon_file,"°","deg")
-        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
-        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
-        air_balloon_in = pd.read_fwf(balloon_file,
-                                     widths=[14]*19,
-                                     skiprows=9,
-                                     skipfooter=15,
-                                     decimal=',',
-                                     header=None,
-                                     names = columns,
-                                     na_values='-----')
-    
-        rowmatches = {
-            't':      lambda x: x['T[C]']+273.15,
-            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
-            'p':      lambda x: x['P[hPa]']*100.,
-            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
-            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
-            'z':      lambda x: x['Altitude[m]'],
-            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
-        }
-        
-        air_balloon = pd.DataFrame()
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon_in)
-        
-        rowmatches = {
-            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
-            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
-            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
-        }
-        
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
-        
-        dpars = {}
-        dpars['longitude']  = current_station['longitude']
-        dpars['latitude']  = current_station['latitude'] 
-        
-        dpars['STNID'] = current_station.name
-        
-
-        # there are issues with the lower measurements in the HUMPPA campaign,
-        # for which a steady decrease of potential temperature is found, which
-        # is unrealistic.  Here I filter them away
-        ifirst = 0
-        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
-            ifirst = ifirst+1
-        print ('ifirst:',ifirst)
-        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
-        
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        valid_indices = air_balloon.index[is_valid].values
-        
-        air_ap_mode='b'
-        
-        if len(valid_indices) > 0:
-            dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-        
-        
-        
-        if ~np.isnan(dpars['h']):
-            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-        else:
-            dpars['Ps'] = np.nan
-        
-        if ~np.isnan(dpars['h']):
-        
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-        
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-
-
-
-        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        jump = air_ap_head.iloc[0] * np.nan
-        
-        if air_ap_tail.shape[0] > 1:
-        
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # only select samples monotonically increasing with height
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        for ibottom in range(1,len(air_ap_tail_orig)):
-            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-
-        # # make theta increase strong enough to avoid numerical
-        # # instability
-        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        # air_ap_tail = pd.DataFrame()
-        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        # theta_low = air_ap_head['theta'].iloc[2]
-        # z_low = air_ap_head['z'].iloc[2]
-        # ibottom = 0
-        # for itop in range(0,len(air_ap_tail_orig)):
-        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-        #     if ((theta_mean > (theta_low+0.2) ) and \
-        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
-
-        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-        #         ibottom = itop+1
-        #         theta_low = air_ap_tail.theta.iloc[-1]
-        #         z_low =     air_ap_tail.z.iloc[-1]
-        #     # elif  (itop > len(air_ap_tail_orig)-10):
-        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        # 
-        # air_ap = \
-        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-        
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-        
-        
-        dpars['lat'] = dpars['latitude']
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        
-        
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        
-        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-        dpars['sw_lit'] = False
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-        
-                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
-        # 
-                for column,decimal in decimals.items():
-                    air_balloon[column] = air_balloon[column].round(decimal)
-                    air_ap[column] = air_ap[column].round(decimal)
-        
-        updateglobal = False
-        if c4gli is None:
-            c4gli = class4gl_input()
-            updateglobal = True
-        
-        print('updating...')
-        print(column)
-        c4gli.update(source='humppa',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-        if updateglobal:
-            c4gli.get_global_input(globaldata)
-
-        # if profile_ini:
-        #     c4gli.runtime = 10 * 3600
-
-        c4gli.dump(file_sounding)
-        
-        # if profile_ini:
-        #     c4gl = class4gl(c4gli)
-        #     c4gl.run()
-        #     c4gl.dump(file_model,\
-        #               include_input=True,\
-        #               timeseries_only=timeseries_only)
-        #     
-        #     # This will cash the observations and model tables per station for
-        #     # the interface
-        # 
-        # if profile_ini:
-        #     profile_ini=False
-        # else:
-        #     profile_ini=True
-        return c4gli
-
-
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
-    print(humpafn)
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
-    balloon_file = open(humpafn,'r',encoding='latin-1')
-
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
- 
-
-# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-# for date,pair  in HOUR_FILES.items(): 
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
-#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
-# file_morning.close()
-# 
-# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
-# for hour in [18]:
-#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
-#     balloon_file = open(humpafn,'r',encoding='latin-1')
-# 
-#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
-# file_afternoon.close()
-
-
-
-# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
-# 
-# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
-
-
-records_morning = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
-                                           )
-
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
-
-# stations_for_iter = stations(path_exp)
-# for STNID,station in stations_iterator(stations_for_iter):
-#     records_current_station_index = \
-#             (records_ini.index.get_level_values('STNID') == STNID)
-#     file_current_station_mod = STNID
-# 
-#     with \
-#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-#         for (STNID,index),record_ini in records_iterator(records_ini):
-#             c4gli_ini = get_record_yaml(file_station_ini, 
-#                                         record_ini.index_start, 
-#                                         record_ini.index_end,
-#                                         mode='ini')
-#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-# 
-#             record_mod = records_mod.loc[(STNID,index)]
-#             c4gl_mod = get_record_yaml(file_station_mod, 
-#                                         record_mod.index_start, 
-#                                         record_mod.index_end,
-#                                         mode='mod')
-#             record_afternoon = records_afternoon.loc[(STNID,index)]
-#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-#                                         record_afternoon.index_start, 
-#                                         record_afternoon.index_end,
-#                                         mode='ini')
-
-
-
-# # select the samples of the afternoon list that correspond to the timing of the
-# # morning list
-# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
-# records_afternoon.index = recods_morning.index
-# 
-# 
-# # create intersectino index
-# index_morning = pd.Index(records_morning.ldatetime.to_date())
-# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
-# 
-# for record_morning in records_morning.iterrows():
-#     
-#     c4gl = class4gl(c4gli)
-#     c4gl.run()
-#     c4gl.dump(c4glfile,\
-#               include_input=True,\
-#               timeseries_only=timeseries_only)
-# 
-# # This will cash the observations and model tables per station for
-# # the interface
-# 
-# records_ini = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=0,\
-#                                    by=2,\
-#                                    subset='ini',
-#                                    refetch_records=True,
-#                                    )
-# records_mod = get_records(pd.DataFrame([current_station]),\
-#                                    path_mod,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='mod',
-#                                    refetch_records=True,
-#                                    )
-# records_eval = get_records(pd.DataFrame([current_station]),\
-#                                    path_obs,\
-#                                    start=1,\
-#                                    by=2,\
-#                                    subset='eval',
-#                                    refetch_records=True,
-#                                    )
-# 
-# 
-# # mod_scores = pd.DataFrame(index=mod_records.index)
-# # for (STNID,index), current_record_mod in mod_records.iterrows():
-# #     print(STNID,index)
-# #     current_station = STN
-# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
-# #     current_record_obs = obs_records.loc[(STNID,index)]
-# # 
-# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
-# #                                           current_station,\
-# #                                           current_record_mod,\
-# #                                          )
-# # 
-# #     record_yaml_obs = \
-# #             get_record_yaml_obs(odirexperiments[keyEXP],\
-# #                                 current_station,\
-# #                                 current_record_obs,\
-# #                                 suffix='.yaml')
-# # 
-# #     record_yaml_obs_afternoon = \
-# #             get_record_yaml_obs(odir,\
-# #                                 current_station,\
-# #                                 current_record_obs_afternoon,\
-# #                                 suffix='_afternoon.yaml')
-# # 
-# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
-# #                    record_yaml_mod.h])
-# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
-# #     
-# # 
-# #     for height,hvalue in HEIGHTS.items():
-# # 
-# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
-# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
-# #         try:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
-# #                 rmse(\
-# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
-# #                     np.interp(\
-# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
-# #                         record_yaml_mod.air_ap.z[lt_mod],\
-# #                         record_yaml_mod.air_ap.theta[lt_mod]\
-# #                     ))
-# #         except ValueError:
-# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
-# #     # # we calculate these things in the interface itself
-# #     # for key in ['q','theta','h']:
-# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_mod.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# #     #     # the actual time of the initial and evaluation sounding can be 
-# #     #     # different, but we consider this as a measurement error for
-# #     #     # the starting and end time of the simulation.
-# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
-# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
-# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
-# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
-# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
-# # 
-# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
-# #         
-# #                 
-# #                 
-# # # for EXP,c4glfile in c4glfiles.items():
-# # #     c4glfile.close()            
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# # 
-# #     
-# #     # {'Time[min:sec]': None 
-# #     #  'P[hPa]': None, 
-# #     #  'T[C]': None, 
-# #     #  'U[%]': None, 
-# #     #  'Wsp[m/s]': None, 
-# #     #  'Wdir[Grd]': None,
-# #     #  'Lon[°]', 
-# #     #  'Lat[°]', 
-# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
-# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
-# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
-# #     # }
-# #     # 
-# #     # #pivotrows =
-# #     # #{
-# # 
-# # 
-# # 
diff --git a/examples/setup_soundings/trash/setup_global_old.py b/examples/setup_soundings/trash/setup_global_old.py
deleted file mode 100644
index d812684..0000000
--- a/examples/setup_soundings/trash/setup_global_old.py
+++ /dev/null
@@ -1,284 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thursday, March 29, 11:30 AM
-
-@author: Hendrik Wouters
-
-The dry-2-dry global radio sounding experiment.
-
-usage:
-    python setup_global.py 
-    where  is an integer indicating the row index of the station list
-    under odir+'/'+fn_stations (see below)
-
-this scripts should be called from the pbs script setup_global.pbs
-
-
-
-dependencies:
-    - pandas
-    - class4gl
-    - data_soundings
-
-
-"""
-
-""" import libraries """
-import pandas as pd
-import sys
-#import copy as cp
-import numpy as np
-from sklearn.metrics import mean_squared_error
-import logging
-import datetime as dt
-import os
-import math
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-
-
-#calculate the root mean square error
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from data_soundings import wyoming
-#from data_global import data_global
-
-# iniitialize global data
-globaldata = data_global()
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-# read the list of stations with valid ground data (list generated with
-# get_valid_stations.py)
-idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-
-df_stations = pd.read_csv(fn_stations)
-
-
-STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = 100
-BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-
-
-iPROC = int(sys.argv[1])
-
-
-for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
-# for iSTN,STN in STNlist[5:]:  
-    
-    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
-    
-
-    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
-    #                   for EXP in experiments.keys()])
-        
-    with open(fnout,'w') as fileout, \
-         open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
-        wy_strm.set_STNM(int(STN['ID']))
-
-        # we consider all soundings after 1981
-        wy_strm.find_first(year=1981)
-        #wy_strm.find(dt.datetime(2004,10,19,6))
-        
-        c4gli = class4gl_input(debug_level=logging.INFO)
-        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
-        # so we continue as long as we can find a new sounding
-        while wy_strm.current is not None:
-            
-            c4gli.clear()
-            c4gli.get_profile_wyoming(wy_strm)
-            #print(STN['ID'],c4gli.pars.datetime)
-            #c4gli.get_global_input(globaldata)
-
-            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
-
-            logic = dict()
-            logic['morning'] =  (c4gli.pars.ldatetime.hour < 12.)
-            logic['daylight'] = \
-                ((c4gli.pars.ldatetime_daylight - 
-                  c4gli.pars.ldatetime).total_seconds()/3600. <= 5.)
-            
-            logic['springsummer'] = (c4gli.pars.theta > 278.)
-            
-            # we take 3000 because previous analysis (ie., HUMPPA) has
-            # focussed towards such altitude
-            le3000 = (c4gli.air_balloon.z <= 3000.)
-            logic['10measurements'] = (np.sum(le3000) >= 10) 
-
-            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
-
-            try:
-                logic['mlerrlow'] = (\
-                        (len(np.where(leh)[0]) > 0) and \
-                        # in cases where humidity is not defined, the mixed-layer
-                        # values get corr
-                        (not np.isnan(c4gli.pars.theta)) and \
-                        (rmse(c4gli.air_balloon.theta[leh] , \
-                              c4gli.pars.theta,filternan_actual=True) < 1.)\
-                              )
-    
-            except:
-                logic['mlerrlow'] = False
-                print('rmse probably failed')
-
-            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
-            
-            print('logic:', logic)
-            # the result
-            morning_ok = np.mean(list(logic.values()))
-            print(morning_ok,c4gli.pars.ldatetime)
-            
-            # the next sounding will be used either for an afternoon sounding
-            # or for the morning sounding of the next day.
-            wy_strm.find_next()
-
-            # If the morning is ok, then we try to find a decent afternoon
-            # sounding
-            if morning_ok == 1.:
-                # we get the current date
-                current_date = dt.date(c4gli.pars.ldatetime.year, \
-                                       c4gli.pars.ldatetime.month, \
-                                       c4gli.pars.ldatetime.day)
-                c4gli_afternoon.clear()
-                c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                if wy_strm.current is not None:
-                    current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
-                else:
-                    # a dummy date: this will be ignored anyway
-                    current_date_afternoon = dt.date(1900,1,1)
-
-                # we will dump the latest afternoon sounding that fits the
-                # minimum criteria specified by logic_afternoon
-                c4gli_afternoon_for_dump = None
-                while ((current_date_afternoon == current_date) and \
-                       (wy_strm.current is not None)):
-                    logic_afternoon =dict()
-
-                    logic_afternoon['afternoon'] = \
-                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                    logic_afternoon['daylight'] = \
-                      ((c4gli_afternoon.pars.ldatetime - \
-                        c4gli_afternoon.pars.ldatetime_daylight \
-                       ).total_seconds()/3600. <= 2.)
-
-
-                    le3000_afternoon = \
-                        (c4gli_afternoon.air_balloon.z <= 3000.)
-                    logic_afternoon['5measurements'] = \
-                        (np.sum(le3000_afternoon) >= 5) 
-
-                    # we only store the last afternoon sounding that fits these
-                    # minimum criteria
-
-                    afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                    print('logic_afternoon: ',logic_afternoon)
-                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                    if afternoon_ok == 1.:
-                        # # doesn't work :(
-                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                        
-                        # so we just create a new one from the same wyoming profile
-                        c4gli_afternoon_for_dump = class4gl_input()
-                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                    wy_strm.find_next()
-                    c4gli_afternoon.clear()
-                    c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                    if wy_strm.current is not None:
-                        current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
-                    else:
-                        # a dummy date: this will be ignored anyway
-                        current_date_afternoon = dt.date(1900,1,1)
-
-                    # Only in the case we have a good pair of soundings, we
-                    # dump them to disk
-                if c4gli_afternoon_for_dump is not None:
-                    c4gli.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                             c4gli.pars.datetime_daylight).total_seconds())})
-    
-    
-                    print('ALMOST...')
-                    if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
-                            
-        
-                        c4gli.get_global_input(globaldata)
-                        print('VERY CLOSE...')
-                        if c4gli.check_source_globaldata() and \
-                            (c4gli.check_source(source='wyoming',\
-                                               check_only_sections='pars')):
-                            c4gli.dump(fileout)
-                            
-                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                            
-                            
-                            # for keyEXP,dictEXP in experiments.items():
-                            #     
-                            #     c4gli.update(source=keyEXP,pars = dictEXP)
-                            #     c4gl = class4gl(c4gli)
-                            #     # c4gl.run()
-                            #     
-                            #     c4gl.dump(c4glfiles[key])
-                            
-                            print('HIT!!!')
-                
-                
-    # for c4glfile in c4glfiles:
-    #     c4glfile.close()            
-
diff --git a/interface_functions.py b/interface_functions.py
deleted file mode 100644
index 3e483f3..0000000
--- a/interface_functions.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-#from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-#'_afternoon.yaml'
-def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
-    filename = yaml_file.name
-    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-    #yaml_file = open(filename)
-
-    #print('going to next observation',filename)
-    yaml_file.seek(index_start)
-
-    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-
-    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-    filebuffer.write(buf)
-    filebuffer.close()
-    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-    
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-
-    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-    print(command)
-    os.system(command)
-    jsonstream = open(filename+'.buffer.json.'+str(index_start))
-    record_dict = json.load(jsonstream)
-    jsonstream.close()
-    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-
-
-    if mode =='mod':
-        modelout = class4gl()
-        modelout.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        return modelout
-    elif mode == 'ini':
-
- 
-        # datetimes are incorrectly converted to strings. We need to convert them
-        # again to datetimes
-        for key,value in record_dict['pars'].items():
-            # we don't want the key with columns that have none values
-            if value is not None: 
-                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
-               # elif (type(value) == str):
-                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-
-            if (value == 0.9e19) or (value == '.9e19'):
-                record_dict['pars'][key] = np.nan
-        for key in record_dict.keys():
-            #print(key)
-            if key in ['air_ap','air_balloon',]:
-                #NNprint('check')
-                for datakey,datavalue in record_dict[key].items():
-                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-
-        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        c4gli = class4gl_input()
-        print(c4gli.logger,'hello')
-        c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-        return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-
-class stations(object):
-    def __init__(self,path,suffix='ini',refetch_stations=False):
-
-        self.path = path
-
-        self.file = self.path+'/stations_list.csv'
-        if (os.path.isfile(self.file)) and (not refetch_stations):
-            self.table = pd.read_csv(self.file)
-        else:
-            self.table = self.get_stations(suffix=suffix)
-            self.table.to_csv(self.file)
-        
-        self.table = self.table.set_index('STNID')
-        #print(self.table)
-
-    def get_stations(self,suffix):
-        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
-        if len(stations_list_files) == 0:
-            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
-        stations_list_files.sort()
-        print(stations_list_files)
-        if len(stations_list_files) == 0:
-            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
-        stations_list = []
-        for stations_list_file in stations_list_files:
-            thisfile = open(stations_list_file,'r')
-            yamlgen = yaml.load_all(thisfile)
-            try:
-                first_record  = yamlgen.__next__()
-            except:
-                first_record = None
-            if first_record is not None:
-                stations_list.append({})
-                for column in ['STNID','latitude','longitude']:
-                    #print(first_record['pars'].keys())
-                    stations_list[-1][column] = first_record['pars'][column]
-                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
-            yamlgen.close()
-            thisfile.close()
-    
-        print(stations_list)
-        return pd.DataFrame(stations_list)
-
-class stations_iterator(object):
-    def __init__(self,stations):
-        self.stations = stations
-        self.ix = -1 
-    def __iter__(self):
-        return self
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.stations.table)) 
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_row(self,row):
-        self.ix = row
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_STNID(self,STNID):
-        self.ix = np.where((self.stations.table.index == STNID))[0][0]
-        print(self.ix)
-        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-    def close():
-        del(self.ix)
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.records))
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-# #'_afternoon.yaml'
-# def get_record_yaml(yaml_file,index_start,index_end):
-#     filename = yaml_file.name
-#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-#     #yaml_file = open(filename)
-# 
-#     #print('going to next observation',filename)
-#     yaml_file.seek(index_start)
-# 
-#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-# 
-#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-#     filebuffer.write(buf)
-#     filebuffer.close()
-#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-#     
-#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-# 
-#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-#     print(command)
-#     os.system(command)
-#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
-#     record_dict = json.load(jsonstream)
-#     jsonstream.close()
-#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-#  
-#     # datetimes are incorrectly converted to strings. We need to convert them
-#     # again to datetimes
-#     for key,value in record_dict['pars'].items():
-#         # we don't want the key with columns that have none values
-#         if value is not None: 
-#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
-#            # elif (type(value) == str):
-#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-#                 
-#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
-# 
-#         if (value == 0.9e19) or (value == '.9e19'):
-#             record_dict['pars'][key] = np.nan
-#     for key in record_dict.keys():
-#         print(key)
-#         if key in ['air_ap','air_balloon',]:
-#             print('check')
-#             for datakey,datavalue in record_dict[key].items():
-#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-# 
-#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-# 
-#     c4gli = class4gl_input()
-#     c4gli.load_yaml_dict(record_dict)
-#     return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
-
-    records = pd.DataFrame()
-    for STNID,station in stations.iterrows():
-        dictfnchunks = []
-        if getchunk is 'all':
-
-            # we try the old single-chunk filename format first (usually for
-            # original profile pairs)
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
-            if os.path.isfile(fn):
-                chunk = 0
-                dictfnchunks.append(dict(fn=fn,chunk=chunk))
-
-            # otherwise, we use the new multi-chunk filename format
-            else:
-                chunk = 0
-                end_of_chunks = False
-                while not end_of_chunks:
-                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(fn):
-                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
-                    else:
-                        end_of_chunks = True
-                    chunk += 1
-
-            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
-            # yamlfilenames = glob.glob(globyamlfilenames)
-            # yamlfilenames.sort()
-        else:
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
-            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
-            
-        if len(dictfnchunks) > 0:
-            for dictfnchunk in dictfnchunks:
-                yamlfilename = dictfnchunk['fn']
-                chunk = dictfnchunk['chunk']
-                print(chunk)
-
-                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
-                pklfilename = yamlfilename.replace('.yaml','.pkl')
-
-                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
-                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
-                generate_pkl = False
-                if not os.path.isfile(pklfilename): 
-                    print('pkl file does not exist. I generate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                elif not (os.path.getmtime(yamlfilename) <  \
-                    os.path.getmtime(pklfilename)):
-                    print('pkl file older than yaml file, so I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-
-                if refetch_records:
-                    print('refetch_records flag is True. I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(pklfilename)])
-                   # irecord = 0
-                else:
-                    with open(yamlfilename) as yaml_file:
-
-                        dictout = {}
-
-                        next_record_found = False
-                        end_of_file = False
-                        while (not next_record_found) and (not end_of_file):
-                            linebuffer = yaml_file.readline()
-                            next_record_found = (linebuffer == '---\n')
-                            end_of_file = (linebuffer == '')
-                        next_tell = yaml_file.tell()
-                        
-                        while not end_of_file:
-
-                            print(' next record:',next_tell)
-                            current_tell = next_tell
-                            next_record_found = False
-                            yaml_file.seek(current_tell)
-                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
-                            linebuffer = ''
-                            while ( (not next_record_found) and (not end_of_file)):
-                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
-                                linebuffer = yaml_file.readline()
-                                next_record_found = (linebuffer == '---\n')
-                                end_of_file = (linebuffer == '')
-                            filebuffer.close()
-                            
-                            next_tell = yaml_file.tell()
-                            index_start = current_tell
-                            index_end = next_tell
-
-                            
-                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                            print(command)
-                            
-                            os.system(command)
-                            #jsonoutput = subprocess.check_output(command,shell=True) 
-                            #print(jsonoutput)
-                            #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
-                            record = json.load(jsonstream)
-                            dictouttemp = {}
-                            for key,value in record['pars'].items():
-                                # we don't want the key with columns that have none values
-                                if value is not None: 
-                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
-                                   if (type(value) in regular_numeric_types):
-                                        dictouttemp[key] = value
-                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
-                            recordindex = record['index']
-                            dictouttemp['chunk'] = chunk
-                            dictouttemp['index_start'] = index_start
-                            dictouttemp['index_end'] = index_end
-                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
-                            for key,value in dictouttemp.items():
-                                if key not in dictout.keys():
-                                    dictout[key] = {}
-                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
-                            print(' obs record registered')
-                            jsonstream.close()
-                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
-                    records_station = pd.DataFrame.from_dict(dictout)
-                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+pklfilename+') for station '\
-                          +str(STNID))
-                    records_station.to_pickle(pklfilename)
-                    # else:
-                    #     os.system('rm '+pklfilename)
-                    records = pd.concat([records,records_station])
-    return records
-
-def stdrel(mod,obs,columns):
-    stdrel = pd.DataFrame(columns = columns)
-    for column in columns:
-        stdrel[column] = \
-                (mod.groupby('STNID')[column].transform('mean') -
-                 obs.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') + \
-                (mod[column] -
-                 mod.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') 
-    return stdrel
-
-def pct(obs,columns):
-    pct = pd.DataFrame(columns=columns)
-    for column in columns:
-        #print(column)
-        pct[column] = ""
-        pct[column] = obs[column].rank(pct=True)
-    return pct
-
-def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
-    stats = pd.DataFrame()
-    for key in keys: 
-        stats['d'+key+'dt'] = ""
-        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
-                              (obs_afternoon.ldatetime - \
-                               obs_morning.ldatetime).dt.seconds*3600.
-    return stats
-
diff --git a/lib/class4gl.py b/lib/class4gl.py
deleted file mode 100644
index 7baaa51..0000000
--- a/lib/class4gl.py
+++ /dev/null
@@ -1,1611 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-
-Created on Mon Jan 29 12:33:51 2018
-
-Module file for class4gl, which  extents the class-model to be able to take
-global air profiles as input. It exists of:
-
-CLASSES:
-    - an input object, namely class4gl_input. It includes:
-        - a function to read Wyoming sounding data from a yyoming stream object
-        - a function to read global data from a globaldata library object 
-    - the model object: class4gl
-    - ....    
-
-DEPENDENCIES:
-    - xarray
-    - numpy
-    - data_global
-    - Pysolar
-    - yaml
-
-@author: Hendrik Wouters
-
-"""
-
-
-
-""" Setup of envirnoment """
-
-# Standard modules of the stand class-boundary-layer model
-from model import model
-from model import model_output as class4gl_output
-from model import model_input
-from model import qsat
-#from data_soundings import wyoming 
-import Pysolar
-import yaml
-import logging
-import warnings
-import pytz
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-
-# Generic Python Packages
-import numpy as np
-import datetime as dt
-import pandas as pd
-import xarray as xr
-import io
-#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
-from data_global import data_global
-grav = 9.81
-
-# this is just a generic input object
-class generic_input(object):
-    def __init__(self):
-        self.init = True
-
-
-# all units from all variables in CLASS(4GL) should be defined here!
-units = {
-         'h':'m',
-         'theta':'K', 
-         'q':'kg/kg',
-         'cc': '-',
-         'cveg': '-',
-         'wg': 'm3 m-3',
-         'w2': 'm3 m-3',
-         #'wg': 'kg/kg',
-         'Tsoil': 'K',
-         'T2': 'K',
-         'z0m': 'm',
-         'alpha': '-',
-         'LAI': '-',
-         'dhdt':'m/h',
-         'dthetadt':'K/h',
-         'dqdt':'kg/kg/h',
-         'BR': '-',
-         'EF': '-',
-}
-
-class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
-
-    def __init__(self,set_pars_defaults=True,debug_level=None):
-
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        print('hello')
-        self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
-     
-        # # add ch to logger
-        # self.logger.addHandler(ch)
-
-        # """ end set up logger """
-
-
-
-        # these are the standard model input single-value parameters for class
-        self.pars = model_input()
-
-        # diagnostic parameters of the initial profile
-        self.diag = dict()
-
-        # In this variable, we keep track of the different parameters from where it originates from. 
-        self.sources = {}
-
-        if set_pars_defaults:
-            self.set_pars_defaults()
-
-    def set_pars_defaults(self):
-
-        """ 
-        Create empty model_input and set up case
-        """
-        defaults = dict( 
-        dt         = 60.    , # time step [s] 
-        runtime    = 6*3600 ,  # total run time [s]
-        
-        # mixed-layer input
-        sw_ml      = True   ,  # mixed-layer model switch
-        sw_shearwe = False  ,  # shear growth mixed-layer switch
-        sw_fixft   = False  ,  # Fix the free-troposphere switch
-        h          = 200.   ,  # initial ABL height [m]
-        Ps         = 101300.,  # surface pressure [Pa]
-        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
-        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
-        
-        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
-        dtheta     = 1.     ,  # initial temperature jump at h [K]
-        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
-        advtheta   = 0.     ,  # advection of heat [K s-1]
-        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
-        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
-        
-        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
-        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
-        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
-        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
-        
-        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
-        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
-        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
-        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
-        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
-        sw_wind    = True  ,  # prognostic wind switch
-        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
-        du         = 0.     ,  # initial u-wind jump at h [m s-1]
-        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
-        advu       = 0.     ,  # advection of u-wind [m s-2]
-        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
-        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
-        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
-        advv       = 0.     ,  # advection of v-wind [m s-2]
-        sw_sl      = True   , # surface layer switch
-        ustar      = 0.3    ,  # surface friction velocity [m s-1]
-        z0m        = 0.02   ,  # roughness length for momentum [m]
-        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
-        sw_rad     = True   , # radiation switch
-        lat        = 51.97  ,  # latitude [deg]
-        lon        = -4.93  ,  # longitude [deg]
-        doy        = 268.   ,  # day of the year [-]
-        tstart     = 6.8    ,  # time of the day [h UTC]
-        cc         = 0.0    ,  # cloud cover fraction [-]
-        Q          = 400.   ,  # net radiation [W m-2] 
-        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
-        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
-        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
-        cveg       = 0.85   ,  # vegetation fraction [-]
-        Tsoil      = 295.   ,  # temperature top soil layer [K]
-        Ts         = 295.   ,    # initial surface temperature [K]
-        T2         = 296.   ,  # temperature deeper soil layer [K]
-        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
-        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
-        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
-        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
-        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
-        wfc        = 0.323  ,  # volumetric water content field capacity [-]
-        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
-        C1sat      = 0.132  ,  
-        C2ref      = 1.8    ,
-        LAI        = 2.     ,  # leaf area index [-]
-        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
-        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
-        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
-        alpha      = 0.25   ,  # surface albedo [-]
-        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
-        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
-        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
-        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
-        sw_cu      = False  ,  # Cumulus parameterization switch
-        dz_h       = 150.   ,  # Transition layer thickness [m]
-        cala       = None   ,  # soil heat conductivity [W/(K*m)]
-        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
-        sw_ls      = True   ,
-        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
-        sw_lit     = False,
-        )
-        pars = model_input()
-        for key in defaults:
-            pars.__dict__[key] = defaults[key]
-        
-        self.update(source='defaults',pars=pars)
-        
-    def clear(self):
-        """ this procudure clears the class4gl_input """
-
-        for key in list(self.__dict__.keys()):
-            del(self.__dict__[key])
-        self.__init__()
-
-    def dump(self,file):
-        """ this procedure dumps the class4gl_input object into a yaml file
-            
-            Input: 
-                - self.__dict__ (internal): the dictionary from which we read 
-            Output:
-                - file: All the parameters in self.__init__() are written to
-                the yaml file, including pars, air_ap, sources etc.
-        """
-        file.write('---\n')
-        index = file.tell()
-        file.write('# CLASS4GL input; format version: 0.1\n')
-
-        # write out the position of the current record
-        yaml.dump({'index':index}, file, default_flow_style=False)
-
-        # we do not include the none values
-        for key,data in self.__dict__.items():
-            #if ((type(data) == model_input) or (type(class4gl_input):
-            if key == 'pars':
-
-                pars = {'pars' : self.__dict__['pars'].__dict__}
-                parsout = {}
-                for key in pars.keys():
-                    if pars[key] is not None:
-                        parsout[key] = pars[key]
-
-                yaml.dump(parsout, file, default_flow_style=False)
-            elif type(data) == dict:
-                if key == 'sources':
-                    # in case of sources, we want to have a
-                    # condensed list format as well, so we leave out
-                    # 'default_flow_style=False'
-                    yaml.dump({key : data}, file)
-                else: 
-                    yaml.dump({key : data}, file,
-                              default_flow_style=False)
-            elif type(data) == pd.DataFrame:
-                # in case of dataframes (for profiles), we want to have a
-                # condensed list format as well, so we leave out
-                # 'default_flow_style=False'
-                yaml.dump({key: data.to_dict(orient='list')},file)
-
-                # # these are trials to get it into a more human-readable
-                # fixed-width format, but it is too complex
-                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
-                #file.write(stream)
-                
-                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
-                #file.write(key+': !!str |\n')
-                #file.write(str(data)+'\n')
-       
-    def load_yaml_dict(self,yaml_dict,reset=True):
-        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
-            
-            Input: 
-                - yaml_dict: the dictionary from which we read 
-                - reset: reset data before reading        
-            Output:
-                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
-        """
-        
-        if reset:
-            for key in list(self.__dict__.keys()):
-                del(self.__dict__[key])
-            self.__init__()
-
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                self.__dict__[key] = model_input()
-                self.__dict__[key].__dict__ = data
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            elif key == 'sources':
-                self.__dict__[key] = data
-            elif key == 'diag':
-                self.__dict__[key] = data
-            else: 
-                warnings.warn("Key '"+key+"' may not be implemented.")
-                self.__dict__[key] = data
-
-    def update(self,source,**kwargs):
-        """ this procedure is to make updates of input parameters and tracking
-        of their source more convenient. It implements the assignment of
-        parameter source/sensitivity experiment IDs ('eg.,
-        'defaults', 'sounding balloon', any satellite information, climate
-        models, sensitivity tests etc.). These are all stored in a convenient
-        way with as class4gl_input.sources.  This way, the user can always consult with
-        from where parameters data originates from.  
-        
-        Input:
-            - source:    name of the underlying dataset
-            - **kwargs: a dictionary of data input, for which the key values
-            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
-            the values is a again a dictionary/dataframe of datakeys/columns
-            ('wg','PRES','datetime', ...) and datavalues (either single values,
-            profiles ...), eg., 
-
-                pars = {'wg': 0.007  , 'w2', 0.005}
-                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
-                                     300.,...]}
-            
-        Output:
-            - self.__dict__[datatype] : object to which the parameters are
-                                        assigned. They can be consulted with
-                                        self.pars, self.profiles, etc.
-                                        
-            - self.sources[source] : It supplements the overview overview of
-                                     data sources can be consulted with
-                                     self.sources. The structure is as follows:
-                                     as:
-                self.sources = { 
-                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
-                'GLEAM' :  ['pars:wg','pars:w2', ...],
-                 ...
-                }
-        
-        """
-
-        #print(source,kwargs)
-
-        for key,data in kwargs.items():
-
-            #print(key)
-            # if the key is not in class4gl_input object, then just add it. In
-            # that case, the update procedures below will just overwrite it 
-            if key not in self.__dict__:
-                self.__dict__[key] = data
-
-
-            
-
-            #... we do an additional check to see whether there is a type
-            # match. I not then raise a key error
-            if (type(data) != type(self.__dict__[key]) \
-                # we allow dict input for model_input pars
-                and not ((key == 'pars') and (type(data) == dict) and \
-                (type(self.__dict__[key]) == model_input))):
-
-                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
-
-
-            # This variable keeps track of the added data that is supplemented
-            # by the current source. We add this to class4gl_input.sources
-            datakeys = []
-
-            #... and we update the class4gl_input data, and this depends on the
-            # data type
-
-            if type(self.__dict__[key]) == pd.DataFrame:
-                # If the data type is a dataframe, then we update the columns
-                for column in list(data.columns):
-                    #print(column)
-                    self.__dict__[key][column] = data[column]
-                    datakeys.append(column)
-                    
-
-            elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
-                # dictionary of parameters
-                if type(data) == model_input:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data.__dict__}
-                    datakeys = list(data.__dict__.keys())
-                elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
-                    datakeys = list(data.keys())
-                else:
-                    raise TypeError('input key '+key+' is not of the same type\
-                                    as the one in the class4gl_object')
-
-            elif type(self.__dict__[key]) == dict:
-                # if the data type is a dictionary, we update the
-                # dictionary 
-                self.__dict__[key] = {self.__dict__[key] , data}
-                datakeys = list(data.keys())
-
-
-            # if source entry is not existing yet, we add it
-            if source not in self.sources.keys():
-                self.sources[source] = []
-
-
-            # self.logger.debug('updating section "'+\
-            #                  key+' ('+' '.join(datakeys)+')'\
-            #                  '" from source \
-            #                  "'+source+'"')
-
-            # Update the source dictionary: add the provided data keys to the
-            # specified source list
-            for datakey in datakeys:
-                # At first, remove the occurences of the keys in the other
-                # source lists
-                for sourcekey,sourcelist in self.sources.items():
-                    if key+':'+datakey in sourcelist:
-                        self.sources[sourcekey].remove(key+':'+datakey)
-                # Afterwards, add it to the current source list
-                self.sources[source].append(key+':'+datakey)
-
-
-        # # in case the datatype is a class4gl_input_pars, we update its keys
-        # # according to **kwargs dictionary
-        # if type(self.__dict__[datatype]) == class4gl_input_pars:
-        #     # add the data parameters to the datatype object dictionary of the
-        #     # datatype
-        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
-        #                                        **kwargs}
-        # # in case, the datatype reflects a dataframe, we update the columns according
-        # # to the *args list
-        # elif type(self.__dict__[datatype]) == pd.DataFrame:
-        #     for dataframe in args:
-        #         for column in list(dataframe.columns):
-        #             self.__dict__[datatype][column] = dataframe[column]
-        
-
-    def get_profile(self,IOBJ, *args, **argv):
-        # if type(IOBJ) == wyoming:
-        self.get_profile_wyoming(IOBJ,*args,**argv)
-        # else:
-        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
-        
-    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
-        """ 
-            Purpose: 
-                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
-
-            Input:
-                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
-                function will take the profile at the stream's current
-                position. 
-                2. air_ap_mode: which air profile do we take? 
-                    - b : best
-                    - l : according to lower limit for the mixed-layer height
-                            estimate
-                    - u : according to upper limit for the mixed-layer height
-                            estimate
-
-
-            Output:
-                1. all single-value parameters are stored in the
-                   class4gl_input.pars object
-                2. the souding profiles are stored in the in the
-                   class4gl_input.air_balloon dataframe
-                3. modified sounding profiles for which the mixed layer height
-                   is fitted
-                4. ...
-
-        """
-
-
-        # Raise an error in case the input stream is not the correct object
-        # if type(wy_strm) is not wyoming:
-        #    raise TypeError('Not a wyoming type input stream')
-
-        # Let's tell the class_input object that it is a Wyoming fit type
-        self.air_ap_type = 'wyoming'
-        # ... and which mode of fitting we apply
-        self.air_ap_mode = air_ap_mode
-
-        """ Temporary variables used for output """
-        # single value parameters derived from the sounding profile
-        dpars = dict()
-        # profile values
-        air_balloon = pd.DataFrame()
-        # fitted profile values
-        air_ap = pd.DataFrame()
-        
-        string = wy_strm.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = wy_strm.current.find_next('pre').find_next('pre').text
-        
-        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
-        dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
-               }
-        
-        # we get weird output when it's a numpy Timestamp, so we convert it to
-        # pd.datetime type
-
-        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
-        dpars['STNID'] = dpars['Station number']
-
-        # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
-        # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
-                              / \
-                             (air_balloon.MIXR/1000.+1.)
-        # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-        
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
-
-        
-
-        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-
-        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
-
-
-        # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        #is_valid = (air_balloon.z >= 0)
-        # # this is an alternative pipe/numpy method
-        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
-
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
-
-        if len(valid_indices) > 0:
-            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            
-            # the final mixed-layer height that will be used by class. We round it
-            # to 1 decimal so that we get a clean yaml output format
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
-            dpars['Ps'] = np.nan
-
-
-
-
-        if ~np.isnan(dpars['h']):
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u 
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-            
-
-
-
-        # First 3 data points of the mixed-layer fit. We create a empty head
-        # first
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-        
-        #calculate mixed-layer jump ( this should be larger than 0.1)
-        
-        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
-        
-        # make a row object for defining the jump
-        jump = air_ap_head.iloc[0] * np.nan
-            
-        if air_ap_tail.shape[0] > 1:
-
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = dpars['theta']
-        z_low =     dpars['h']
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                (z_mean > (z_low+10.)) and \
-                (theta_mean > (theta_low+0.2) ) and \
-                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-
-
-
-
-
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        #print(air_ap['PRES'].iloc[0])
-
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-
-        
-        dpars['lat'] = dpars['Station latitude']
-        dpars['latitude'] = dpars['lat']
-        
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        dpars['longitude'] = dpars['Station longitude']
-        
-        dpars['ldatetime'] = dpars['datetime'] \
-                            + \
-                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-
-        # # we make a pars object that is similar to the destination object
-        # pars = model_input()
-        # for key,value in dpars.items():
-        #     pars.__dict__[key] = value
-
-
-        # we round the columns to a specified decimal, so that we get a clean
-        # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
-# 
-        for column,decimal in decimals.items():
-            air_balloon[column] = air_balloon[column].round(decimal)
-            air_ap[column] = air_ap[column].round(decimal)
-
-        self.update(source='wyoming',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-
-        
-    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
-    
-        """
-        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
-                 according to the position (lat lon) and the class datetime and timespan
-                 globaldata should be a globaldata multifile object
-        
-        Input: 
-            - globaldata: this is the library object
-            - only_keys: only extract specified keys
-            - exclude_keys: do not inherit specified keys
-        """
-        classdatetime      = np.datetime64(self.pars.datetime_daylight)
-        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
-                                           + \
-                                           dt.timedelta(seconds=self.pars.runtime)\
-                                          )
-
-
-        # # list of variables that we get from global ground data
-        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
-        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
-        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
-        #                 'texture', 'itex', 'isoil', 'BR',
-        #                 'b', 'cveg',
-        #                 'C1sat', 
-        #                 'C2ref', 'p', 'a',
-        #                 ] #globaldata.datasets.keys():
-
-        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
-        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
-
-
-        if type(globaldata) is not data_global:
-            raise TypeError("Wrong type of input library") 
-
-        # by default, we get all dataset keys
-        keys = list(globaldata.datasets.keys())
-
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
-
-        # # In case there is surface pressure, we also calculate the half-level
-        # # and full-level pressure fields
-        # if ('sp' in keys):
-        #     keys.append('pfull')
-        #     keys.append('phalf')
-
-        # If specified, we only take the keys that are in only_keys
-        if only_keys is not None:
-            for key in keys:
-                if key not in only_keys:
-                    keys.remove(key)
-                
-        # If specified, we take out keys that are in exclude keys
-        if exclude_keys is not None:
-            for key in keys:
-                if key in exclude_keys:
-                    keys.remove(key)
-
-        # we set everything to nan first in the pars section (non-profile parameters
-        # without lev argument), so that we can check afterwards whether the
-        # data is well-fetched or not.
-        for key in keys:
-            if not ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None) and \
-                ('lev' in globaldata.datasets[key].page[key].dims)):
-                self.update(source='globaldata',pars={key:np.nan})
-            # # we do not check profile input for now. We assume it is
-            # # available
-            #else:
-            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
-
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
-
-        for key in keys:
-            # If we find it, then we obtain the variables
-            if ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None)):
-
-                # check first whether the dataset has a height coordinate (3d space)
-                if 'lev' in globaldata.datasets[key].page[key].dims:
-
-                    # first, we browse to the correct file that has the current time
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-                        globaldata.datasets[key].browse_page(time=classdatetime)
-
-                    
-                    if (globaldata.datasets[key].page is not None):
-                        # find longitude and latitude coordinates
-                        ilats = (np.abs(globaldata.datasets[key].page.lat -
-                                        self.pars.latitude) < 0.5)
-                        ilons = (np.abs(globaldata.datasets[key].page.lon -
-                                        self.pars.longitude) < 0.5)
-                        
-                        # if we have a time dimension, then we look up the required timesteps during the class simulation
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
-
-                            # In case we didn't find any correct time, we take the
-                            # closest one.
-                            if np.sum(itimes) == 0.:
-
-
-                                classdatetimemean = \
-                                    np.datetime64(self.pars.datetime_daylight + \
-                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
-                                                ))
-
-                                dstimes = globaldata.datasets[key].page.time
-                                time = dstimes.sel(time=classdatetimemean,method='nearest')
-                                itimes = (globaldata.datasets[key].page.time ==
-                                          time)
-                                
-                        else:
-                            # we don't have a time coordinate so it doesn't matter
-                            # what itimes is
-                            itimes = 0
-
-                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
-
-                        # over which dimensions we take a mean:
-                        dims = globaldata.datasets[key].page[key].dims
-                        namesmean = list(dims)
-                        namesmean.remove('lev')
-                        idxmean = [dims.index(namemean) for namemean in namesmean]
-                        
-                        value = \
-                        globaldata.datasets[key].page[key].isel(time=itimes,
-                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
-
-                        # Ideally, source should be equal to the datakey of globaldata.library 
-                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
-                        #  but therefore the globaldata class requires a revision to make this work
-                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
-
-                else:
-                    # this procedure is for reading the ground fields (2d space). 
-                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-    
-                       # first, we browse to the correct file
-                       #print(key)
-                       globaldata.datasets[key].browse_page(time=classdatetime)
-    
-                    if globaldata.datasets[key].page is not None:
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - self.pars.latitude))
-                        ilat = np.where((DIST) == np.min(DIST))[0][0]
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - self.pars.longitude))
-                        ilon = np.where((DIST) == np.min(DIST))[0][0]
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - (self.pars.latitude + 0.5)))
-                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmax = ilat
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - (self.pars.longitude  + 0.5)))
-                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmax = ilon
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lat.values\
-                                - (self.pars.latitude - 0.5)))
-                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmin = ilat
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lon.values\
-                                - (self.pars.longitude  - 0.5)))
-                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmin = ilon        
-                        
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
-                        else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
-                            
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                            
-                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
-                            
-                            classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
-                                                             dt.timedelta(seconds=self.pars.runtime)\
-                                                            ) 
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
-                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
-                                idatetimeend -= 1
-                            idatetime = np.min((idatetime,idatetimeend))
-                            #for gleam, we take the previous day values
-                            if key in ['wg', 'w2']:
-                                idatetime = idatetime - 1
-                                idatetimeend = idatetimeend - 1
-
-                            # in case of soil temperature, we take the exact
-                            # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
-                                idatetimeend = idatetime
-                            
-                            idts = range(idatetime,idatetimeend+1)
-                            
-                            count = 0
-                            self.__dict__[key] = 0.
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    for iidts in idts:
-                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
-                                        count += 1
-                            value = value/count
-                            self.update(source='globaldata',pars={key:value.item()})
-                                
-                        else:
-                                
-                            count = 0
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
-                                    count += 1
-                            value = value/count                        
-
-                            self.update(source='globaldata',pars={key:value.item()})
-
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            self.logger.debug('also update LAI based on LAIpixel and cveg') 
-            # I suppose LAI pixel is already determined in the previous
-            # procedure. Anyway...
-            key = 'LAIpixel'
-
-            if globaldata.datasets[key].page is not None:
-                # first, we browse to the correct file that has the current time
-                if 'time' in list(globaldata.datasets[key].page[key].dims):
-                    globaldata.datasets[key].browse_page(time=classdatetime)
-            
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - self.pars.latitude))
-                ilat = np.where((DIST) == np.min(DIST))[0][0]
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - self.pars.longitude))
-                ilon = np.where((DIST) == np.min(DIST))[0][0]
-                 
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude + 0.5)))
-                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmax = ilat
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values \
-                        - (self.pars.longitude  + 0.5)))
-                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmax = ilon
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude - 0.5)))
-                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmin = ilat
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - (self.pars.longitude  - 0.5)))
-                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmin = ilon        
-                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                
-                
-                if ilatmin < ilatmax:
-                    ilatrange = range(ilatmin,ilatmax+1)
-                else:
-                    ilatrange = range(ilatmax,ilatmin+1)
-                    
-                if ilonmin < ilonmax:
-                    ilonrange = range(ilonmin,ilonmax+1)
-                else:
-                    ilonrange = range(ilonmax,ilonmin+1)           
-                
-                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
-                LAIpixel = 0.
-                count = 0
-                for iilat in [ilat]: #ilatrange
-                    for iilon in [ilon]: #ilonrange
-                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
-                        
-                                        
-                        # if np.isnan(tarray[idatetime]):
-                        #     print("interpolating GIMMS LAIpixel nan value")
-                        #     
-                        #     mask = np.isnan(tarray)
-                        #     
-                        #     #replace each nan value with a interpolated value
-                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-                        #         
-                        #     else:
-                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
-                    
-                        #         tarray *= np.nan 
-                        
-                        count += 1
-                        #tarray_res += tarray
-                LAIpixel = LAIpixel/count
-                
-                count = 0
-                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
-  
-                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
-                #print('LAIpixel:',self.__dict__['LAIpixel'])
-                #print('cveg:',self.__dict__['cveg'])
-                
-                # finally, we rescale the LAI according to the vegetation
-                # fraction
-                value = 0. 
-                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
-                   value =self.pars.LAIpixel/self.pars.cveg
-                else:
-                    # in case of small vegetation fraction, we take just a standard 
-                    # LAI value. It doesn't have a big influence anyway for
-                    # small vegetation
-                    value = 2.
-                #print('LAI:',self.__dict__['LAI'])
-                self.update(source='globaldata',pars={'LAI':value}) 
-
-
-        # in case we have 'sp', we also calculate the 3d pressure fields at
-        # full level and half level
-        if ('sp' in keys) and ('sp' in self.pars.__dict__):
-            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
-
-            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # hydrostatic thickness of each model layer
-            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
-            # # dz = rhodz/(R * T / pfull)
-
-
-            # # subsidence multiplied by density. We calculate the subsidence of
-            # # the in class itself
-            # wrho = np.zeros_like(phalf)
-            # wrho[-1] = 0. 
-
-            # for ihlev in range(0,wrho.shape[0]-1):
-            #     # subsidence multiplied by density is the integral of
-            #     # divergences multiplied by the layer thicknessies
-            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
-            #                     self.air_ac['divU_y'][ihlev:]) * \
-            #                    delpdgrav[ihlev:]).sum()
-
-
-            
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'p':list(pfull)}))
-            self.update(source='globaldata',\
-                        air_ach=pd.DataFrame({'p':list(phalf)}))
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
-            # self.update(source='globaldata',\
-            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
-
-    def check_source(self,source,check_only_sections=None):
-        """ this procedure checks whether data of a specified source is valid.
-
-        INPUT:
-            source: the data source we want to check
-            check_only_sections: a string or list with sections to be checked
-        OUTPUT:
-            returns True or False
-        """
-
-        # we set source ok to false as soon as we find a invalid input
-        source_ok = True
-
-        # convert to a single-item list in case of a string
-        check_only_sections_def = (([check_only_sections]) if \
-                                   type(check_only_sections) is str else \
-                                    check_only_sections)
-                                  
-        if source not in self.sources.keys():
-            self.logger.info('Source '+source+' does not exist')
-            source_ok = False
-
-        for sectiondatakey in self.sources[source]:                             
-            section,datakey = sectiondatakey.split(':')                         
-            if ((check_only_sections_def is None) or \
-                (section in check_only_sections_def)):                          
-                checkdatakeys = []
-                if type(self.__dict__[section]) is pd.DataFrame:
-                    checkdata = self.__dict__[section]
-                elif type(self.__dict__[section]) is model_input:
-                    checkdata = self.__dict__[section].__dict__
-
-                if (datakey not in checkdata):                              
-                    # self.logger.info('Expected key '+datakey+\
-                    #                  ' is not in parameter input')                        
-                    source_ok = False                                           
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
-        
-                    # self.logger.info('Key value of "'+datakey+\
-                    #                  '" is invalid: ('+ \
-                    # str(self.__dict__[section].__dict__[datakey])+')')         
-                    source_ok = False
-
-        return source_ok
-
-    def check_source_globaldata(self):
-        """ this procedure checks whether all global parameter data is
-        available, according to the keys in the self.sources"""
-
-        source_globaldata_ok = True
-
-        #self.get_values_air_input()
-
-        # and now we can get the surface values
-        #class_settings = class4gl_input()
-        #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
-        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
-            source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
-        else:
-            # we only check the ground parameter data (pars section). The 
-            # profile data (air_ap section) are supposed to be valid in any 
-            # case.
-            source_ok = self.check_source(source='globaldata',\
-                                          check_only_sections=['air_ac',\
-                                                               'air_ap',\
-                                                               'pars'])
-            if not source_ok:
-                source_globaldata_ok = False
-        
-            # Additional check: we exclude desert-like
-            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
-                source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
-                source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
-            elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
-                source_globaldata_ok = False
-
-        return source_globaldata_ok
-
-
-class c4gli_iterator():
-    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
-    
-        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
-    """
-    def __init__(self,file):
-        # take file as IO stream
-        self.file = file
-        self.yaml_generator = yaml.load_all(file)
-        self.current_dict = {}
-        self.current_class4gl_input = class4gl_input()
-        separator = self.file.readline() # this is just dummy
-        self.header = file.readline()
-        if self.header != '# CLASS4GL record; format version: 0.1\n':
-            raise NotImplementedError("Wrong format version: '"+self.header+"'")
-    def __iter__(self):
-        return self
-    def __next__(self):
-        self.current_dict = self.yaml_generator.__next__()
-        self.current_class4gl_input.load_yaml_dict(self.current_dict)
-        return self.current_class4gl_input
-
-
-
-#get_cape and lift_parcel are adapted from the SkewT package
-    
-class gl_dia(object):
-    def get_lifted_index(self,timestep=-1):
-        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
-    
-#from SkewT
-#def get_lcl(startp,startt,startdp,nsteps=101):
-#    from numpy import interp
-#    #--------------------------------------------------------------------
-#    # Lift a parcel dry adiabatically from startp to LCL.
-#    # Init temp is startt in K, Init dew point is stwrtdp,
-#    # pressure levels are in Pa    
-#    #--------------------------------------------------------------------
-#
-#    assert startdp<=startt
-#
-#    if startdp==startt:
-#        return np.array([startp]),np.array([startt]),np.array([startdp]),
-#
-#    # Pres=linspace(startp,60000.,nsteps)
-#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
-#
-#    # Lift the dry parcel
-#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
-#    # Mixing ratio isopleth
-#    starte=VaporPressure(startdp)
-#    startw=MixRatio(starte,startp)
-#    e=Pres*startw/(.622+startw)
-#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
-#
-#    # Solve for the intersection of these lines (LCL).
-#    # interp requires the x argument (argument 2)
-#    # to be ascending in order!
-#    P_lcl=interp(0.,T_iso-T_dry,Pres)
-#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
-#
-#    # # presdry=linspace(startp,P_lcl)
-#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
-#
-#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
-#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
-#
-#    return P_lcl,T_lcl
-
-
-
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
-
-
-
-#from class
-def get_lcl(startp,startt,startqv):
-        # Find lifting condensation level iteratively
-    lcl = 20.
-    RHlcl = 0.5
-    
-    itmax = 30
-    it = 0
-    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       #self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_air_input()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        #print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_air_input()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_air_input()
-       
-
-
-    def get_values_air_input(self,latitude=None,longitude=None):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        BLHV = np.max((BLHV,10.))
-        BLHVu = np.max((BLHVu,10.))
-        BLHVd = np.max((BLHVd,10.))
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
-                
-                # get index of lowest valid observation. This seems to vary
-                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
-                if len(idxvalid) > 0:
-                    #print('idxvalid',idxvalid)
-                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
-                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
-                    else:
-                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
-                    #print(col,meanabl)
-               
-                
-                # if col == 'PRES':
-                #     meanabl =  
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV',]:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
-                        dtheta = np.max((0.1,dtheta_pre))
-                        #meanabl = meanabl - (dtheta - dtheta_pre)
-                        #print('dtheta_pre',dtheta_pre)
-                        #print('dtheta',dtheta)
-                        #print('meanabl',meanabl)
-                        #stop
-                        
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
-            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-            
-            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
-        
-            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
-            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
-
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-
-        try:
-            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
-        except:
-            print("could not convert latitude coordinate")
-            PARAMS.insert(0,'latitude', np.nan)
-            PARAMS.insert(0,'lat', np.nan)
-        try:
-            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
-            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
-            PARAMS.insert(0,'lon', 0.)
-        except:
-            print("could not convert longitude coordinate")
-            PARAMS.insert(0,'longitude', np.nan)
-            PARAMS.insert(0,'lon', 0.)
-
-        if latitude is not None:
-            print('overwriting latitude with specified value')
-            PARAMS['latitude'] = np.float(latitude)
-            PARAMS['lat'] = np.float(latitude)
-        if longitude is not None:
-            print('overwriting longitude with specified value')
-            PARAMS['longitude'] = np.float(longitude)
-        try:
-            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
-            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
-            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            # This is the nearest datetime when sun is up (for class)
-            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
-            # apply the same time shift for UTC datetime
-            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
-            
-        except:
-            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
-            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
-            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
-            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
-
-        
-
-        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
-        # as we are forcing lon equal to zero this is is expressed in local suntime
-        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
-
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
-        elif self.mode == 'b': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNb
-            BLCOLUMN = ONE_COLUMNb
-        elif self.mode == 'u': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNu
-            BLCOLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNd
-            BLCOLUMN = ONE_COLUMNd
-        else:
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb
-
-        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
-        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
-        # print(BLCOLUMN['HAGL'][lt6000])
-        # print(BLCOLUMN['HAGL'][lt2500])
-        # 
-        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
-
-        #print(BLCOLUMN['HAGL'][lt2500])
-        PARAMS.insert(0,'OK',
-                      ((BLHVe < 200.) and 
-                       ( len(np.where(lt6000)[0]) > 5) and
-                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
-                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
-                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
-                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
-                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
-                      )
-                     )
-
-        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
-        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
-        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
-        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
-        
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = USE_ONECOLUMN
-        # if self.mode == 'o': #original 
-        #     self.ONE_COLUMN = ONE_COLUMN
-        # elif self.mode == 'b': # best BLH
-        #     self.ONE_COLUMN = ONE_COLUMNb
-        # elif self.mode == 'u':# upper BLH
-        #     self.ONE_COLUMN = ONE_COLUMNu
-        # elif self.mode == 'd': # lower BLH
-        #     self.ONE_COLUMN=ONE_COLUMNd
-        # else:
-        #     self.ONE_COLUMN = ONE_COLUMN
-
diff --git a/lib/data_global.py b/lib/data_global.py
deleted file mode 100644
index 9c3d9b5..0000000
--- a/lib/data_global.py
+++ /dev/null
@@ -1,936 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: Hendrik Wouters
-
-Purpose: provides class routines for ground and atmosphere conditions used for
-the CLASS miced-layer model
-
-Usage:
-    from data_global import data_global
-    from class4gl import class4gl_input
-    from data_soundings import wyoming
-
-    # create a data_global object and load initial data pages
-    globaldata = data_global()
-    globaldata.load_datasets()
-    # create a class4gl_input object
-    c4gli = class4gl_input()
-    # Initialize it with profile data. We need to do this first. Actually this
-    # will set the coordinate parameters (datetime, latitude, longitude) in
-    # class4gl_input.pars.__dict__, which is required to read point data from
-    # the data_global object.
-
-    # open a Wyoming stream for a specific station
-    wy_strm = wyoming(STNM=91376)
-    # load the first profile
-    wy_strm.find_first()
-    # load the profile data into the class4gl_input object
-    c4gli.get_profile_wyoming(wy_strm)
-    
-    # and finally, read the global input data for this profile
-    c4gli.get_global_input(globaldata)
-
-
-"""
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-#import pynacolada as pcd
-import pandas as pd
-import xarray as xr
-import os
-import glob
-import sys
-import errno
-import warnings
-import logging
-
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-class book(object):
-    """ this is a class for a dataset spread over multiple files. It has a
-    similar purpose  open_mfdataset, but only 1 file (called current 'page')
-    one is loaded at a time. This saves precious memory.  """
-    def __init__(self,fn,concat_dim = None,debug_level=None):
-        self.logger = logging.getLogger('book')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # filenames are expanded as a list and sorted by filename
-        self.pages = glob.glob(fn); self.pages.sort()
-        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
-        if len(self.pages) == 0:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
-        self.ipage = -1; self.page = None
-        self.renames = {} # each time when opening a file, a renaming should be done.
-        self.set_page(0)
-
-        # we consider that the outer dimension is the one we concatenate
-        self.concat_dim = concat_dim
-        if self.concat_dim is None:
-            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
-
-    # this wraps the xarray sel-commmand
-    def sel(*args, **kwargs):
-        for dim in kwargs.keys():
-            if dim == self.concat_dim:
-                self.browse_page(**{dim: kwargs[dim]})
-        return page.sel(*args,**kwargs)
-
-
-    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
-    #def __getattr__(self,attr):
-    #    orig_attr = self.page.__getattribute__(attr)
-    #    if callable(orig_attr):
-    #        def hooked(*args, **kwargs):
-    #            for dim in kwargs.keys():
-    #                if dim == self.concat_dim:
-    #                    self.browse_page(**{dim: kwargs[dim]})
-    #
-    #            result = orig_attr(*args, **kwargs)
-    #            # prevent wrapped_class from becoming unwrapped
-    #            if result == self.page:
-    #                return self
-    #            self.post()
-    #            return result
-    #        return hooked
-    #    else:
-    #        return orig_attr
-
-    def set_renames(self,renames):
-        #first, we convert back to original names, and afterwards, we apply the update of the renames.
-        reverse_renames = dict((v,k) for k,v in self.renames.items())
-        self.renames = renames
-        if self.page is not None:
-            self.page = self.page.rename(reverse_renames)
-            self.page = self.page.rename(self.renames)
-
-    def set_page(self,ipage,page=None):
-        """ this sets the right page according to ipage:
-                - We do not switch the page if we are already at the right one
-                - we set the correct renamings (level -> lev, latitude -> lat,
-                etc.)
-                - The dataset is also squeezed.
-        """
-
-        if ((ipage != self.ipage) or (page is not None)):
-
-            if self.page is not None:
-                self.page.close()
-
-            self.ipage = ipage
-            if page is not None:
-                self.page = page
-            else:
-                if self.ipage == -1:
-                   self.page = None
-                else:
-                    #try:
-
-                    self.logger.info("Switching to page "+str(self.ipage)+': '\
-                                     +self.pages[self.ipage])
-                    self.page = xr.open_dataset(self.pages[self.ipage])
-
-
-            # do some final corrections to the dataset to make them uniform
-            if self.page is not None:
-               if 'latitude' in self.page.dims:
-#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
-               if 'level' in self.page.dims:
-                   self.page = self.page.rename({'level':'lev'})
-
-               self.page = self.page.rename(self.renames)
-               self.page = self.page.squeeze(drop=True)
-
-    def browse_page(self,rewind=2,**args):
-
-        # at the moment, this is only tested with files that are stacked according to the time dimension.
-        dims = args.keys()
-
-
-        if self.ipage == -1:
-            self.set_page(0)
-
-        found = False
-        iipage = 0
-        startipage = self.ipage - rewind
-        while (iipage < len(self.pages)) and not found:
-            ipage = (iipage+startipage) % len(self.pages)
-            for dim in args.keys():
-                this_file = True
-
-                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
-                if 'dims' not in self.__dict__:
-                    self.dims = {}
-                if dim not in self.dims.keys():
-                    self.dims[dim] = [None]*len(self.pages)
-
-                if self.dims[dim][ipage] is None:
-                    self.logger.info('Loading coordinates of dimension "'+dim+\
-                                     '" of page "' +str(ipage)+'".')
-                    self.set_page(ipage)
-                    # print(ipage)
-                    # print(dim)
-                    # print(dim,self.page[dim].values)
-                    self.dims[dim][ipage] = self.page[dim].values
-
-                # determine current time range of the current page
-                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
-                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
-
-                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
-                    this_file = False
-
-            if this_file:
-                found = True
-                self.set_page(ipage)
-            else:
-
-                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
-                #    iipage = len(self.pages) # we stop searching
-
-                iipage += 1
-
-        if not found:
-            self.logger.info("Page not found. Setting to page -1")
-            #iipage = len(self.pages) # we stop searching further
-            self.set_page(-1)
-
-        if self.ipage != -1:
-            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
-        else:
-            self.logger.debug("I'm now at page "+ str(self.ipage))
-
-
-class data_global(object):
-    def __init__(self,sources= {
-        # # old gleam
-        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
-        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
-        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
-        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
-        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
-        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
-        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
-        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
-        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
-        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
-        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
-        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
-        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
-        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
-        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
-        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
-        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
-        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
-        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
-        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
-        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
-        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
-        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
-        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
-        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
-        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
-        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
-        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
-        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
-        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
-        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
-        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
-        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
-        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
-        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
-        },debug_level=None):
-        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
-        self.sources = sources
-        self.datarefs = {}
-        self.datasets = {}
-        self.datetime = dt.datetime(1981,1,1)
-
-        self.logger = logging.getLogger('data_global')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-        self.debug_level = debug_level
-
-        warnings.warn('omitting pressure field p and advection')
-
-    def in_library(self,fn):
-        if fn not in self.library.keys():
-            return False
-        else:
-            print("Warning: "+fn+" is already in the library.")
-            return True
-
-    def add_to_library(self,fn):
-        if not self.in_library(fn):
-            print("opening: "+fn)
-            self.library[fn] = \
-                book(fn,concat_dim='time',debug_level=self.debug_level)
-
-            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
-            #if 'latitude' in self.library[fn].variables:
-            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-
-    # default procedure for loading datasets into the globaldata library
-    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
-        if type(varssource) is str:
-            varssource = [varssource]
-        if type(varsdest) is str:
-            varsdest = [varsdest]
-
-        self.add_to_library(input_fn)
-
-        if varssource is None:
-            varssource = []
-            for var in self.sources[input_fn].variables:
-                avoid = \
-                ['lat','lon','latitude','longitude','time','lev','level']
-                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
-                    varssource.append(var)
-
-        if varsdest is None:
-            varsdest = varssource
-
-        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        for ivar,vardest in enumerate(varsdest):
-            varsource = varssource[ivar]
-            print('setting '+vardest+' as '+varsource+' from '+input_fn)
-
-            if vardest in self.datarefs.keys():
-                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
-            #self.add_to_library(fn,varsource,vardest)
-            if vardest != varsource:
-                libkey = input_fn+'.'+varsource+'.'+vardest
-                if libkey not in self.library.keys():
-                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
-                    self.library[libkey] = book(input_fn,\
-                                                debug_level=self.debug_level)
-                    self.library[libkey].set_renames({varsource: vardest})
-
-                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-            else:
-                self.datarefs[vardest] = input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-
-            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
-            #     print('Warning: '+ vardest "not in " + input_fn)
-
-
-
-    def load_datasets(self,sources = None,recalc=0):
-
-        if sources is None:
-            sources = self.sources
-        for key in sources.keys():
-            #datakey,vardest,*args = key.split(':')
-            datakey,vardest = key.split(':')
-            #print(datakey)
-
-            fnvarsource = sources[key].split(':')
-            if len(fnvarsource) > 2:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource,fnargs = fnvarsource
-                fnargs = [fnargs]
-            elif len(fnvarsource) > 1:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource = fnvarsource
-                fnargs = []
-            else:
-                fn = sources[key]
-                varsource = vardest
-            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
-
-    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
-            # the default way of loading a 2d dataset
-            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
-                self.load_dataset_default(fn,varsource,vardest)
-            elif datakey == 'IGBPDIS':
-                if vardest == 'alpha':
-                    ltypes = ['W','B','H','TC']
-                    for ltype in ltypes:
-                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
-                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
-
-
-                    # landfr = {}
-                    # for ltype in ['W','B','H','TC']:
-                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
-
-
-
-                    keytemp = 'alpha'
-                    fnkeytemp = fn+':IGBPDIS:alpha'
-                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
-                        self.library[fnkeytemp]  = book(fnkeytemp,
-                                                        debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-                    else:
-                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
-                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
-                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
-                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
-                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
-                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
-                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
-
-                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-
-                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
-                        for ltype in ltypes:
-                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
-
-                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
-                        print('writing file to: '+fnkeytemp)
-                        os.system('rm '+fnkeytemp)
-                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
-                        self.library[fnkeytemp].close()
-
-
-                        self.library[fnkeytemp]  = \
-                            book(fnkeytemp,debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-
-
-                else:
-                    self.load_dataset_default(fn,varsource,vardest)
-
-
-            elif datakey == 'GLAS':
-                self.load_dataset_default(fn,varsource,vardest)
-                if vardest == 'z0m':
-                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
-                elif vardest == 'z0h':
-                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
-            elif datakey == 'DSMW':
-
-
-                # Procedure of the thermal properties:
-                # 1. determine soil texture from DSMW/10.
-                # 2. soil type with look-up table (according to DWD/EXTPAR)
-                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
-                #    with parameter look-up table from Noilhan and Planton (1989).
-                #    Note: The look-up table is inspired on DWD/COSMO
-
-                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
-
-
-
-                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
-                self.load_dataset_default(fn,'DSMW')
-                print('calculating texture')
-                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
-                TEMP  = {}
-                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
-                TEMP3 = {}
-                for SPKEY in SPKEYS:
-
-
-                    keytemp = SPKEY+'_values'
-                    fnoutkeytemp = fnout+':DSMW:'+keytemp
-                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                    else:
-                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
-                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
-                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-                        # for faster computation, we need to get it to memory out of Dask.
-                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
-                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
-
-                # yes, I know I only check the last file.
-                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
-                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
-                        print('idx',idx,SPKEY)
-                        SEL = (TEMP2 == idx)
-                    #     print(idx,len(TEMP3))
-                        for SPKEY in SPKEYS:
-                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
-
-                    for SPKEY in SPKEYS:
-                        keytemp = SPKEY+'_values'
-                        fnoutkeytemp = fnout+':DSMW:'+keytemp
-                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
-                        os.system('rm '+fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].close()
-
-
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                keytemp = 'texture'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-                else:
-                    self.library[fn+':DSMW:texture'] = xr.Dataset()
-                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
-                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
-                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
-                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-
-                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
-
-                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
-                    zundef[zundef < 0] = np.nan
-                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
-                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
-
-                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-
-
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-                print('calculating texture type')
-
-
-
-                keytemp = 'itex'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-                else:
-                    self.library[fnoutkeytemp] = xr.Dataset()
-                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-                    X = self.datasets['texture'].page['texture'].values*100
-                    X[pd.isnull(X)] = -9
-
-
-                    self.datasets[keytemp][keytemp].values = X
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
-                    self.datasets['itex'].close()
-
-
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-
-                keytemp = 'isoil'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                isoil_reprocessed = False
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-                else:
-                    isoil_reprocessed = True
-                    print('calculating soil type')
-                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    ITEX = self.datasets['itex'].page['itex'].values
-                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
-                    LOOKUP = [
-                              [-10 ,9],# ocean
-                              [0 ,7],# fine textured, clay (soil type 7)
-                              [20,6],# medium to fine textured, loamy clay (soil type 6)
-                              [40,5],# medium textured, loam (soil type 5)
-                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                              [80,3],# coarse textured, sand (soil type 3)
-                              [100,9],# coarse textured, sand (soil type 3)
-                            ]
-                    for iitex,iisoil in LOOKUP:
-                        ISOIL[ITEX > iitex] = iisoil
-                        print('iitex,iisoil',iitex,iisoil)
-
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    LOOKUP = [
-                              [9001, 1 ], # ice, glacier (soil type 1)
-                              [9002, 2 ], # rock, lithosols (soil type 2)
-                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                              [9,    9 ], # undefined (ocean)
-                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                              [9000, 9 ], # undefined (inland lake)
-                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                            ]
-                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
-
-                    CODE_VALUES[ITEX == 901200] = 9012
-                    for icode,iisoil in LOOKUP:
-                        ISOIL[CODE_VALUES == icode] = iisoil
-
-                    self.datasets['isoil']['isoil'].values = ISOIL
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-                    print('saved inbetween file to: '+fnoutkeytemp)
-
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                #adopted from data_soil.f90 (COSMO5.0)
-                SP_LOOKUP = {
-                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
-                  # (by index)                                           loam                    loam                                water      ice
-                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-                  # Important note: For peat, the unknown values below are set equal to that of loam
-                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
-                  #error in table 2 of NP89: values need to be multiplied by e-6
-                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
-
-                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
-                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
-                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
-                }
-
-
-                # isoil_reprocessed = False
-                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-
-                #     self.library[fn+':DSMW:isoil'] = \
-                #             book(fnoutkeytemp,debug_level=self.debug_level)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-                # else:
-                #     isoil_reprocessed = True
-                #     print('calculating soil type')
-                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-
-
-
-                # this should become cleaner in future but let's hard code it for now.
-                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
-                print('calculating soil parameter')
-                DATATEMPSPKEY = {}
-                if (recalc < 1) and (isoil_reprocessed == False): 
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        keytemp = SPKEY
-                        fnoutkeytemp=fnout+':DSMW:'+keytemp
-                        self.library[fn+':DSMW:'+SPKEY] =\
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
-                        self.datarefs[SPKEY] =fnoutkeytemp
-                else:
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-
-                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
-                    ISOIL = self.datasets['isoil'].page['isoil'].values
-                    print(np.where(ISOIL>0.))
-                    for i in range(11):
-                        SELECT = (ISOIL == i)
-                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
-
-                        os.system('rm '+fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].close()
-                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
-
-                        self.library[fn+':DSMW:'+SPKEY] = \
-                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-
-
-            else:
-                self.load_dataset_default(fn,varsource,vardest)
-
-
-
-
-
-
-#
-#                 # only print the last parameter value in the plot
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'cala'
-#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'crhoc'
-#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#     key = "CERES"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         CERES_start_date = dt.datetime(2000,3,1)
-#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-#
-#         var = 'cc'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#         print(class_settings.lat,class_settings.lon)
-#
-#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
-#
-#         input_nc.close()
-#
-
-
-#     key = "GIMMS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-#         print("Reading Leag Area Index from "+input_fn)
-#         var = 'LAI'
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-#
-#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-#
-#         if np.isnan(tarray[idatetime]):
-#             print("interpolating GIMMS cveg nan value")
-#
-#             mask = np.isnan(tarray)
-#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-#             else:
-#                 print("Warning. Could not interpolate GIMMS cveg nan value")
-#
-#         class_settings.__dict__[var] = tarray[idatetime]
-#
-#         input_nc.close()
-#
-#     key = "IGBPDIS_ALPHA"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         var = 'alpha'
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-#         print("Reading albedo from "+input_fn)
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#
-#         landfr = {}
-#         for ltype in ['W','B','H','TC']:
-#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-#
-#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-#
-#         alpha=0.
-#         for ltype in landfr.keys():
-#             alpha += landfr[ltype]*aweights[ltype]
-#
-#
-#         class_settings.__dict__[var] = alpha
-#         input_nc.close()
-#
-#
-#     key = "ERAINT_ST"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         print("Reading soil temperature from "+input_fn)
-#
-#         var = 'Tsoil'
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         var = 'T2'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-#
-#
-#         input_nc.close()
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #var = 'T2'
-#     #valold = class_settings.__dict__[var]
-#     #
-#     #class_settings.__dict__[var] = 305.
-#     #class_settings.__dict__['Tsoil'] = 302.
-#     #valnew = class_settings.__dict__[var]
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #
-#     #var = 'Lambda'
-#     #valold = class_settings.__dict__[var]
-#
-#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
-#     ## I need to ask Chiel.
-#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-#     #
-#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
-#     #class_settings.__dict__[var] = valnew
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     key = "GLAS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-#         print("Reading canopy height for determining roughness length from "+input_fn)
-#         var = 'z0m'
-#
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-#
-#         lowerlimit = 0.01
-#         if testval < lowerlimit:
-#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-#             class_settings.__dict__[var] = lowerlimit
-#         else:
-#             class_settings.__dict__[var] = testval
-#
-#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-#
-#
-#         input_nc.close()
-
-
-
-
-
diff --git a/lib/interface_functions.py b/lib/interface_functions.py
deleted file mode 100644
index 3e483f3..0000000
--- a/lib/interface_functions.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-#from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-#'_afternoon.yaml'
-def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
-    filename = yaml_file.name
-    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-    #yaml_file = open(filename)
-
-    #print('going to next observation',filename)
-    yaml_file.seek(index_start)
-
-    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-
-    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-    filebuffer.write(buf)
-    filebuffer.close()
-    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-    
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-
-    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-    print(command)
-    os.system(command)
-    jsonstream = open(filename+'.buffer.json.'+str(index_start))
-    record_dict = json.load(jsonstream)
-    jsonstream.close()
-    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-
-
-    if mode =='mod':
-        modelout = class4gl()
-        modelout.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        return modelout
-    elif mode == 'ini':
-
- 
-        # datetimes are incorrectly converted to strings. We need to convert them
-        # again to datetimes
-        for key,value in record_dict['pars'].items():
-            # we don't want the key with columns that have none values
-            if value is not None: 
-                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
-               # elif (type(value) == str):
-                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-
-            if (value == 0.9e19) or (value == '.9e19'):
-                record_dict['pars'][key] = np.nan
-        for key in record_dict.keys():
-            #print(key)
-            if key in ['air_ap','air_balloon',]:
-                #NNprint('check')
-                for datakey,datavalue in record_dict[key].items():
-                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-
-        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        c4gli = class4gl_input()
-        print(c4gli.logger,'hello')
-        c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-        return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-
-class stations(object):
-    def __init__(self,path,suffix='ini',refetch_stations=False):
-
-        self.path = path
-
-        self.file = self.path+'/stations_list.csv'
-        if (os.path.isfile(self.file)) and (not refetch_stations):
-            self.table = pd.read_csv(self.file)
-        else:
-            self.table = self.get_stations(suffix=suffix)
-            self.table.to_csv(self.file)
-        
-        self.table = self.table.set_index('STNID')
-        #print(self.table)
-
-    def get_stations(self,suffix):
-        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
-        if len(stations_list_files) == 0:
-            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
-        stations_list_files.sort()
-        print(stations_list_files)
-        if len(stations_list_files) == 0:
-            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
-        stations_list = []
-        for stations_list_file in stations_list_files:
-            thisfile = open(stations_list_file,'r')
-            yamlgen = yaml.load_all(thisfile)
-            try:
-                first_record  = yamlgen.__next__()
-            except:
-                first_record = None
-            if first_record is not None:
-                stations_list.append({})
-                for column in ['STNID','latitude','longitude']:
-                    #print(first_record['pars'].keys())
-                    stations_list[-1][column] = first_record['pars'][column]
-                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
-            yamlgen.close()
-            thisfile.close()
-    
-        print(stations_list)
-        return pd.DataFrame(stations_list)
-
-class stations_iterator(object):
-    def __init__(self,stations):
-        self.stations = stations
-        self.ix = -1 
-    def __iter__(self):
-        return self
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.stations.table)) 
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_row(self,row):
-        self.ix = row
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_STNID(self,STNID):
-        self.ix = np.where((self.stations.table.index == STNID))[0][0]
-        print(self.ix)
-        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-    def close():
-        del(self.ix)
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.records))
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-# #'_afternoon.yaml'
-# def get_record_yaml(yaml_file,index_start,index_end):
-#     filename = yaml_file.name
-#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-#     #yaml_file = open(filename)
-# 
-#     #print('going to next observation',filename)
-#     yaml_file.seek(index_start)
-# 
-#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-# 
-#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-#     filebuffer.write(buf)
-#     filebuffer.close()
-#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-#     
-#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-# 
-#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-#     print(command)
-#     os.system(command)
-#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
-#     record_dict = json.load(jsonstream)
-#     jsonstream.close()
-#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-#  
-#     # datetimes are incorrectly converted to strings. We need to convert them
-#     # again to datetimes
-#     for key,value in record_dict['pars'].items():
-#         # we don't want the key with columns that have none values
-#         if value is not None: 
-#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
-#            # elif (type(value) == str):
-#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-#                 
-#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
-# 
-#         if (value == 0.9e19) or (value == '.9e19'):
-#             record_dict['pars'][key] = np.nan
-#     for key in record_dict.keys():
-#         print(key)
-#         if key in ['air_ap','air_balloon',]:
-#             print('check')
-#             for datakey,datavalue in record_dict[key].items():
-#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-# 
-#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-# 
-#     c4gli = class4gl_input()
-#     c4gli.load_yaml_dict(record_dict)
-#     return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
-
-    records = pd.DataFrame()
-    for STNID,station in stations.iterrows():
-        dictfnchunks = []
-        if getchunk is 'all':
-
-            # we try the old single-chunk filename format first (usually for
-            # original profile pairs)
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
-            if os.path.isfile(fn):
-                chunk = 0
-                dictfnchunks.append(dict(fn=fn,chunk=chunk))
-
-            # otherwise, we use the new multi-chunk filename format
-            else:
-                chunk = 0
-                end_of_chunks = False
-                while not end_of_chunks:
-                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(fn):
-                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
-                    else:
-                        end_of_chunks = True
-                    chunk += 1
-
-            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
-            # yamlfilenames = glob.glob(globyamlfilenames)
-            # yamlfilenames.sort()
-        else:
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
-            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
-            
-        if len(dictfnchunks) > 0:
-            for dictfnchunk in dictfnchunks:
-                yamlfilename = dictfnchunk['fn']
-                chunk = dictfnchunk['chunk']
-                print(chunk)
-
-                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
-                pklfilename = yamlfilename.replace('.yaml','.pkl')
-
-                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
-                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
-                generate_pkl = False
-                if not os.path.isfile(pklfilename): 
-                    print('pkl file does not exist. I generate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                elif not (os.path.getmtime(yamlfilename) <  \
-                    os.path.getmtime(pklfilename)):
-                    print('pkl file older than yaml file, so I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-
-                if refetch_records:
-                    print('refetch_records flag is True. I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(pklfilename)])
-                   # irecord = 0
-                else:
-                    with open(yamlfilename) as yaml_file:
-
-                        dictout = {}
-
-                        next_record_found = False
-                        end_of_file = False
-                        while (not next_record_found) and (not end_of_file):
-                            linebuffer = yaml_file.readline()
-                            next_record_found = (linebuffer == '---\n')
-                            end_of_file = (linebuffer == '')
-                        next_tell = yaml_file.tell()
-                        
-                        while not end_of_file:
-
-                            print(' next record:',next_tell)
-                            current_tell = next_tell
-                            next_record_found = False
-                            yaml_file.seek(current_tell)
-                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
-                            linebuffer = ''
-                            while ( (not next_record_found) and (not end_of_file)):
-                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
-                                linebuffer = yaml_file.readline()
-                                next_record_found = (linebuffer == '---\n')
-                                end_of_file = (linebuffer == '')
-                            filebuffer.close()
-                            
-                            next_tell = yaml_file.tell()
-                            index_start = current_tell
-                            index_end = next_tell
-
-                            
-                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                            print(command)
-                            
-                            os.system(command)
-                            #jsonoutput = subprocess.check_output(command,shell=True) 
-                            #print(jsonoutput)
-                            #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
-                            record = json.load(jsonstream)
-                            dictouttemp = {}
-                            for key,value in record['pars'].items():
-                                # we don't want the key with columns that have none values
-                                if value is not None: 
-                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
-                                   if (type(value) in regular_numeric_types):
-                                        dictouttemp[key] = value
-                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
-                            recordindex = record['index']
-                            dictouttemp['chunk'] = chunk
-                            dictouttemp['index_start'] = index_start
-                            dictouttemp['index_end'] = index_end
-                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
-                            for key,value in dictouttemp.items():
-                                if key not in dictout.keys():
-                                    dictout[key] = {}
-                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
-                            print(' obs record registered')
-                            jsonstream.close()
-                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
-                    records_station = pd.DataFrame.from_dict(dictout)
-                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+pklfilename+') for station '\
-                          +str(STNID))
-                    records_station.to_pickle(pklfilename)
-                    # else:
-                    #     os.system('rm '+pklfilename)
-                    records = pd.concat([records,records_station])
-    return records
-
-def stdrel(mod,obs,columns):
-    stdrel = pd.DataFrame(columns = columns)
-    for column in columns:
-        stdrel[column] = \
-                (mod.groupby('STNID')[column].transform('mean') -
-                 obs.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') + \
-                (mod[column] -
-                 mod.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') 
-    return stdrel
-
-def pct(obs,columns):
-    pct = pd.DataFrame(columns=columns)
-    for column in columns:
-        #print(column)
-        pct[column] = ""
-        pct[column] = obs[column].rank(pct=True)
-    return pct
-
-def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
-    stats = pd.DataFrame()
-    for key in keys: 
-        stats['d'+key+'dt'] = ""
-        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
-                              (obs_afternoon.ldatetime - \
-                               obs_morning.ldatetime).dt.seconds*3600.
-    return stats
-
diff --git a/lib/interface_multi.py b/lib/interface_multi.py
deleted file mode 100644
index 83148e5..0000000
--- a/lib/interface_multi.py
+++ /dev/null
@@ -1,2061 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-# from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-cdictpres = {'blue': (\
-                   (0.,    0.,  0.),
-                   (0.25,  0.25, 0.25),
-                   (0.5,  .70, 0.70),
-                   (0.75, 1.0, 1.0),
-                   (1,     1.,  1.),
-                   ),
-       'green': (\
-                   (0. ,   0., 0.0),
-                   (0.25,  0.50, 0.50),
-                   (0.5,  .70, 0.70),
-                   (0.75,  0.50, 0.50),
-                   (1  ,    0,  0.),
-                   ),
-       'red':  (\
-                  (0 ,  1.0, 1.0),
-                  (0.25 ,  1.0, 1.0),
-                   (0.5,  .70, 0.70),
-                  (0.75 , 0.25, 0.25),
-                  (1,    0., 0.),
-                  )}
-
-statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-os.system('module load Ruby')
-
-class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
-        """ creates an interactive interface for analysing class4gl experiments
-
-        INPUT:
-            path_exp : path of the experiment output
-            path_obs : path of the observations 
-            globaldata: global data that is being shown on the map
-            refetch_stations: do we need to build the list of the stations again?
-        OUTPUT:
-            the procedure returns an interface object with interactive plots
-
-        """
-        
-        # set the ground
-        self.globaldata = globaldata
-
- 
-        self.path_exp = path_exp
-        self.path_obs = path_obs
-        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
-
-        # # get the list of stations
-        # stationsfile = self.path_exp+'/stations_list.csv'
-        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
-        #     stations = pd.read_csv(stationsfile)
-        # else:
-        #     stations = get_stations(self.path_exp)
-        #     stations.to_csv(stationsfile)
-
-        # stations = stations.set_index('STNID')
-
-        self.frames = {}
-
-        self.frames['stats'] = {}
-        self.frames['worldmap'] = {}
-                
-        self.frames['profiles'] = {}
-        self.frames['profiles'] = {}
-        self.frames['profiles']['DT'] = None
-        self.frames['profiles']['STNID'] = None
-
-        #self.frames['worldmap']['stationsfile'] = stationsfile
-        self.frames['worldmap']['stations'] = stations(self.path_exp, \
-                                                       suffix='ini',\
-                                                       refetch_stations=refetch_stations)
-
-        # Initially, the stats frame inherets the values/iterators of
-        # worldmap
-        for key in self.frames['worldmap'].keys():
-            self.frames['stats'][key] = self.frames['worldmap'][key]
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_ini'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='ini',\
-                                           refetch_records=refetch_records
-                                           )
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_mod'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='mod',\
-                                           refetch_records=refetch_records
-                                           )
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_obs_afternoon'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_obs,\
-                                           subset='afternoon',\
-                                           refetch_records=refetch_records
-                                           )
-
-        self.frames['stats']['records_all_stations_mod'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['records_all_stations_ini']['dates'] = \
-            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
-
-
-        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
-
-        self.frames['stats']['records_all_stations_obs_afternoon'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['viewkeys'] = ['h','theta','q']
-        print('Calculating table statistics')
-        self.frames['stats']['records_all_stations_mod_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_mod'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-
-        self.frames['stats']['inputkeys'] = inputkeys
-        
-        # self.frames['stats']['inputkeys'] = \
-        #     [ key for key in \
-        #       self.globaldata.datasets.keys() \
-        #       if key in \
-        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
-
-
-        # get units from the class4gl units database
-        self.units = dict(units)
-        # for those that don't have a definition yet, we just ask a question
-        # mark
-        for var in self.frames['stats']['inputkeys']:
-            self.units[var] = '?'
-
-        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
-        self.frames['stats']['records_all_stations_ini_pct'] = \
-                  pct(self.frames['stats']['records_all_stations_ini'], \
-                      columns = self.frames['stats']['inputkeys'])
-
-        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
-        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-        #     mod['
-
-        # 
-        # 
-        # \
-        #        self.frames['stats']['records_all_stations_mod'], \
-
-
-
-        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
-        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
-        #               columns = [ 'd'+key+'dt' for key in \
-        #                           self.frames['stats']['viewkeys']], \
-        #              )
-
-        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
-        #               obs = self.frames['stats']['records_all_stations_ini'], \
-        #               columns = self.frames['stats']['viewkeys'], \
-        #              )
-        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
-        
-        print('filtering pathological data')
-        # some observational sounding still seem problematic, which needs to be
-        # investigated. In the meantime, we filter them
-        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
-
-        # we filter ALL data frames!!!
-        for key in self.frames['stats'].keys():
-            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
-               (self.frames['stats'][key].index.names == indextype):
-                self.frames['stats'][key] = self.frames['stats'][key][valid]
-        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
-
-        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
-
-
-        print("filtering stations from interface that have no records")
-        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
-            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                    == STNID).sum() == 0):
-                print("dropping", STNID)
-                self.frames['worldmap']['stations'].table = \
-                        self.frames['worldmap']['stations'].table.drop(STNID)
-                    
-        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-        
-        # TO TEST: should be removed, since it's is also done just below
-        self.frames['stats']['stations_iterator'] = \
-            self.frames['worldmap']['stations_iterator'] 
-
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
-        self.next_station()
-
-        # self.goto_datetime_worldmap(
-        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-        #     'after')
-    def sel_station(self,STNID=None,rownumber=None):
-
-        if (STNID is not None) and (rownumber is not None):
-            raise ValueError('Please provide either STNID or rownumber, not both.')
-
-        if (STNID is None) and (rownumber is None):
-            raise ValueError('Please provide either STNID or rownumber.')
-            
-        if STNID is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
-            print(
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-            )
-            self.update_station()
-        elif rownumber is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
-            self.update_station()
-
-
-
-    def next_station(self,event=None,jump=1):
-        with suppress(StopIteration):
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-                = self.frames['worldmap']['stations_iterator'].__next__(jump)
-            # self.frames['worldmap']['stations_iterator'].close()
-            # del(self.frames['worldmap']['stations_iterator'])
-            # self.frames['worldmap']['stations_iterator'] = \
-            #                 selfself.frames['worldmap']['stations'].iterrows()
-            # self.frames['worldmap']['STNID'],\
-            # self.frames['worldmap']['current_station'] \
-            #     = self.frames['worldmap']['stations_iterator'].__next__()
-
-        self.update_station()
-
-    def prev_station(self,event=None):
-        self.next_station(jump = -1,event=event)
-    def update_station(self):
-        for key in ['STNID','current_station','stations_iterator']: 
-            self.frames['stats'][key] = self.frames['worldmap'][key] 
-
-
-
-        # generate index of the current station
-        self.frames['stats']['records_current_station_index'] = \
-            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-             == \
-             self.frames['stats']['current_station'].name)
-
-        # create the value table of the records of the current station
-        tab_suffixes = \
-                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        for tab_suffix in tab_suffixes:
-            self.frames['stats']['records_current_station'+tab_suffix] = \
-                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-        # go to first record of current station
-        self.frames['stats']['records_iterator'] = \
-                        records_iterator(self.frames['stats']['records_current_station_mod'])
-        (self.frames['stats']['STNID'] , \
-        self.frames['stats']['current_record_chunk'] , \
-        self.frames['stats']['current_record_index']) , \
-        self.frames['stats']['current_record_mod'] = \
-                        self.frames['stats']['records_iterator'].__next__()
-
-        for key in self.frames['stats'].keys():
-            self.frames['profiles'][key] = self.frames['stats'][key]
-
-        STNID = self.frames['profiles']['STNID']
-        chunk = self.frames['profiles']['current_record_chunk']
-        if 'current_station_file_ini' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_ini'].close()
-        self.frames['profiles']['current_station_file_ini'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-        if 'current_station_file_mod' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_mod'].close()
-        self.frames['profiles']['current_station_file_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_afternoon'].close()
-        self.frames['profiles']['current_station_file_afternoon'] = \
-            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-        self.frames['profiles']['records_iterator'] = \
-                        records_iterator(self.frames['profiles']['records_current_station_mod'])
-        (self.frames['profiles']['STNID'] , \
-        self.frames['profiles']['current_record_chunk'] , \
-        self.frames['profiles']['current_record_index']) , \
-        self.frames['profiles']['current_record_mod'] = \
-                        self.frames['profiles']['records_iterator'].__next__()
-
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-
-        self.update_record()
-
-    def next_record(self,event=None,jump=1):
-        with suppress(StopIteration):
-            (self.frames['profiles']['STNID'] , \
-            self.frames['profiles']['current_record_chunk'] , \
-            self.frames['profiles']['current_record_index']) , \
-            self.frames['profiles']['current_record_mod'] = \
-                      self.frames['profiles']['records_iterator'].__next__(jump)
-        # except (StopIteration):
-        #     self.frames['profiles']['records_iterator'].close()
-        #     del( self.frames['profiles']['records_iterator'])
-        #     self.frames['profiles']['records_iterator'] = \
-        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
-        #     (self.frames['profiles']['STNID'] , \
-        #     self.frames['profiles']['current_record_index']) , \
-        #     self.frames['profiles']['current_record_mod'] = \
-        #                     self.frames['profiles']['records_iterator'].__next__()
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        self.update_record()
-
-    def prev_record(self,event=None):
-        self.next_record(jump=-1,event=event)
-
-    def update_record(self):
-        self.frames['profiles']['current_record_ini'] =  \
-            self.frames['profiles']['records_current_station_ini'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'],\
-                  self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon'] =  \
-            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'] , \
-                  self.frames['profiles']['current_record_index'])]
-
-        self.frames['profiles']['current_record_mod_stats'] = \
-                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
-                    self.frames['profiles']['STNID'], \
-                    self.frames['profiles']['current_record_chunk'], \
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_ini_pct'] = \
-                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        # frame
-        # note that the current station, record is the same as the stats frame for initialization
-
-        # select first 
-        #self.frames['profiles']['current_record_index'], \
-        #self.frames['profiles']['record_yaml_mod'] = \
-        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
-        #                   self.frames['stats']['current_record_index'])
-        self.frames['profiles']['record_yaml_mod'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_mod'], \
-               self.frames['profiles']['current_record_mod'].index_start,
-               self.frames['profiles']['current_record_mod'].index_end,
-               mode='mod')
-                                
-        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_ini'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_ini'], \
-               record_ini.index_start,
-               record_ini.index_end,
-                mode='ini')
-
-        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_obs_afternoon'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_afternoon'], \
-               record_afternoon.index_start,
-               record_afternoon.index_end,
-                mode='ini')
-
-
-        key = self.frames['worldmap']['inputkey']
-        # only redraw the map if the current world map has a time
-        # dimension
-        if 'time' in self.globaldata.datasets[key].page[key].dims:
-            self.goto_datetime_worldmap(
-                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                'after')
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap',
-                                                  'profiles'])
-        else:
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap_stations',
-                                                  'profiles'])
-
-    def abline(self,slope, intercept,axis):
-        """Plot a line from slope and intercept"""
-        #axis = plt.gca()
-        x_vals = np.array(axis.get_xlim())
-        y_vals = intercept + slope * x_vals
-        axis.plot(x_vals, y_vals, 'k--')
-
-    def plot(self):
-        import pylab as pl
-        from matplotlib.widgets import Button
-        import matplotlib.pyplot as plt
-        import matplotlib as mpl
-        '''
-        Definition of the axes for the sounding table stats
-        '''
-        
-        fig = pl.figure(figsize=(14,9))
-        axes = {} #axes
-        btns = {} #buttons
-
-        # frames, which sets attributes for a group of axes, buttens, 
-        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
-            label = 'stats_'+str(key)
-            axes[label] = fig.add_subplot(\
-                            len(self.frames['stats']['viewkeys']),\
-                            5,\
-                            5*ikey+1,label=label)
-            # Actually, the axes should be a part of the frame!
-            #self.frames['stats']['axes'] = axes[
-
-            # pointer to the axes' point data
-            axes[label].data = {}
-
-            # pointer to the axes' color fields
-            axes[label].fields = {}
-
-
-        fig.tight_layout()
-        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
-
-        label ='stats_colorbar'
-        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
-        axes[label].fields = {}
-
-        from matplotlib.colors import LinearSegmentedColormap
-        cdictpres = {'blue': (\
-                           (0.,    0.,  0.),
-                           (0.25,  0.25, 0.25),
-                           (0.5,  .70, 0.70),
-                           (0.75, 1.0, 1.0),
-                           (1,     1.,  1.),
-                           ),
-               'green': (\
-                           (0. ,   0., 0.0),
-                           (0.25,  0.50, 0.50),
-                           (0.5,  .70, 0.70),
-                           (0.75,  0.50, 0.50),
-                           (1  ,    0,  0.),
-                           ),
-               'red':  (\
-                          (0 ,  1.0, 1.0),
-                          (0.25 ,  1.0, 1.0),
-                           (0.5,  .70, 0.70),
-                          (0.75 , 0.25, 0.25),
-                          (1,    0., 0.),
-                          )}
-        
-        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-        label = 'times'
-               
-        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-
-
-        label = 'worldmap'
-               
-        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-        axes[label].lat = None
-        axes[label].lon = None
-
-        label = 'worldmap_colorbar'
-        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
-        axes[label].fields = {}
-
-        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
-        label = 'worldmap_stations'
-        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
-        axes[label].data = {}
-
-        fig.canvas.mpl_connect('pick_event', self.on_pick)
-        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
-
-
-        """ buttons definitions """
-        
-        label = 'bprev_dataset'
-        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous dataset')
-        btns[label].on_clicked(self.prev_dataset)
-
-        label = 'bnext_dataset'
-        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next dataset')
-        btns[label].on_clicked(self.next_dataset)
-
-        label = 'bprev_datetime'
-        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous datetime')
-        btns[label].on_clicked(self.prev_datetime)
-
-        label = 'bnext_datetime'
-        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next datetime')
-        btns[label].on_clicked(self.next_datetime)
-
-
-        label = 'bprev_station'
-        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous station')
-        btns[label].on_clicked(self.prev_station)
-
-        label = 'bnext_station'
-        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next station')
-        btns[label].on_clicked(self.next_station)
-
-        label = 'bprev_record'
-        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous record')
-        btns[label].on_clicked(self.prev_record)
-
-        label = 'bnext_record'
-        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next record')
-        btns[label].on_clicked(self.next_record)
-
-
-        # self.nstatsview = nstatsview
-        # self.statsviewcmap = statsviewcmap
-        self.fig = fig
-        self.axes = axes
-        self.btns = btns
-        self.tbox = {}
-        # self.hover_active = False
-
-        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
-        #                                transform=plt.gcf().transFigure)
-
-        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
-                                          transform=plt.gcf().transFigure)
-
-        label = 'air_ap:theta'
-        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
-
-        label = 'air_ap:q'
-        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
-
-        label = 'out:h'
-        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
-
-        label = 'out:theta'
-        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
-
-        label = 'out:q'
-        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
-
-        label = 'SEB'
-        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
-
-
-        self.hover_active = False
-        self.fig = fig
-        self.fig.show()
-        self.fig.canvas.draw()
-        self.refresh_plot_interface()
-
-
-    # def scan_stations(self):
-    #     blabla
-        
-
-
-    # def get_records(current_file):
-    #     records = pd.DataFrame()
-
-    #     # initial position
-    #     next_record_found = False
-    #     while(not next_record_found):
-    #         next_record_found = (current_file.readline() == '---\n')
-    #     next_tell = current_file.tell() 
-    #     end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #     while not end_of_file:
-    #         current_tell = next_tell
-    #         next_record_found = False
-    #         current_file.seek(current_tell)
-    #         while ( (not next_record_found) and (not end_of_file)):
-    #             current_line = current_file.readline()
-    #             next_record_found = (currentline == '---\n')
-    #             end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #         # we store the position of the next record
-    #         next_tell = current_file.tell() 
-    #         
-    #         # we get the current record. Unfortunately we need to reset the
-    #         # yaml record generator first.
-    #         current_yamlgen.close()
-    #         current_yamlgen = yaml.load_all(current_file)
-    #         current_file.seek(current_tell)
-    #         current_record_mod = current_yamlgen.__next__()
-    #     current_yamlgen.close()
-
-    #     return records
-
-       #      next_record_found = False
-       #      while(not record):
-       #          next_record_found = (self.current_file.readline() == '---\n')
-       #      self.current_tell0 = self.current_file.tell() 
-
-       #  
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell0 = self.current_file.tell() 
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell1 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell0)
-       #  self.r0 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell1)
-       #  next_record_found = False
-       #  while ( (not next_record_found) and (not end_of_file):
-       #      current_line = self.current_file.readline()
-       #      next_record_found = (currentline == '---\n')
-       #      end_of_file = (currentline == '') # an empty line means we are at the end
-
-       #  self.current_tell2 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell1)
-       #  self.r1 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell2)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell3 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell2)
-       #  self.r2 = self.current_yamlgen.__next__()
-
-       #  # go to position of next record in file
-       #  self.current_file.seek(self.current_tell3)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell4 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell3)
-       #  self.r3 = self.current_yamlgen.__next__()
- 
-       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
-
-    def goto_datetime_worldmap(self,DT,shift=None):
-        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
-            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
-            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
-            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
-                self.frames['worldmap']['iDT'] += 1
-            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
-                self.frames['worldmap']['iDT'] -= 1 
-            # for gleam, we take the values of the previous day
-            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
-                self.frames['worldmap']['iDT'] -= 2 
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-        #else:
-        #    self.frames['worldmap'].pop('DT')
-
-    def next_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def prev_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def next_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-    def prev_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-
-
-    def sel_dataset(self,inputkey):
-        self.frames['worldmap']['inputkey'] = inputkey
-        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
-        self.goto_datetime_worldmap(
-            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-            'after')# get nearest datetime of the current dataset to the profile
-        if "fig" in self.__dict__.keys():
-            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
-       
-    # def prev_station(self,event=None):
-    #     self.istation = (self.istation - 1) % self.stations.shape[0]
-    #     self.update_station()
-
-
-
-
-    #def update_datetime(self):
-    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
-    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
-    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
-    #        print(self.worldmapfocus['DT'])
-    #        self.refresh_plot_interface(only='worldmap')
-
-    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
-
-        #print('r1')
-        for argkey in args.keys():
-            self.__dict__[arg] = args[argkey]
-
-        axes = self.axes
-        tbox = self.tbox
-        frames = self.frames
-        fig = self.fig
- 
-        if (only is None) or ('worldmap' in only):
-            globaldata = self.globaldata
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
-            else:
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
-            keystotranspose = ['lat','lon']
-            for key in dict(datasetxr.dims).keys():
-                if key not in keystotranspose:
-                    keystotranspose.append(key)
-
-            datasetxr = datasetxr.transpose(*keystotranspose)
-            datasetxr = datasetxr.sortby('lat',ascending=False)
-
-            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
-            lonleft = lonleft - 360.
-            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
-            label = 'worldmap'
-            axes[label].clear()
-            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
-            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
-
-        if (only is None) or ('worldmap' in only):
-            #if 'axmap' not in self.__dict__ :
-            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
-            #else:
-
-            #stations = self.stations
-
-
-            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
-            #     resolution = 'l', 
-            # area_thresh = 0.1,
-            #     llcrnrlon=-180., llcrnrlat=-90.0,
-            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
-            # 
-            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
-            # self.gmap.drawcountries(color='white',linewidth=0.3)
-            # #self.gmap.fillcontinents(color = 'gray')
-            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
-            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
-            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
-            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # #self.ax5.shadedrelief()
-
-           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
-
-
-            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
-            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
-
-            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
-            if 'lev' in field.dims:
-                field = field.isel(lev=-1)
-
-            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
-            axes[label].axis('off')
-
-            from matplotlib import cm
-            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
-            
-            
-            title=frames['worldmap']['inputkey']
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
-            axes[label].set_title(title)
-
-            label ='worldmap_colorbar'
-            axes[label].clear()
-            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
-
-
-            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
-            # x,y = self.gmap(lons,lats)
-            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
-            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
-
-        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
-
-            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
-            store_xlim = {}
-            store_ylim = {}
-            for ikey, key in enumerate(statskeys_out):
-                if (only is not None) and ('stats_lightupdate' in only):
-                    store_xlim[key] = axes['stats_'+key].get_xlim()
-                    store_ylim[key] = axes['stats_'+key].get_ylim()
-                self.axes['stats_'+key].clear()    
-
-            label = 'times'
-            self.axes[label].clear()
-
-            key = 'dthetadt'
-            x = self.frames['stats']['records_all_stations_ini']['datetime']
-            #print(x)
-            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-            #print(y)
-            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            #print(z)
-
-            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
-            self.axes[label].data[label] = self.axes[label].scatter(x.values,
-                                                                    y.values,
-                                                                    c=z.values,
-                                                                    cmap=self.statsviewcmap,
-                                                                    s=2,
-                                                                    vmin=0.,
-                                                                    vmax=1.,
-                                                                    alpha=alpha_cloud_pixels)
-
-            
-            x = self.frames['stats']['records_current_station_ini']['datetime']
-            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-
-            x = self.frames['profiles']['records_current_station_ini']['datetime']
-            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
-            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
-
-            for ikey, key in enumerate(statskeys_out):
-
-                # show data of all stations
-                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_all_stations_mod_stats'][key]
-                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                qvalmax = x.quantile(0.999)
-                qvalmin = x.quantile(0.001)
-                print('applying extra filter over extreme values for plotting stats')
-                selx = (x >= qvalmin) & (x < qvalmax)
-                sely = (x >= qvalmin) & (x < qvalmax)
-                x = x[selx & sely]
-                y = y[selx & sely]
-                z = z[selx & sely]
-                self.axes['stats_'+key].data['stats_'+key] = \
-                       self.axes['stats_'+key].scatter(x,y, c=z,\
-                                cmap=self.statsviewcmap,\
-                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
-
-                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_current_station_mod_stats'][key]
-                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['profiles']['records_current_station_mod_stats'][key]
-                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
-
-                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
-                y = self.frames['stats']['current_record_mod_stats'][key]
-                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
-                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
-                    axes['stats_'+key].annotate(text, \
-                                               xy=(x,y),\
-                                               xytext=(0.05,0.05),\
-                                               textcoords='axes fraction',\
-                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
-                                               color='white',\
-                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
-                # self.axes['stats_'+key].data[key+'_current_record'] = \
-                #        self.axes['stats_'+key].scatter(x,y, c=z,\
-                #                 cmap=self.statsviewcmap,\
-                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
-
-                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
-                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
-                # # highlight data for curent station
-                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
-
-                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
-
-                if ikey == len(statskeys_out)-1:
-                    self.axes['stats_'+key].set_xlabel('external')
-                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
-                axes['stats_'+key].set_ylabel('model')
-
-
-                if (only is not None) and ('stats_lightupdate' in only):
-                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
-                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
-                else:
-                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
-                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
-                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
-                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
-                self.abline(1,0,axis=self.axes['stats_'+key])
-
-        if (only is None) or ('stats_colorbar' in only):
-            label ='stats_colorbar'
-            axes[label].clear()
-            import matplotlib as mpl
-            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
-            self.axes[label].fields[label] = \
-             mpl.colorbar.ColorbarBase(self.axes[label],\
-                        orientation='horizontal',\
-                        label="percentile of "+self.frames['worldmap']['inputkey'],
-                        alpha=1.,
-                                cmap=self.statsviewcmap,\
-                                       norm=norm
-                         )
-
-        #print('r1')
-        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
-            #print('r2')
-            label = 'worldmap_stations'
-            axes[label].clear()
-            
-            stations = self.frames['worldmap']['stations'].table
-            globaldata = self.globaldata
-            
-            key = label
-
-            #print('r3')
-            if (stations is not None):
-                xlist = []
-                ylist = []
-                #print('r4')
-                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
-            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    xlist.append(x)
-                    ylist.append(y)
-                #picker is needed to make it clickable (pick_event)
-                axes[label].data[label] = axes[label].scatter(xlist,ylist,
-                                                              c='r', s=15,
-                                                              picker = 15,
-                                                              label=key,
-                                                              edgecolor='k',
-                                                              linewidth=0.8)
-
-            # cb.set_label('Wilting point [kg kg-3]')
-                #print('r5')
-
-                
-            #     xseries = []
-            #     yseries = []
-            #     for iSTN,STN in stations.iterrows():
-            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
-            #         xseries.append(x)                    
-            #         yseries.append(y)
-            #         
-            #         
-            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
-                    
-                if ('current_station' in frames['worldmap']):
-                    #print('r5')
-                    STN = frames['stats']['current_station']
-                    STNID = frames['stats']['STNID']
-                    #print('r5')
-
-                    x,y = len(axes['worldmap'].lon)* \
-                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
-                          len(axes['worldmap'].lat)* \
-                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    #print('r6')
-                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
-                    #                          == \
-                    #                          self.frames['worldmap']['STNID'])\
-                    #                         & \
-                    #                         (self.seltablestats['DT'] \
-                    #                          == self.axes['statsview0].focus['DT']) \
-                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
-                    #print('r7')
-                    text = 'STNID: '+ format(STNID,'10.0f') + \
-                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
-                            ', LON: '+format(STN['longitude'],'3.3f')+ \
-                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
-
-                            #+', VAL: '+format(VAL,'.3e')
-
-                    axes[label].scatter(x,y, c='r', s=30,\
-                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
-                    #print('r8')
-            
-                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
-                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
-                    #colorstation = max((min((1.,colorstation)),0.))
-                    colorstation =0.2
-                    from matplotlib import cm
-                    axes[label].annotate(text,
-                                         xy=(x,y),
-                                         xytext=(0.05,0.05),
-                                         textcoords='axes fraction', 
-                                         bbox=dict(boxstyle="round",
-                                         fc = cm.viridis(colorstation)),
-                                         arrowprops=dict(arrowstyle="->",
-                                                         linewidth=1.1),
-                                         color='white' if colorstation < 0.5 else 'black')
-                    #print('r9')
-
-                    # #pos = sc.get_offsets()[ind["ind"][0]]
-                    # 
-                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
-                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
-                    # axes[label].data[label+'statannotate'].set_text(text)
-                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
-                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
-            #print('r9')
-            axes[label].axis('off')
-            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
-            axes[label].set_ylim((len(axes['worldmap'].lat),0))
-            #print('r10')
-
-        if (only is None) or ('profiles' in only): 
-            #print('r11')
-
-            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
-            # # self.update_station(goto_first_sounding=False)
-            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
-            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
-            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
-            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
-
-            label = 'air_ap:theta'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-                # +\
-                # ' -> '+ \
-                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-            
-            
-            
-            
-            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-            #print('r12')
-
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
-            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
-            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
-            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            #print('r13')
-            # 
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r14')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-
-            #print('r15')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-                          
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r16')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r17')
-            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
-            print(hmax)
-            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
-            if valid_mod:
-
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="mod "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                                 +'LT')
-
-            #print('r18')
-            axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('theta [K]')
-
-            label = 'air_ap:q'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-
-            #print('r19')
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            if valid_mod:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            else:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            # 
-            #print('r20')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r21')
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            #print('r23')
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r24')
-            if valid_mod:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="fit ")#+\
-                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
-                             #+'LT')
-            #print('r25')
-            #axes[label].legend()
-
-            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            #axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('q [kg/kg]')
-
-            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-
-            # #pl.subplots_adjust(right=0.6)
-
-            # label = 'q_pro'
-            # axes[label].clear()
-
-            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
-            # 
-            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
-            # 
-            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
-
-            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
-            # #pl.subplots_adjust(right=0.6)
-            # axes[label].set_xlabel('specific humidity [kg/kg]')
- 
-
-            #print('r26')
-            time = self.frames['profiles']['record_yaml_mod'].out.time
-            for ilabel,label in enumerate(['h','theta','q']):
-                axes["out:"+label].clear()
-                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
-                axes["out:"+label].set_ylabel(label)
-                if ilabel == 2:
-                    axes["out:"+label].set_xlabel('local sun time [h]')
-                
-            #print('r27')
-            label = 'SEB'
-            axes[label].clear()
-            
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
-            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
-            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
-            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
-                
-            #print('r28')
-            
-            axes[label].legend()
-            
-            #         for ax in self.fig_timeseries_axes:
-#             ax.clear()
-#         
-#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
-#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
-#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
-#         #print(self.morning_sounding.c4gl.out.Swin)
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
-#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
-#         self.fig_timeseries_axes[3].legend()
-#         self.fig.canvas.draw()
-            
-
-
-
-
-
-
-        #self.ready()
-        #print('r29')
-        fig.canvas.draw()
-        #fig.show()
-
-        self.axes = axes
-        self.tbox = tbox
-        self.fig = fig
-
-    def on_pick(self,event):
-        #print("HELLO")
-        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
-        #self.axes['theta_pro'].clear()
-        #self.axes['q_pro'].clear()
-        
-
-        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
-        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
-        keys_to_axes = {}
-        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
-
-        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
-        keys_to_axes['worldmap'] = 'worldmap'
-        
-        axes = self.axes
-        #nstatsview = self.nstatsview
-        #statsviewcmap = self.statsviewcmap
-        stations = self.frames['worldmap']['stations'].table
-
-
-        #print("p1")
-        current = event
-        artist = event.artist
-        
-        selkey = artist.get_label()
-        
-        #print(keys_to_axes)
-        
-        label = keys_to_axes[selkey]
-        #print("HELLO",selkey,label)
-
-        # # Get to know in which axes we are
-        # label = None
-        # for axeskey in axes.keys():
-        #     if event.inaxes == axes[axeskey]:
-        #         label = axeskey
-        #         
-
-        # cont, pos = None, None
-        
-        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
-        ind = event.ind
-        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
-        d = axes[label].collections[0]
-        #d.set_offset_position('data')
-        xy = d.get_offsets()
-        x, y =  xy[:,0],xy[:,1]
-        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
-
-        #print("p2")
-        if len(ind) > 0:
-            #print("p3")
-            pos = x[ind[0]], y[ind[0]]
-
-            #if label[:-1] == 'statsview':
-            #    #seltablestatsstdrel = self.seltablestatsstdrel
-            #    #seltablestatspct = self.seltablestatspct
-
-            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-            #    
-            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
-            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-            #    
-            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
-            #el
-            if (label == 'worldmap') or (label == 'worldmap_stations'):
-                self.hover_active = False
-                if (self.frames['worldmap']['STNID'] !=
-                    self.frames['profiles']['STNID']):
-                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
-                # so we just need to perform update_station
-                    self.update_station()
-            elif (label[:5] == 'stats'):
-
-                self.hover_active = False
-                if (self.frames['stats']['STNID'] !=
-                self.frames['profiles']['STNID']) or \
-                   (self.frames['stats']['current_record_chunk'] != 
-                    self.frames['profiles']['current_record_chunk']) or \
-                   (self.frames['stats']['current_record_index'] != 
-                    self.frames['profiles']['current_record_index']):
-
-
-
-                    for key in ['STNID','current_station','stations_iterator']: 
-                        self.frames['worldmap'][key] = self.frames['stats'][key] 
-
-                    for key in self.frames['stats'].keys():
-                        self.frames['profiles'][key] = self.frames['stats'][key]
-
-                    STNID = self.frames['profiles']['STNID']
-                    chunk = self.frames['profiles']['current_record_chunk']
-                    if 'current_station_file_ini' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_ini'].close()
-                    self.frames['profiles']['current_station_file_ini'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-                    if 'current_station_file_mod' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_mod'].close()
-                    self.frames['profiles']['current_station_file_mod'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_afternoon'].close()
-                    self.frames['profiles']['current_station_file_afternoon'] = \
-                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-                    # go to hovered record of current station
-                    self.frames['profiles']['records_iterator'] = \
-                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # ... and go to the record of the profile window (last one that
-                    # was picked by the user)
-                    found = False
-                    EOF = False
-                    while (not found) and (not EOF):
-                        try:
-                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
-                            #print("hello*")
-                            #print(self.frames['profiles']['current_record_index'])
-                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
-                               (index == self.frames['profiles']['current_record_index']) and \
-                               (STNID == self.frames['profiles']['STNID']):
-                                #print('found!')
-                                found = True
-                        except StopIteration:
-                            EOF = True
-                    if found:
-                        self.frames['stats']['current_record_mod'] = record
-                        self.frames['stats']['current_record_chunk'] = chunk
-                        self.frames['stats']['current_record_index'] = index
-                    # # for the profiles we make a distinct record iterator, so that the
-                    # # stats iterator can move independently
-                    # self.frames['profiles']['records_iterator'] = \
-                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # (self.frames['profiles']['STNID'] , \
-                    # self.frames['profiles']['current_record_index']) , \
-                    # self.frames['profiles']['current_record_mod'] = \
-                    #                 self.frames['profiles']['records_iterator'].__next__()
-
-
-                    # for the profiles we make a distinct record iterator, so that the
-                    # stats iterator can move independently
-
-                    self.update_record()
-
-
-
-    def on_plot_hover(self,event):
-        axes = self.axes
-        #print('h1')
-
-        # Get to know in which axes we are
-        label = None
-        for axeskey in axes.keys():
-            if event.inaxes == axes[axeskey]:
-                label = axeskey
-                
-        #print('h2')
-
-        cont, pos = None, None
-        #print (label)
-        
-        if label is not None:
-            if  ('data' in axes[label].__dict__.keys()) and \
-                (label in axes[label].data.keys()) and \
-                (axes[label].data[label] is not None):
-                
-                #print('h3')
-                cont, ind =  axes[label].data[label].contains(event)
-                selkey = axes[label].data[label].get_label()
-                if len(ind["ind"]) > 0:
-                    #print('h4')
-                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
-                    #print('pos',pos,selkey)
-
-
-                    #if label[:-1] == 'statsview':
-                    #    seltablestatsstdrel = self.seltablestatsstdrel
-                    #    seltablestatspct = self.seltablestatspct
-
-                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
-                    #    self.hover_active = True
-                    #    
-                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
-                    #    
-                    #el
-                    #print(label[:5])
-                    if (label[:5] == 'stats') or (label == 'times'):
-                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
-                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
-                        
-
-                        if label[:5] == 'stats':
-                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                            (self.frames['stats']['STNID'] ,
-                             self.frames['stats']['current_record_chunk'], 
-                             self.frames['stats']['current_record_index']) = \
-                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-                        # elif label[:5] == 'stats':
-                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
-                        #     (self.frames['stats']['STNID'] ,
-                        #      self.frames['stats']['current_record_chunk'], 
-                        #      self.frames['stats']['current_record_index']) = \
-                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-
-
-                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-                        
-                        # # TO TEST: should be removed, since it's is also done just below
-                        # self.frames['stats']['stations_iterator'] = \
-                        #     self.frames['worldmap']['stations_iterator'] 
-                
-                
-                        # self.goto_datetime_worldmap(
-                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-                        #     'after')
-
-
-                        # scrolling to the right station
-                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                        EOF = False
-                        found = False
-                        while (not found and not EOF):
-                            if (STNID == self.frames['stats']['STNID']):
-                                   found = True 
-                            if not found:
-                                try:
-                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                                except (StopIteration):
-                                    EOF = True
-                        if found:
-                        #    self.frames['stats']['STNID'] = STNID
-                            self.frames['stats']['current_station'] =  station
-
-                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
-                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
-
-
-                        # generate index of the current station
-                        self.frames['stats']['records_current_station_index'] = \
-                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                             == self.frames['stats']['STNID'])
-
-
-                        tab_suffixes = \
-                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            self.frames['stats']['records_current_station'+tab_suffix] = \
-                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-
-                        # go to hovered record of current station
-                        self.frames['stats']['records_iterator'] = \
-                                        records_iterator(self.frames['stats']['records_current_station_mod'])
-
-
-                        # ... and go to the record of the profile window (last one that
-                        # was picked by the user)
-                        found = False
-                        EOF = False
-                        while (not found) and (not EOF):
-                            try:
-                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                                #print("hello*")
-                                #print(self.frames['profiles']['current_record_index'])
-                                if (index == self.frames['stats']['current_record_index']) and \
-                                   (chunk == self.frames['stats']['current_record_chunk']) and \
-                                   (STNID == self.frames['stats']['STNID']):
-                                    #print('found!')
-                                    found = True
-                            except StopIteration:
-                                EOF = True
-                        if found:
-                            #print('h5')
-                            self.frames['stats']['current_record_mod'] = record
-                            self.frames['stats']['current_record_chunk'] = chunk
-                            self.frames['stats']['current_record_index'] = index
-
-                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
-                        tab_suffixes = \
-                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            #print(tab_suffix)
-                            #print(self.frames['stats']['records_current_station'+tab_suffix])
-                            self.frames['stats']['current_record'+tab_suffix] =  \
-                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                      (self.frames['stats']['STNID'] , \
-                                       self.frames['stats']['current_record_chunk'] , \
-                                       self.frames['stats']['current_record_index'])]
-
-
-                        self.hover_active = True
-                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                        # print('h13')
-                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
-                        #     self.goto_datetime_worldmap(
-                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                        #         'after')
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap',
-                        #                                           'profiles'])
-                        # else:
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap_stations',
-                        #                                           'profiles'])
-
-
-
-                    elif label in ['worldmap_stations','worldmap']:
-                        #print('h5')
-
-                        if (self.axes['worldmap'].lat is not None) and \
-                           (self.axes['worldmap'].lon is not None):
-
-
-                            #self.loading()
-                            self.fig.canvas.draw()
-                            self.fig.show()
-
-
-                            # get position of 
-                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
-                                                                 self.axes['worldmap'].lat[0]) + \
-                                           self.axes['worldmap'].lat[0],4)
-                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
-                                                                 self.axes['worldmap'].lon[0]) + \
-                                           self.axes['worldmap'].lon[0],4)
-                        
-                            stations = self.frames['worldmap']['stations'].table
-                            #print('h7')
-                        
-                            #reset stations iterator:
-                            # if 'stations_iterator' in self.frames['worldmap'].keys():
-                            #     self.frames['worldmap']['stations_iterator'].close()
-                            #     del(self.frames['worldmap']['stations_iterator'])
-                            # if 'stations_iterator' in self.frames['stats'].keys():
-                            #     self.frames['stats']['stations_iterator'].close()
-                            #     del(self.frames['stats']['stations_iterator'])
-                            self.frames['worldmap']['stations_iterator'] =\
-                               stations_iterator(self.frames['worldmap']['stations'])
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                            EOF = False
-                            found = False
-                            while (not found and not EOF):
-                                #print('h8',station.latitude,latmap)
-                                #print('h8',station.longitude,lonmap)
-                                if (round(station.latitude,3) == round(latmap,3)) and \
-                                    (round(station.longitude,3) == round(lonmap,3)):
-                                       found = True 
-                                if not found:
-                                    try:
-                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                                    except (StopIteration):
-                                        EOF = True
-                            if found:
-                                self.frames['worldmap']['STNID'] = STNID
-                                self.frames['worldmap']['current_station'] = \
-                                        station
-                        
-                            self.frames['stats']['stations_iterator'] = \
-                                self.frames['worldmap']['stations_iterator'] 
-                            #print('h8')
-                            # inherit station position for the stats frame...
-                            for key in self.frames['worldmap'].keys():
-                                self.frames['stats'][key] = self.frames['worldmap'][key]
-                                
-                            ## fetch records of current station...
-                            #self.frames['stats']['records_current_station_mod'] =\
-                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                            # ... and their indices
-                            self.frames['stats']['records_current_station_index'] = \
-                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                                     == \
-                                     self.frames['stats']['current_station'].name)
-
-
-                            tab_suffixes = \
-                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['records_current_station'+tab_suffix] = \
-                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-                            # ... create a record iterator ...
-                            #self.frames['stats']['records_iterator'].close()
-                            del(self.frames['stats']['records_iterator'])
-                            self.frames['stats']['records_iterator'] = \
-                                self.frames['stats']['records_current_station_mod'].iterrows()
-
-
-
-                        
-                            #print('h9')
-                            # ... and go to to the first record of the current station
-                            (self.frames['stats']['STNID'] , \
-                             self.frames['stats']['current_record_chunk'] , \
-                             self.frames['stats']['current_record_index']) , \
-                            self.frames['stats']['current_record_mod'] = \
-                                self.frames['stats']['records_iterator'].__next__()
-                        
-
-
-
-                            #print('h10')
-                            # cash the current record
-                            tab_suffixes = \
-                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['current_record'+tab_suffix] =  \
-                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                          (self.frames['stats']['STNID'] , \
-                                           self.frames['stats']['current_record_chunk'] , \
-                                           self.frames['stats']['current_record_index'])]
-
-                            #print('h11')
-                            
-                            self.hover_active = True
-                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                            #print('h13')
-
-                        
-
-            #if (stations is not None):
-            #    for iSTN,STN in stations.iterrows():
-            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
-            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
-
-        # self.fig.show()
- 
-        # we are hovering on nothing, so we are going back to the position of
-        # the profile sounding
-        if pos is None:
-            if self.hover_active == True:
-                #print('h1*')
-                
-                #self.loading()
-                # to do: reset stations iterators
-
-                # get station and record index from the current profile
-                for key in ['STNID', 'current_station']:
-                    self.frames['stats'][key] = self.frames['profiles'][key]
-
-                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
-                self.frames['stats']['current_station'] = \
-                        self.frames['profiles']['current_station']
-                #print('h3a*')
-                self.frames['stats']['records_current_station_mod'] = \
-                        self.frames['profiles']['records_current_station_mod']
-                #print('h3b*')
-
-                # the next lines recreate the records iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-
-                # reset stations iterator...
-                #self.frames['stats']['records_iterator'].close()
-                del(self.frames['stats']['records_iterator'])
-                self.frames['stats']['records_iterator'] = \
-                    self.frames['stats']['records_current_station_mod'].iterrows()
-                #print('h4*')
-
-                # ... and go to the record of the profile window (last one that
-                # was picked by the user)
-                found = False
-                EOF = False
-                while (not found) and (not EOF):
-                    try:
-                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                        #print("hello*")
-                        #print(self.frames['profiles']['current_record_index'])
-                        #print(self.frames['profiles']['STNID'])
-                        #print(STNID,index)
-                        if (index == self.frames['profiles']['current_record_index']) and \
-                            (chunk == self.frames['profiles']['current_record_chunk']) and \
-                            (STNID == self.frames['profiles']['STNID']):
-                            #print('found!')
-                            found = True
-                    except StopIteration:
-                        EOF = True
-                if found:
-                    #print('h5*')
-                    self.frames['stats']['current_record_mod'] = record
-                    self.frames['stats']['current_record_chunk'] = chunk
-                    self.frames['stats']['current_record_index'] = index
-
-                #print('h6*')
-
-
-
-                # # fetch records of current station...
-                # self.frames['stats']['records_current_station_mod'] =\
-                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                # ... and their indices
-                self.frames['stats']['records_current_station_index'] = \
-                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                         == \
-                         self.frames['stats']['current_station'].name)
-
-
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['records_current_station'+tab_suffix] = \
-                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-                
-
-                # cash the records of the current stations
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['current_record'+tab_suffix] =  \
-                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                              (self.frames['stats']['STNID'] , \
-                               self.frames['stats']['current_record_chunk'] , \
-                               self.frames['stats']['current_record_index'])]
-
-
-                # the next lines recreate the stations iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-                #print('h7*')
-
-                # reset the stations iterators
-                for framekey in ['stats','worldmap']:
-                    ##print(framekey)
-                    if 'stations_iterator' in self.frames[framekey]:
-                        #self.frames[framekey]['stations_iterator'].close()
-                        del(self.frames[framekey]['stations_iterator'])
-
-                self.frames['worldmap']['current_station'] = \
-                        self.frames['profiles']['current_station']
-
-                #recreate the stations iterator for the worldmap...
-                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-
-                # ... and go the position of the profile
-                #print('h8*')
-                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                EOF = False
-                found = False
-                while (not found and not EOF):
-                    if STNID == self.frames['profiles']['STNID'] :
-                        found = True 
-                    if not found:
-                        try:
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                        except (StopIteration):
-                            EOF = True
-                if found:
-                    self.frames['worldmap']['current_station'] = station
-                    self.frames['worldmap']['STNID'] = STNID
-                #print('h9*')
-                self.frames['stats']['stations_iterator'] = \
-                    self.frames['worldmap']['stations_iterator'] 
-
-                # the stats window now inherits the current station from the
-                # worldmap
-                for key in ['STNID','current_station','stations_iterator']: 
-                    self.frames['stats'][key] = self.frames['worldmap'][key] 
-                #print('h10*')
-
-                # # we now only need inherit station position and go to first record
-                # for key in self.frames['worldmap'].keys():
-                #     self.frames['stats'][key] = self.frames['worldmap'][key]
-
-                # self.frames['stats']['records_current_station'] =\
-                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
-
-                # #print(self.frames['stats']['records_current_station'])
-                # self.frames['stats']['records_iterator'] = \
-                #                 self.frames['stats']['records_current_station'].iterrows()
-                # (self.frames['stats']['STNID'] , \
-                # self.frames['stats']['current_record_index']) , \
-                # self.frames['stats']['current_record_mod'] = \
-                #                 self.frames['stats']['records_iterator'].__next__()
-                
-
-
-
-
-
-
-                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
-                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
-                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
-                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-                self.hover_active = False
-                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
-    # def loading(self):
-    #     self.tbox['loading'].set_text('Loading...')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-    #     sleep(0.1)
-    # def ready(self):
-    #     self.tbox['loading'].set_text('Ready')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-
-
-
diff --git a/lib/model.py b/lib/model.py
deleted file mode 100644
index 8760411..0000000
--- a/lib/model.py
+++ /dev/null
@@ -1,2214 +0,0 @@
-# 
-# CLASS
-# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
-# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
-# Copyright (c) 2011-2015 Chiel van Heerwaarden
-# Copyright (c) 2011-2015 Bart van Stratum
-# Copyright (c) 2011-2015 Kees van den Dries
-# 
-# This file is part of CLASS
-# 
-# CLASS is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published bygamma
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-# 
-# CLASS is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-# 
-# You should have received a copy of the GNU General Public License
-# along with CLASS.  If not, see .
-#
-
-import copy as cp
-import numpy as np
-import sys
-import warnings
-import pandas as pd
-from ribtol_hw import zeta_hs2 , funcsche
-import logging
-#from SkewT.thermodynamics import Density
-#import ribtol
-
-grav = 9.81
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-
-def qsat(T,p):
-    return 0.622 * esat(T) / p
-
-
-def ribtol(Rib, zsl, z0m, z0h): 
-    Rib = np.float64(Rib)
-    zsl = np.float64(zsl)
-    z0m = np.float64(z0m)
-    z0h = np.float64(z0h)
-    #print(Rib,zsl,z0m,z0h)
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    #print(Rib,zsl,z0m,z0h)
-    while (abs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-        #print(L,fx/fxdif)
-        if(abs(L) > 1e12):
-            break
-
-    return L
-  
-def psim(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psim = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-  
-def psih(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * np.log( (1. + x*x) / 2.)
-        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
- 
-class model:
-    def __init__(self, model_input = None,debug_level=None):
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        self.logger = logging.getLogger('model')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        """ initialize the different components of the model """ 
-
-        if model_input is not None:
-            # class4gl style input
-            if 'pars' in model_input.__dict__.keys():
-
-                # we make a reference to the full input first, so we can dump it
-                # afterwards
-                self.input_c4gl = model_input
-
-                # we copy the regular parameters first. We keep the classical input
-                # format as self.input so that we don't have to change the entire
-                # model code.
-                self.input = cp.deepcopy(model_input.pars)
-
-                # we copy other sections we are interested in, such as profile
-                # data, and store it also under input
-
-                # I know we mess up a bit the structure of the class4gl_input, but
-                # we will make it clean again at the time of dumping data
-
-                # So here, we copy the profile data into self.input
-                # 1. Air circulation data 
-                if 'sw_ac' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ac']:
-                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
-                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
-
-                    # correct pressure of levels according to surface pressure
-                    # error (so that interpolation is done in a consistent way)
-
-                    p_e = self.input.Ps - self.input.sp
-                    for irow in self.input.air_ac.index[::-1]:
-                       self.input.air_ac.p.iloc[irow] =\
-                        self.input.air_ac.p.iloc[irow] + p_e
-                       p_e = p_e -\
-                       (self.input.air_ac.p.iloc[irow]+p_e)/\
-                        self.input.air_ac.p.iloc[irow] *\
-                        self.input.air_ac.delpdgrav.iloc[irow]*grav
-
-
-
-                # 2. Air circulation data 
-                if 'sw_ap' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ap']:
-                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
-
-            # standard class input
-            else:
-                self.input = cp.deepcopy(model_input)
-
-    def load_yaml_dict(self,yaml_dict):
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                for keydata,value in data.items():
-                    self.__dict__[keydata] = value
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            #elif key == 'sources':
-            #    self.__dict__[key] = data
-            elif key == 'out':
-                # lets convert it to a list of dictionaries
-                dictouttemp = pd.DataFrame(data).to_dict('list')
-            else: 
-                 warnings.warn("Key '"+key+"' is be implemented.")
-            #     self.__dict__[key] = data
-
-
-        self.tsteps = len(dictouttemp['h'])
-        self.out = model_output(self.tsteps)
-        for keydictouttemp in dictouttemp.keys():
-            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
-
-
-  
-    def run(self):
-        # initialize model variables
-        self.init()
-  
-        # time integrate model 
-        #for self.t in range(self.tsteps):
-        while self.t < self.tsteps:
-          
-            # time integrate components
-            self.timestep()
-  
-        # delete unnecessary variables from memory
-        self.exitmodel()
-    
-    def init(self):
-        # assign variables from input data
-        # initialize constants
-        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
-        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        self.rho        = 1.2                   # density of air [kg m-3]
-        self.k          = 0.4                   # Von Karman constant [-]
-        self.g          = 9.81                  # gravity acceleration [m s-2]
-        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-        self.bolz       = 5.67e-8               # Bolzman constant [-]
-        self.rhow       = 1000.                 # density of water [kg m-3]
-        self.S0         = 1368.                 # solar constant [W m-2]
-
-        # A-Gs constants and settings
-        # Plant type:       -C3-     -C4-
-        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
-        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
-        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
-        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
-        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
-        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
-        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
-        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
-        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
-        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
-        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
-
-        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
-        self.mair       =  28.9;                # molecular weight air [g mol -1]
-        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
-
-        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
-        self.wmax       =  0.55;                # upper reference value soil water [-]
-        self.wmin       =  0.005;               # lower reference value soil water [-]
-        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
-        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
-
-        # Read switches
-        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
-        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
-        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
-        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
-        self.sw_sl      = self.input.sw_sl      # surface layer switch
-        self.sw_rad     = self.input.sw_rad     # radiation switch
-        self.sw_ls      = self.input.sw_ls      # land surface switch
-        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
-        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
-
-        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
-        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
-        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-  
-        # initialize mixed-layer
-        self.h          = self.input.h          # initial ABL height [m]
-        self.Ps         = self.input.Ps         # surface pressure [Pa]
-        self.sp         = self.input.sp         # This is also surface pressure
-                                                #but derived from the global data [Pa]
-        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
-        self.ws         = None                  # large-scale vertical velocity [m s-1]
-        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
-        self.we         = -1.                   # entrainment velocity [m s-1]
-       
-         # Temperature 
-        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
-        
-        
-        self.substep    = False
-        self.substeps   = 0
-
-
-
-        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
-        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
-        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
-        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
-        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
- 
-        self.wstar      = 0.                    # convective velocity scale [m s-1]
- 
-        # 2m diagnostic variables 
-        self.T2m        = None                  # 2m temperature [K]
-        self.q2m        = None                  # 2m specific humidity [kg kg-1]
-        self.e2m        = None                  # 2m vapor pressure [Pa]
-        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
-        self.u2m        = None                  # 2m u-wind [m s-1]
-        self.v2m        = None                  # 2m v-wind [m s-1]
- 
-        # Surface variables 
-        self.thetasurf  = self.input.theta      # surface potential temperature [K]
-        self.thetavsurf = None                  # surface virtual potential temperature [K]
-        self.qsurf      = None                  # surface specific humidity [g kg-1]
-
-        # Mixed-layer top variables
-        self.P_h        = None                  # Mixed-layer top pressure [pa]
-        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
-        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
-        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
-        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
-        self.dz_h       = None                  # Transition layer thickness [-]
-        self.lcl        = None                  # Lifting condensation level [m]
-
-        # Virtual temperatures and fluxes
-        self.thetav     = None                  # initial mixed-layer potential temperature [K]
-        self.dthetav    = None                  # initial virtual temperature jump at h [K]
-        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
-        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
-       
-        
-        
-        
-        
-        
-        # Moisture 
-        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
-
-        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
-        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
-        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
-  
-        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
-        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
-        self.e          = None                  # mixed-layer vapor pressure [Pa]
-        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
-        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
-      
-        
-        
-        # CO2
-        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
-        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
-        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
-        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
-        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
-        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
-        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
-        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
-        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
-        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
-       
-        # Wind 
-        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
-        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
-        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = self.input.advu       # advection of u-wind [m s-2]
-        
-        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
-        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = self.input.advv       # advection of v-wind [m s-2]
-         
-  # BEGIN -- HW 20170606
-        # z-coordinate for vertical profiles of stratification above the mixed-layer height
-
-        if self.sw_ac:
-        # this is the data frame with the grided profile on the L60 grid
-        # (subsidence, and advection) 
-            self.air_ac      = self.input.air_ac  # full level air circulation
-                                                  # forcing
-            # self.air_ach     = self.input.air_ach # half level air circulation
-            #                                       # forcing
-            
-
-        if self.sw_ap:
-        # this is the data frame with the fitted profile (including HAGL,
-        # THTA,WSPD, SNDU,WNDV PRES ...)
-            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
-
-            # just for legacy reasons...
-            if 'z' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
-            if 'p' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
-
-            indexh = np.where(self.air_ap.z.values == self.h)
-            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
-                raise ValueError("Error input profile consistency: mixed- \
-                                 layer height needs to be equal to the second \
-                                 and third \
-                                 level of the vertical profile input!")
-            # initialize q from its profile when available
-            p_old = self.Ps
-            p_new = self.air_ap.p[indexh[0][0]]
-            
-            if ((p_old is not None) & (p_old != p_new)):
-                print("Warning: Ps input was provided ("+str(p_old)+\
-                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
-                    +str(p_new)+"Pa).")
-                                    
-            self.Ps = p_new
-            # these variables/namings are more convenient to work with in the code
-            # we will update the original variables afterwards
-            #self.air_ap['q'] = self.air_ap.QABS/1000.
-
-            self.air_ap = \
-                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
-            # we require the temperature fields, since we need to consider
-            # advection
-            # if self.sw_ac:
-            #     #self.air_ap['theta'] = self.air_ap['t'] *
-
-            #     # we consider self.sp in case of air-circulation input (for
-            #     # consistence)
-            #     self.air_ap['t'] = \
-            #                 self.air_ap.theta *  \
-            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
-            # else:
-            # we consider self.Ps in case of balloon input only 
-            self.air_ap = self.air_ap.assign(t = lambda x: \
-                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
-
-            #self.air_ap['theta'] = self.air_ap.THTA
-            if 'u' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
-            if 'v' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
-
-            for var in ['theta','q','u','v']:
-
-                
-                if self.air_ap[var][1] != self.air_ap[var][0]:
-                    raise ValueError("Error input profile consistency: two \
-                                     lowest profile levels for "+var+" should \
-                                     be equal.")
-                
-                # initialize the value from its profile when available
-                value_old = self.__dict__[var]
-                value_new = self.air_ap[var][indexh[0][0]]
-                
-                if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided \
-                                     ("+str(value_old)+ "kg kg-1), \
-                                     but it is now overwritten by the first \
-                                     level (index 0) of air_ap]var\ which is \
-                                     different (" +str(value_new)+"K).")
-                                        
-                self.__dict__[var] = value_new
-
-                # make a profile of the stratification 
-                # please note that the stratification between z_pro[i] and
-                # z_pro[i+1] is given by air_ap.GTHT[i]
-
-                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
-                # np.gradient(self.z_pro)
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
-
-
-                self.__dict__['gamma'+var] = \
-                    self.air_ap['gamma'+var][np.where(self.h >= \
-                                                     self.air_ap.z)[0][-1]]
-
-
-
-        # the variable p_pro is just for diagnosis of lifted index
-            
-            
-
-            # input Ph is wrong, so we correct it according to hydrostatic equation
-            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-
-            #if self.sw_ac:
-                # note that we use sp as surface pressure, which is determined
-                # from era-interim instead of the observations. This is to
-                # avoid possible failure of the interpolation routine
-                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
-                #                          + \
-                #                          list(self.air_ap.p[3:]))
-
-            # else:
-                # in the other case, it is updated at the time of calculting
-                # the statistics 
-
-# END -- HW 20170606      
-        #print(self.air_ap)
-
-        if self.sw_ac and not self.sw_ap:
-            raise ValueError("air circulation switch only possible when air \
-                             profiles are given")
-        
-        if self.sw_ac:
-
-            # # # we comment this out, because subsidence is calculated
-            # according to advection
-            # #interpolate subsidence towards the air_ap height coordinate
-            # self.air_ap['w'] = np.interp(self.air_ap.p,\
-            #                               self.air_ac.p,\
-            #                               self.air_ac.w) 
-            # #subsidence at the mixed-layer top
-            # self.w = self.air_ap.w[1]
-        
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-                # in case we didn't find any points, we just take the lowest one.
-                # actually, this can happen if ERA-INTERIM pressure levels are
-                # inconsistent with 
-                if in_ml.sum() == 0:
-                    warnings.warn(" no circulation points in the mixed layer \
-                                  found. We just take the bottom one.")
-                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-
-                for var in ['t','q','u','v']:
-    
-                   # calculation of the advection variables for the mixed layer
-                   # we weight by the hydrostatic thickness of each layer and
-                   # divide by the total thickness
-                   self.__dict__['adv'+var] = \
-                            ((self.air_ac['adv'+var+'_x'][in_ml] \
-                             + \
-                             self.air_ac['adv'+var+'_y'][in_ml])* \
-                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                            self.air_ac['delpdgrav'][in_ml].sum()
-
-                   # calculation of the advection variables for the profile above
-                   # (lowest 3 values are not used by class)
-                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
-                   self.air_ap['adv'+var] = \
-                           np.interp(self.air_ap.p,\
-                                     self.air_ac.p,\
-                                     self.air_ac['adv'+var+'_x']) \
-                           + \
-                           np.interp(self.air_ap.p, \
-                                       self.air_ac.p, \
-                                       self.air_ac['adv'+var+'_y'])
-
-                # as an approximation, we consider that advection of theta in the
-                # mixed layer is equal to advection of t. This is a sufficient
-                # approximation since theta and t are very similar at the surface
-                # pressure.
-                self.__dict__['advtheta'] = self.__dict__['advt']
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # self.wrho = np.interp(self.P_h,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) 
-            # self.ws   = self.air_ap.w.iloc[1]
-
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                self.air_ap = self.air_ap.assign(wp = 0.)
-                self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                              self.air_ac.p, \
-                                              self.air_ac['wp'])
-                self.air_ap = self.air_ap.assign(R = 0.)
-                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                     self.Rv*self.air_ap.q)
-                self.air_ap = self.air_ap.assign(rho = 0.)
-                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-                
-                self.air_ap = self.air_ap.assign(w = 0.)
-                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-                #print('hello w ini')
-
-                # Note: in case of sw_ac is False, we update it from prescribed
-                # divergence
-                self.ws   = self.air_ap.w[1]
-
-                # self.ws   = self.wrho/self.rho
-                # self.ws   = self.wrho/(self.P_h/ \
-                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
-                #                         self.theta) # this should be T!!!
-
-                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-                #                         + \
-                #                         self.air_ac['divU_y'][in_ml])* \
-                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                #             self.air_ac['delpdgrav'][in_ml].sum() \
-        
-
-        # Tendencies 
-        self.htend      = None                  # tendency of CBL [m s-1]
-        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
-        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
-        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
-        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
-        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
-        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
-        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
-        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
-        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
-        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
-        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
-  
-        # initialize surface layer
-        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
-        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
-        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
-        self.z0m        = self.input.z0m        # roughness length for momentum [m]
-        self.z0h        = self.input.z0h        # roughness length for scalars [m]
-        self.Cm         = 1e12                  # drag coefficient for momentum [-]
-        self.Cs         = 1e12                  # drag coefficient for scalars [-]
-        self.L          = None                  # Obukhov length [m]
-        self.Rib        = None                  # bulk Richardson number [-]
-        self.ra         = None                  # aerodynamic resistance [s m-1]
-  
-        # initialize radiation
-        self.lat        = self.input.lat        # latitude [deg]
-        #self.fc         = self.input.fc         # coriolis parameter [s-1]
-        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
-        self.lon        = self.input.lon        # longitude [deg]
-        self.doy        = self.input.doy        # day of the year [-]
-        self.tstart     = self.input.tstart     # time of the day [-]
-        self.cc         = self.input.cc         # cloud cover fraction [-]
-        self.Swin       = None                  # incoming short wave radiation [W m-2]
-        self.Swout      = None                  # outgoing short wave radiation [W m-2]
-        self.Lwin       = None                  # incoming long wave radiation [W m-2]
-        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
-        self.Q          = self.input.Q          # net radiation [W m-2]
-        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
-  
-        # initialize land surface
-        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
-        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
-        self.T2         = self.input.T2         # temperature deeper soil layer [K]
-                           
-        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
-        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
-        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
-        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
-                           
-        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
-        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
-        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
-                           
-        self.C1sat      = self.input.C1sat      
-        self.C2ref      = self.input.C2ref      
-
-        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
-        
-        self.LAI        = self.input.LAI        # leaf area index [-]
-        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
-        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = self.input.alpha      # surface albedo [-]
-  
-        self.rs         = 1.e6                  # resistance transpiration [s m-1]
-        self.rssoil     = 1.e6                  # resistance soil [s m-1]
-                           
-        self.Ts         = self.input.Ts         # surface temperature [K]
-                           
-        self.cveg       = self.input.cveg       # vegetation fraction [-]
-        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
-        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
-        self.cliq       = None                  # wet fraction [-]
-                          
-        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
-  
-        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
-        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
-        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
-  
-        self.H          = None                  # sensible heat flux [W m-2]
-        self.LE         = None                  # evapotranspiration [W m-2]
-        self.LEliq      = None                  # open water evaporation [W m-2]
-        self.LEveg      = None                  # transpiration [W m-2]
-        self.LEsoil     = None                  # soil evaporation [W m-2]
-        self.LEpot      = None                  # potential evaporation [W m-2]
-        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
-        self.G          = None                  # ground heat flux [W m-2]
-
-        # initialize A-Gs surface scheme
-        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
-
-        # initialize cumulus parameterization
-        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
-        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
-        self.ac         = 0.                    # Cloud core fraction [-]
-        self.M          = 0.                    # Cloud core mass flux [m s-1] 
-        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
-  
-        # initialize time variables
-        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
-        self.dt     = self.input.dt
-        self.dtcur      = self.dt
-        self.firsttime = True
-        self.t      = 0
- 
-        # Some sanity checks for valid input
-        if (self.c_beta is None): 
-            self.c_beta = 0                     # Zero curvature; linear response
-        assert(self.c_beta >= 0 or self.c_beta <= 1)
-
-        # initialize output
-        self.out = model_output(self.tsteps)
- 
-        self.statistics()
-  
-        # calculate initial diagnostic variables
-        if(self.sw_rad):
-            self.run_radiation()
- 
-        if(self.sw_sl):
-            for i in range(10): 
-                self.run_surface_layer()
-  
-        if(self.sw_ls):
-            self.run_land_surface()
-
-        if(self.sw_cu):
-            self.run_mixed_layer()
-            self.run_cumulus()
-        
-        if(self.sw_ml):
-            self.run_mixed_layer()
-
-    def timestep(self):
-
-        self.dtmax = +np.inf
-        self.logger.debug('before stats') 
-        self.statistics()
-
-        # run radiation model
-        self.logger.debug('before rad') 
-        if(self.sw_rad):
-            self.run_radiation()
-  
-        # run surface layer model
-        if(self.sw_sl):
-            self.logger.debug('before surface layer') 
-            self.run_surface_layer()
-        
-        # run land surface model
-        if(self.sw_ls):
-            self.logger.debug('before land surface') 
-            self.run_land_surface()
- 
-        # run cumulus parameterization
-        if(self.sw_cu):
-            self.logger.debug('before cumulus') 
-            self.run_cumulus()
-   
-        self.logger.debug('before mixed layer') 
-        # run mixed-layer model
-        if(self.sw_ml):
-            self.run_mixed_layer()
-        self.logger.debug('after mixed layer') 
- 
-        #get first profile data point above mixed layer
-        if self.sw_ap:
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                # here we correct for the fact that the upper profile also
-                # shifts in the vertical.
-
-                diffhtend = self.htend - self.air_ap.w[zidx_first]
-                if diffhtend > 0:
-                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            else:
-                if self.htend > 0:
-                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            #print(self.h,zidx_first,self.ws,self.air_ap.z)
-
-        
-        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
-        self.logger.debug('before store') 
-        self.substep =  (self.dtcur > self.dtmax)
-        if self.substep:
-            dtnext = self.dtcur - self.dtmax
-            self.dtcur = self.dtmax
-
-        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
-
-        # HW: this will be done multiple times in case of a substep is needed
-        # store output before time integration
-        if self.firsttime:
-            self.store()
-  
-        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
-        # time integrate land surface model
-        if(self.sw_ls):
-            self.integrate_land_surface()
-        self.logger.debug('before integrate mixed layer') 
-        # time integrate mixed-layer model
-        if(self.sw_ml):
-            self.integrate_mixed_layer() 
-        self.logger.debug('after integrate mixed layer') 
-        if self.substep:
-            self.dtcur = dtnext
-            self.firsttime = False
-            self.substeps += 1
-        else:
-            self.dtcur = self.dt
-            self.t += 1 
-            self.firsttime = True
-            self.substeps = 0
-        self.logger.debug('going to next step')
-        
-        
-  
-    def statistics(self):
-        # Calculate virtual temperatures 
-        self.thetav   = self.theta  + 0.61 * self.theta * self.q
-        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
-        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
-        # Mixed-layer top properties
-        self.P_h    = self.Ps - self.rho * self.g * self.h
-        # else:
-            # in the other case, it is updated at the time that the profile is
-            # updated (and at the initialization
-
-        self.T_h    = self.theta - self.g/self.cp * self.h
-
-        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
-        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
-
-        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
-
-        # Find lifting condensation level iteratively
-        if(self.t == 0):
-            self.lcl = self.h
-            RHlcl = 0.5
-        else:
-            RHlcl = 0.9998 
-
-        itmax = 30
-        it = 0
-        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
-            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
-            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
-        else:
-            self.q2_h   = 0.
-            self.CO22_h = 0.
-
-        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
-        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
-        self.M      = self.ac * self.wstar
-        self.wqM    = self.M * self.q2_h**0.5
-
-        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
-        if(self.dCO2 < 0):
-            self.wCO2M  = self.M * self.CO22_h**0.5
-        else:
-            self.wCO2M  = 0.
-
-    def run_mixed_layer(self):
-        if(not self.sw_sl):
-            # decompose ustar along the wind components
-            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
-            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
-
-
-
-        # calculate large-scale vertical velocity (subsidence)
-        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
-            self.ws = -self.divU * self.h
-        # else:
-        #     in case the air circulation switch is turned on, subsidence is
-        #     calculated from the circulate profile at the initialization and
-        #     in the integrate_mixed_layer routine
-              
-        # calculate compensation to fix the free troposphere in case of subsidence 
-        if(self.sw_fixft):
-            w_th_ft  = self.gammatheta * self.ws
-            w_q_ft   = self.gammaq     * self.ws
-            w_CO2_ft = self.gammaCO2   * self.ws 
-        else:
-            w_th_ft  = 0.
-            w_q_ft   = 0.
-            w_CO2_ft = 0. 
-      
-        # calculate mixed-layer growth due to cloud top radiative divergence
-        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
-       
-        # calculate convective velocity scale w* 
-        if(self.wthetav > 0.):
-            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
-        else:
-            self.wstar  = 1e-6;
-      
-        # Virtual heat entrainment flux 
-        self.wthetave    = -self.beta * self.wthetav 
-        
-        # compute mixed-layer tendencies
-        if(self.sw_shearwe):
-            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
-        else:
-            self.we    = -self.wthetave / self.dthetav
-        # Don't allow boundary layer shrinking if wtheta < 0 
-        if(self.we < 0):
-            self.we = 0.
-
-        # Calculate entrainment fluxes
-        self.wthetae     = -self.we * self.dtheta
-        self.wqe         = -self.we * self.dq
-        self.wCO2e       = -self.we * self.dCO2
-        
-        htend_pre       = self.we + self.ws + self.wf - self.M
-        
-        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-        
- 
-        #print('thetatend_pre',thetatend_pre)
-        
-        #preliminary boundary-layer top chenage
-        #htend_pre = self.we + self.ws + self.wf - self.M
-        #preliminary change in temperature jump
-        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
-                          thetatend_pre + w_th_ft
-        
-        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
-        l_entrainment = True
-
-        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
-            l_entrainment = False
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! temperature jump is at the lower limit \
-                          and is not growing: entrainment is disabled for this (sub)timestep.") 
-        elif dtheta_pre < 0.1:
-            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
-            l_entrainment = True
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          " Warning! Potential temperature jump at mixed- \
-                          layer height would become too low limiting timestep \
-                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
-            self.dtmax = min(self.dtmax,dtmax_new)
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "next subtimestep, entrainment will be disabled")
-            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
-
-
-
-        # when entrainment is disabled, we just use the simplified formulation
-        # as in Wouters et al., 2013 (section 2.2.1)
-
-        self.dthetatend = l_entrainment*dthetatend_pre + \
-                        (1.-l_entrainment)*0.
-        self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
-        self.htend = l_entrainment*htend_pre + \
-                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
-        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
-        #stop
-
-
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
-
-
-        # self.qtend = l_entrainment*qtend_pre + \
-        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
-        # self.CO2tend = l_entrainment*CO2tend_pre + \
-        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-
-
-        #     # part of the timestep for which the temperature mixed-layer jump
-        #     # was changing, and for which entrainment took place. For the other
-        #     # part, we don't assume entrainment anymore, and we use the
-        #     # simplified formulation  of Wouters et al., 2013
-
-        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
-        #   
-        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
-        #                      self.dthetatend + w_th_ft) + \
-        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
-        #     self.htend = fac*self.htend + \
-        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
-        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
-        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-
-        # else:
-        #     #self.htend = htend_pre
-        #     self.dthetatend = dthetatend_pre
-        #     self.thetatend = thetatend_pre
-        
-        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
-     
-        # assume u + du = ug, so ug - u = du
-        if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
-  
-            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
-        
-        # tendency of the transition layer thickness
-        if(self.ac > 0 or self.lcl - self.h < 300):
-            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
-        else:
-            self.dztend = 0.
-
-   
-    def integrate_mixed_layer(self):
-        # set values previous time step
-        h0      = self.h
-        
-        theta0  = self.theta
-        dtheta0 = self.dtheta
-        q0      = self.q
-        dq0     = self.dq
-        CO20    = self.CO2
-        dCO20   = self.dCO2
-        
-        u0      = self.u
-        du0     = self.du
-        v0      = self.v
-        dv0     = self.dv
-
-        dz0     = self.dz_h
-  
-        # integrate mixed-layer equations
-        
-            
-
-# END -- HW 20170606        
-        self.h        = h0      + self.dtcur * self.htend
-        # print(self.h,self.htend)
-        # stop
-        self.theta    = theta0  + self.dtcur * self.thetatend
-        #print(dtheta0,self.dtcur,self.dthetatend)
-        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
-        self.q        = q0      + self.dtcur * self.qtend
-        self.dq       = dq0     + self.dtcur * self.dqtend
-        self.CO2      = CO20    + self.dtcur * self.CO2tend
-        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
-        self.dz_h     = dz0     + self.dtcur * self.dztend
-            
-        # Limit dz to minimal value
-        dz0 = 50
-        if(self.dz_h < dz0):
-            self.dz_h = dz0 
-  
-        if(self.sw_wind):
-            self.u        = u0      + self.dtcur * self.utend
-            self.du       = du0     + self.dtcur * self.dutend
-            self.v        = v0      + self.dtcur * self.vtend
-            self.dv       = dv0     + self.dtcur * self.dvtend
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-            for var in ['t','q','u','v']:
-                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
-
-            # take into account advection for the whole profile
-                
-                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
-
-            var = 'z'
-            #print(self.air_ap[var])
-                #     print(self.air_ap['adv'+var])
-
-
-
-
-            #moving the profile vertically according to the vertical wind
-                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
-
-
-            # air_apvarold = pd.Series(np.array(self.air_ap.z))
-            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
-            # stop
-
-
-                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
-                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
-
-            #As t is updated, we also need to recalculate theta (and R)
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-
-            # air_aptheta_old = pd.Series(self.air_ap['theta'])
-            self.air_ap['theta'] = \
-                        self.air_ap.t * \
-                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
-                                         self.dtcur * self.air_ap.w[zidx_first:]
-
-#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
-#            print(self.t, self.dtcur,self.dt,self.htend)
-
-            # # the pressure levels of the profiles are recalculated according to
-            # # there new height (after subsidence)
-            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
-            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
-            #         * self.dtcur *  self.air_ap.w[zidx_first:]
-
-            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
-                    self.dtcur * self.air_ap.wp[zidx_first:]
-
-            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
-        # note that theta and q itself are updatet by class itself
-
-    
-        if self.sw_ap:
-            # Just for model consistency preservation purposes, we set the
-            # theta variables of the mixed-layer to nan values, since the
-            # mixed-layer values should overwritte by the mixed-layer
-            # calculations of class.
-            self.air_ap['theta'][0:3] = np.nan 
-            self.air_ap['p'][0:3] = np.nan 
-            self.air_ap['q'][0:3] = np.nan 
-            self.air_ap['u'][0:3] = np.nan 
-            self.air_ap['v'][0:3] = np.nan 
-            self.air_ap['t'][0:3] = np.nan 
-            self.air_ap['z'][0:3] = np.nan 
-
-            # Update the vertical profiles: 
-            #   - new mixed layer properties( h, theta, q ...)
-            #   - any data points below the new ixed-layer height are removed
-
-            # Three data points at the bottom that describe the mixed-layer
-            # properties
-            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
-                                           # columns as air_ap
-            # air_ap_head['z'].iloc[0] = 2.
-            # air_ap_head['z'].iloc[1] = self.__dict__['h']
-            # air_ap_head['z'].iloc[2] = self.__dict__['h']
-            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
-                        [2.,self.__dict__['h'],self.__dict__['h']]
-            for var in ['theta','q','u','v']:
-
-                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
-                        [self.__dict__[var], \
-                         self.__dict__[var], \
-                         self.__dict__[var] + self.__dict__['d'+var]]
-                
-            #print(self.air_ap)
-
-            # This is the remaining profile considering the remaining
-            # datapoints above the mixed layer height
-            air_ap_tail = self.air_ap.iloc[3:]
-            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
-
-            # print('h',self.h)
-            # # only select samples monotonically increasing with height
-            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            # air_ap_tail = pd.DataFrame()
-            # theta_low = self.theta
-            # z_low =     self.h
-            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            # for ibottom in range(1,len(air_ap_tail_orig)):
-            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
-            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-            # make theta increase strong enough to avoid numerical
-            # instability
-            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            air_ap_tail = pd.DataFrame()
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            theta_low = self.theta
-            z_low =     self.h
-            ibottom = 0
-            itop = 0
-            # print(air_ap_tail_orig)
-            # stop
-
-            # HW: this is the lower limit that we use for gammatheta, which is
-            # there to avoid model crashes. Besides on this limit, the upper
-            # air profile is modified in a way that is still conserves total
-            # quantities of moisture and temperature. The limit is set by trial
-            # and error. The numerics behind the crash should be investigated
-            # so that a cleaner solution can be provided.
-            gammatheta_lower_limit = 0.002
-            while ((itop in range(0,1)) or (itop != ibottom)):
-                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-                if (
-                    #(z_mean > (z_low+0.2)) and \
-                    #(theta_mean > (theta_low+0.02) ) and \
-                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
-                  (itop >= (len(air_ap_tail_orig)-1)) \
-                   :
-
-                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                    ibottom = itop+1
-                    theta_low = air_ap_tail.theta.iloc[-1]
-                    z_low =     air_ap_tail.z.iloc[-1]
-    
-
-                itop +=1
-                # elif  (itop > len(air_ap_tail_orig)-10):
-                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-                #print(itop,ibottom)
-
-            if itop > 1:
-                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! Temperature profile was too steep. \
-                                  Modifying profile: "+ \
-                                  str(itop - 1)+ " measurements were dropped \
-                                  and replaced with its average \
-                                  Modifying profile. \
-                                  mean with next profile point(s).") 
-
-
-            self.air_ap = pd.concat((air_ap_head,\
-                                     air_ap_tail,\
-                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
-                                                                      axis=1)
-
-            if  self.sw_ac:
-                qvalues = \
-                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
-
-                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
-                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
-                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-                self.P_h    = self.Ps - self.rho * self.g * self.h
-                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
-                        [self.Ps,  self.P_h, self.P_h-0.1]
-
-                self.air_ap.t = \
-                            self.air_ap.theta * \
-                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
-
-
-        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
-
-
-
-
-        # else:
-            # in the other case, it is updated at the time the statistics are
-            # calculated 
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if in_ml.sum() == 0:
-                warnings.warn(" no circulation points in the mixed layer \
-                              found. We just take the bottom one.")
-                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-            for var in ['t','q','u','v']:
-
-                # calculation of the advection variables for the mixed-layer
-                # these will be used for the next timestep
-                # Warning: w is excluded for now.
-
-                self.__dict__['adv'+var] = \
-                        ((self.air_ac['adv'+var+'_x'][in_ml] \
-                         + \
-                         self.air_ac['adv'+var+'_y'][in_ml])* \
-                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                        self.air_ac['delpdgrav'][in_ml].sum()
-
-                # calculation of the advection variables for the profile above
-                # the mixed layer (also for the next timestep)
-                self.air_ap['adv'+var] = \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p,\
-                                              self.air_ac['adv'+var+'_x']) \
-                                    + \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p, \
-                                              self.air_ac['adv'+var+'_y'])
-                # if var == 't':
-                #     print(self.air_ap['adv'+var])
-                #     stop
-
-            # as an approximation, we consider that advection of theta in the
-            # mixed layer is equal to advection of t. This is a sufficient
-            # approximation since theta and t are very similar at the surface
-            # pressure.
-
-            self.__dict__['advtheta'] = self.__dict__['advt']
-
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            # update the vertical wind profile
-            self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                          self.air_ac.p, \
-                                          self.air_ac['wp'])
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-            
-            air_apwold = self.air_ap['w']
-            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-            #print('hello w upd')
-
-            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # # self.wrho = np.interp(self.P_h,\
-            # #                      self.air_ach.p,\
-            # #                      self.air_ach['wrho']) \
-
-
-
-            # Also update the vertical wind at the mixed-layer height
-            # (subsidence)
-            self.ws   = self.air_ap.w[1]
-        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
-
-            ## Finally, we update he 
-            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-            #                        + \
-            #                        self.air_ac['divU_y'][in_ml])* \
-            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-            #            self.air_ac['delpdgrav'][in_ml].sum() 
-            
-
-        if self.sw_ap:
-            for var in ['theta','q','u','v']:
-
-                # update of the slope (gamma) for the different variables, for
-                # the next timestep!
-
-                # there is an warning message that tells about dividing through
-                # zero, which we ignore
-
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                    # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap['gamma'+var] = gammavar
-
-                # Based on the above, update the gamma value at the mixed-layer
-                # top
-                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
-                                                                     self.air_ap.z)[0][-1]]
-
-            
-    def run_radiation(self):
-        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
-        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
-        sinlea = max(sinlea, 0.0001)
-        
-        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
-  
-        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
-  
-        self.Swin  = self.S0 * Tr * sinlea
-        self.Swout = self.alpha * self.S0 * Tr * sinlea
-        
-        
-        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
-        self.Lwout = self.bolz * self.Ts ** 4.
-          
-        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
-        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
-  
-    def run_surface_layer(self):
-        # HW: I had to raise the minimum wind speed to make the simulation with
-        # the non-iterative solution stable (this solution was a wild guess, so I don't
-        # know the exact problem of the instability in case of very low wind
-        # speeds yet)
-        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        # version of 20180730 where there are still some runs crashing. Maybe
-        # an upper limit should be set on the monin-obukhov length instead of
-        # a lower limmit on the wind speed?
-        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        
-        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
-        qsatsurf       = qsat(self.thetasurf, self.Ps)
-        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
-        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
-
-        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
-  
-        zsl       = 0.1 * self.h
-        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
-        
-
-
-        if self.sw_lit:
-            self.Rib  = min(self.Rib, 0.2)
-            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
-            self.zeta  = zsl/self.L
-            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
-            
-        
-            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
-            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
-            
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-        
-     
-            # diagnostic meteorological variables
-            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
-            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
-            
-            # diagnostic meteorological variables
-        else:
-            
-            ## circumventing any iteration with Wouters et al., 2012
-            self.zslz0m = np.max((zsl/self.z0m,10.))
-            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
-            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
-            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
-            self.L = zsl/self.zeta
-            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
-        
-            self.Cm = self.k**2.0/funm/funm
-            self.Cs = self.k**2.0/funm/funh
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-            
-            # extrapolation from mixed layer (instead of from surface) to 2meter
-            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
-            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
-            self.u2m    =                - self.uw     / self.ustar / self.k * funm
-            self.v2m    =                - self.vw     / self.ustar / self.k * funm
-        
-        
-        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
-        self.e2m    = self.q2m * self.Ps / 0.622
-     
-    def ribtol(self, Rib, zsl, z0m, z0h): 
-        if(Rib > 0.):
-            L    = 1.
-            L0   = 2.
-        else:
-            L  = -1.
-            L0 = -2.
-        #print(Rib,zsl,z0m,z0h)
-        
-        while (abs(L - L0) > 0.001):
-            L0      = L
-            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
-            Lstart  = L - 0.001*L
-            Lend    = L + 0.001*L
-            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
-                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-            L       = L - fx / fxdif
-            #print(L)
-            if(abs(L) > 1e12):
-                break
-
-        return L
-      
-    def psim(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psim = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-        return psim
-      
-    def psih(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psih  = 2. * np.log( (1. + x*x) / 2.)
-            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-        return psih
- 
-    def jarvis_stewart(self):
-        # calculate surface resistances using Jarvis-Stewart model
-        if(self.sw_rad):
-            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
-        else:
-            f1 = 1.
-  
-        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
-            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
-        else:
-            f2 = 1.e8
- 
-        # Limit f2 in case w2 > wfc, where f2 < 1
-        f2 = max(f2, 1.);
- 
-        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
-        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
-  
-        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
-
-    def factorial(self,k):
-        factorial = 1
-        for n in range(2,k+1):
-            factorial = factorial * float(n)
-        return factorial;
-
-    def E1(self,x):
-        E1sum = 0
-        for k in range(1,100):
-            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
-        return -0.57721566490153286060 - np.log(x) - E1sum
- 
-    def ags(self):
-        # Select index for plant type
-        if(self.c3c4 == 'c3'):
-            c = 0
-        elif(self.c3c4 == 'c4'):
-            c = 1
-        else:
-            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
-
-        # calculate CO2 compensation concentration
-        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
-
-        # calculate mesophyll conductance
-        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
-                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
-        gm            = gm / 1000. # conversion from mm s-1 to m s-1
-  
-        # calculate CO2 concentration inside the leaf (ci)
-        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
-        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
-  
-        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
-        D0            = (self.f0[c] - fmin) / self.ad[c]
-  
-        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
-        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
-        ci            = cfrac * (co2abs - CO2comp) + CO2comp
-  
-        # calculate maximal gross primary production in high light conditions (Ag)
-        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
-  
-        # calculate effect of soil moisture stress on gross assimilation rate
-        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
-  
-        # calculate stress function
-        if (self.c_beta == 0):
-            fstr = betaw;
-        else:
-            # Following Combe et al (2016)
-            if (self.c_beta < 0.25):
-                P = 6.4 * self.c_beta
-            elif (self.c_beta < 0.50):
-                P = 7.6 * self.c_beta - 0.3
-            else:
-                P = 2**(3.66 * self.c_beta + 0.34) - 1
-            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
-  
-        # calculate gross assimilation rate (Am)
-        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
-        Rdark        = (1. / 9.) * Am
-        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
-  
-        # calculate  light use efficiency
-        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
-  
-        # calculate gross primary productivity
-        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
-  
-        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
-        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
-        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
-  
-        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
-        a1           = 1. / (1. - self.f0[c])
-        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
-  
-        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
-  
-        # calculate surface resistance for moisture and carbon dioxide
-        self.rs      = 1. / (1.6 * gcco2)
-        rsCO2        = 1. / gcco2
-  
-        # calculate net flux of CO2 into the plant (An)
-        An           = -(co2abs - ci) / (self.ra + rsCO2)
-  
-        # CO2 soil surface flux
-        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
-        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
-  
-        # CO2 flux
-        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
-        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
-        self.wCO2    = self.wCO2A + self.wCO2R
- 
-    def run_land_surface(self):
-        # compute ra
-        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
-        #print('ueff',self.u,self.v,self.wstar)
-
-        if(self.sw_sl):
-          self.ra = (self.Cs * ueff)**-1.
-        else:
-          self.ra = ueff / max(1.e-3, self.ustar)**2.
-
-        #print('ra',self.ra,self.ustar,ueff)
-
-        # first calculate essential thermodynamic variables
-        self.esat    = esat(self.theta)
-        self.qsat    = qsat(self.theta, self.Ps)
-        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
-        self.dqsatdT = 0.622 * desatdT / self.Ps
-        self.e       = self.q * self.Ps / 0.622
-
-        if(self.ls_type == 'js'): 
-            self.jarvis_stewart() 
-        elif(self.ls_type == 'ags'):
-            self.ags()
-        else:
-            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
-
-        # recompute f2 using wg instead of w2
-        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
-          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
-        else:
-          f2        = 1.e8
-        self.rssoil = self.rssoilmin * f2 
- 
-        Wlmx = self.LAI * self.Wmax
-        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
-        self.cliq = min(1., self.Wl / Wlmx) 
-     
-        # calculate skin temperature implictly
-        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
-            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
-            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
-            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
-
-        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
-        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
-        #print('Ts',self.rs)
-
-        esatsurf      = esat(self.Ts)
-        self.qsatsurf = qsat(self.Ts, self.Ps)
-
-        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-  
-        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
-  
-        self.LE     = self.LEsoil + self.LEveg + self.LEliq
-        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
-        #print('H',self.ra,self.Ts,self.theta)
-        self.G      = self.Lambda * (self.Ts - self.Tsoil)
-        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
-        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
-        
-        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
-  
-        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
-   
-        d1          = 0.1
-        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
-        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
-        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
-        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
-  
-        # calculate kinematic heat fluxes
-        self.wtheta   = self.H  / (self.rho * self.cp)
-        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
-        self.wq       = self.LE / (self.rho * self.Lv)
- 
-    def integrate_land_surface(self):
-        # integrate soil equations
-        Tsoil0        = self.Tsoil
-        wg0           = self.wg
-        Wl0           = self.Wl
-  
-        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
-        self.wg       = wg0     + self.dtcur * self.wgtend
-        self.Wl       = Wl0     + self.dtcur * self.Wltend
-  
-    # store model output
-    def store(self):
-        t                      = self.t
-        
-        self.out.time[t]          = t * self.dt / 3600. + self.tstart
-
-        # in case we are at the end of the simulation, we store the vertical
-        # profiles to the output
-        
-        # if t == (len(self.out.time) - 1):
-        #     self.out.air_ac = self.air_ac
-        #     self.out.air_ap = self.air_ap
-
-        
-        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
-        #  for key in self.out.__dict__.keys():
-        #      if key in self.__dict__:
-        #          self.out.__dict__[key][t]  = self.__dict__[key]
-        
-        self.out.h[t]          = self.h
-        
-        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
-        
-        self.out.gammatheta[t] = self.gammatheta
-        self.out.gammau[t]     = self.gammau
-        self.out.gammav[t]     = self.gammav
-        self.out.gammaq[t]     = self.gammaq
-        self.out.theta[t]      = self.theta
-        self.out.thetav[t]     = self.thetav
-        self.out.dtheta[t]     = self.dtheta
-        self.out.dthetav[t]    = self.dthetav
-        self.out.wtheta[t]     = self.wtheta
-        self.out.wthetav[t]    = self.wthetav
-        self.out.wthetae[t]    = self.wthetae
-        self.out.wthetave[t]   = self.wthetave
-        
-        self.out.q[t]          = self.q
-        self.out.dq[t]         = self.dq
-        self.out.wq[t]         = self.wq
-        self.out.wqe[t]        = self.wqe
-        self.out.wqM[t]        = self.wqM
-      
-        self.out.qsat[t]       = self.qsat
-        self.out.e[t]          = self.e
-        self.out.esat[t]       = self.esat
-      
-        fac = (self.rho*self.mco2)/self.mair
-        self.out.CO2[t]        = self.CO2
-        self.out.dCO2[t]       = self.dCO2
-        self.out.wCO2[t]       = self.wCO2  * fac
-        self.out.wCO2e[t]      = self.wCO2e * fac
-        self.out.wCO2R[t]      = self.wCO2R * fac
-        self.out.wCO2A[t]      = self.wCO2A * fac
-
-        self.out.u[t]          = self.u
-        self.out.du[t]         = self.du
-        self.out.uw[t]         = self.uw
-        
-        self.out.v[t]          = self.v
-        self.out.dv[t]         = self.dv
-        self.out.vw[t]         = self.vw
-        
-        self.out.T2m[t]        = self.T2m
-        self.out.q2m[t]        = self.q2m
-        self.out.u2m[t]        = self.u2m
-        self.out.v2m[t]        = self.v2m
-        self.out.e2m[t]        = self.e2m
-        self.out.esat2m[t]     = self.esat2m
-
-
-        self.out.Tsoil[t]      = self.Tsoil
-        self.out.T2[t]         = self.T2
-        self.out.Ts[t]         = self.Ts
-        self.out.wg[t]         = self.wg
-        
-        self.out.thetasurf[t]  = self.thetasurf
-        self.out.thetavsurf[t] = self.thetavsurf
-        self.out.qsurf[t]      = self.qsurf
-        self.out.ustar[t]      = self.ustar
-        self.out.Cm[t]         = self.Cm
-        self.out.Cs[t]         = self.Cs
-        self.out.L[t]          = self.L
-        self.out.Rib[t]        = self.Rib
-  
-        self.out.Swin[t]       = self.Swin
-        self.out.Swout[t]      = self.Swout
-        self.out.Lwin[t]       = self.Lwin
-        self.out.Lwout[t]      = self.Lwout
-        self.out.Q[t]          = self.Q
-  
-        self.out.ra[t]         = self.ra
-        self.out.rs[t]         = self.rs
-        self.out.H[t]          = self.H
-        self.out.LE[t]         = self.LE
-        self.out.LEliq[t]      = self.LEliq
-        self.out.LEveg[t]      = self.LEveg
-        self.out.LEsoil[t]     = self.LEsoil
-        self.out.LEpot[t]      = self.LEpot
-        self.out.LEref[t]      = self.LEref
-        self.out.G[t]          = self.G
-
-        self.out.zlcl[t]       = self.lcl
-        self.out.RH_h[t]       = self.RH_h
-
-        self.out.ac[t]         = self.ac
-        self.out.M[t]          = self.M
-        self.out.dz[t]         = self.dz_h
-        self.out.substeps[t]   = self.substeps
-  
-    # delete class variables to facilitate analysis in ipython
-    def exitmodel(self):
-        del(self.Lv)
-        del(self.cp)
-        del(self.rho)
-        del(self.k)
-        del(self.g)
-        del(self.Rd)
-        del(self.Rv)
-        del(self.bolz)
-        del(self.S0)
-        del(self.rhow)
-  
-        del(self.t)
-        del(self.dt)
-        del(self.tsteps)
-         
-        del(self.h)          
-        del(self.Ps)        
-        del(self.fc)        
-        del(self.ws)
-        del(self.we)
-        
-        del(self.theta)
-        del(self.dtheta)
-        del(self.gammatheta)
-        del(self.advtheta)
-        del(self.beta)
-        del(self.wtheta)
-    
-        del(self.T2m)
-        del(self.q2m)
-        del(self.e2m)
-        del(self.esat2m)
-        del(self.u2m)
-        del(self.v2m)
-        
-        del(self.thetasurf)
-        del(self.qsatsurf)
-        del(self.thetav)
-        del(self.dthetav)
-        del(self.thetavsurf)
-        del(self.qsurf)
-        del(self.wthetav)
-        
-        del(self.q)
-        del(self.qsat)
-        del(self.dqsatdT)
-        del(self.e)
-        del(self.esat)
-        del(self.dq)
-        del(self.gammaq)
-        del(self.advq)
-        del(self.wq)
-        
-        del(self.u)
-        del(self.du)
-        del(self.gammau)
-        del(self.advu)
-        
-        del(self.v)
-        del(self.dv)
-        del(self.gammav)
-        del(self.advv)
-  
-        del(self.htend)
-        del(self.thetatend)
-        del(self.dthetatend)
-        del(self.qtend)
-        del(self.dqtend)
-        del(self.utend)
-        del(self.dutend)
-        del(self.vtend)
-        del(self.dvtend)
-     
-        del(self.Tsoiltend) 
-        del(self.wgtend)  
-        del(self.Wltend) 
-  
-        del(self.ustar)
-        del(self.uw)
-        del(self.vw)
-        del(self.z0m)
-        del(self.z0h)        
-        del(self.Cm)         
-        del(self.Cs)
-        del(self.L)
-        del(self.Rib)
-        del(self.ra)
-  
-        del(self.lat)
-        del(self.lon)
-        del(self.doy)
-        del(self.tstart)
-   
-        del(self.Swin)
-        del(self.Swout)
-        del(self.Lwin)
-        del(self.Lwout)
-        del(self.cc)
-  
-        del(self.wg)
-        del(self.w2)
-        del(self.cveg)
-        del(self.cliq)
-        del(self.Tsoil)
-        del(self.T2)
-        del(self.a)
-        del(self.b)
-        del(self.p)
-        del(self.CGsat)
-  
-        del(self.wsat)
-        del(self.wfc)
-        del(self.wwilt)
-  
-        del(self.C1sat)
-        del(self.C2ref)
-  
-        del(self.LAI)
-        del(self.rs)
-        del(self.rssoil)
-        del(self.rsmin)
-        del(self.rssoilmin)
-        del(self.alpha)
-        del(self.gD)
-  
-        del(self.Ts)
-  
-        del(self.Wmax)
-        del(self.Wl)
-  
-        del(self.Lambda)
-        
-        del(self.Q)
-        del(self.H)
-        del(self.LE)
-        del(self.LEliq)
-        del(self.LEveg)
-        del(self.LEsoil)
-        del(self.LEpot)
-        del(self.LEref)
-        del(self.G)
-  
-        del(self.sw_ls)
-        del(self.sw_rad)
-        del(self.sw_sl)
-        del(self.sw_wind)
-        del(self.sw_shearwe)
-
-# class for storing mixed-layer model output data
-class model_output:
-    def __init__(self, tsteps):
-        self.time          = np.zeros(tsteps)    # time [s]
-
-        # mixed-layer variables
-        self.h          = np.zeros(tsteps)    # ABL height [m]
-        
-        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammau     = np.zeros(tsteps)
-        self.gammav     = np.zeros(tsteps)
-        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
-        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
-        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
-        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
-        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
-        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
-        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
-        
-        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
-        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
-        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
-        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
-
-        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
-        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
-        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
-
-        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
-        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
-        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
-        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
-        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
-        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
-        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
-        
-        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
-        
-        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
-
-        # diagnostic meteorological variables
-        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
-        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
-        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
-        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
-        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
-        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
-
-        # ground variables
-        self.Tsoil       = np.zeros(tsteps)
-        self.T2          = np.zeros(tsteps)
-        self.Ts          = np.zeros(tsteps)
-        self.wg          = np.zeros(tsteps)
-
-        # surface-layer variables
-        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
-        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
-        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
-        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
-        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
-        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
-        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
-        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
-        self.L          = np.zeros(tsteps)    # Obukhov length [m]
-        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
-
-        # radiation variables
-        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
-        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
-        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
-        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
-        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
-
-        # land surface variables
-        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
-        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
-        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
-        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
-        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
-        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
-        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
-        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
-        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
-        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
-
-        # Mixed-layer top variables
-        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
-        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
-
-        # cumulus variables
-        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
-        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
-        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
-        
-        
-        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
-
-# class for storing mixed-layer model input data
-class model_input:
-    def __init__(self):
-
-        # # comment not valid
-        # we comment out the initialization, because there is a problem when
-        # inheriting values from one the another class4gl_iput. We also expect
-        # that the user specifies all the required parmameters (if not, an error
-        # is raised). 
-
-        # general model variables
-        self.runtime    = None  # duration of model run [s]
-        self.dt         = None  # time step [s]
-
-        # mixed-layer variables
-        self.sw_ml      = None  # mixed-layer model switch
-        self.sw_shearwe = None  # Shear growth ABL switch
-        self.sw_fixft   = None  # Fix the free-troposphere switch
-        self.h          = None  # initial ABL height [m]
-        self.Ps         = None  # surface pressure [Pa]
-        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
-        self.fc         = None  # Coriolis parameter [s-1]
-        
-        self.theta      = None  # initial mixed-layer potential temperature [K]
-        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
-
-        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
-
-        self.dtheta     = None  # initial temperature jump at h [K]
-        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = None  # advection of heat [K s-1]
-        self.beta       = None  # entrainment ratio for virtual heat [-]
-        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
-        
-        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
-        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
-        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
-
-        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = None  # advection of moisture [kg kg-1 s-1]
-        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
-
-        self.CO2        = None  # initial mixed-layer potential temperature [K]
-        self.dCO2       = None  # initial temperature jump at h [K]
-        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advCO2     = None  # advection of heat [K s-1]
-        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
-        
-        self.sw_wind    = None  # prognostic wind switch
-        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.du         = None  # initial u-wind jump at h [m s-1]
-        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = None  # advection of u-wind [m s-2]
-
-        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = None  # initial u-wind jump at h [m s-1]
-        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = None  # advection of v-wind [m s-2]
-
-        # surface layer variables
-        self.sw_sl      = None  # surface layer switch
-        self.ustar      = None  # surface friction velocity [m s-1]
-        self.z0m        = None  # roughness length for momentum [m]
-        self.z0h        = None  # roughness length for scalars [m]
-        self.Cm         = None  # drag coefficient for momentum [-]
-        self.Cs         = None  # drag coefficient for scalars [-]
-        self.L          = None  # Obukhov length [-]
-        self.Rib        = None  # bulk Richardson number [-]
-
-        # radiation parameters
-        self.sw_rad     = None  # radiation switch
-        self.lat        = None  # latitude [deg]
-        self.lon        = None  # longitude [deg]
-        self.doy        = None  # day of the year [-]
-        self.tstart     = None  # time of the day [h UTC]
-        self.cc         = None  # cloud cover fraction [-]
-        self.Q          = None  # net radiation [W m-2] 
-        self.dFz        = None  # cloud top radiative divergence [W m-2] 
-
-        # land surface parameters
-        self.sw_ls      = None  # land surface switch
-        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
-        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = None  # temperature top soil layer [K]
-        self.T2         = None  # temperature deeper soil layer [K]
-        
-        self.a          = None  # Clapp and Hornberger retention curve parameter a
-        self.b          = None  # Clapp and Hornberger retention curve parameter b
-        self.p          = None  # Clapp and Hornberger retention curve parameter p 
-        self.CGsat      = None  # saturated soil conductivity for heat
-        
-        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
-        self.wfc        = None  # volumetric water content field capacity [-]
-        self.wwilt      = None  # volumetric water content wilting point [-]
-        
-        self.C1sat      = None 
-        self.C2ref      = None
-
-        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
-        
-        self.LAI        = None  # leaf area index [-]
-        self.gD         = None  # correction factor transpiration for VPD [-]
-        self.rsmin      = None  # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = None  # surface albedo [-]
-        
-        self.Ts         = None  # initial surface temperature [K]
-        
-        self.cveg       = None  # vegetation fraction [-]
-        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
-        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
-        
-        self.Lambda     = None  # thermal diffusivity skin layer [-]
-
-        # A-Gs parameters
-        self.c3c4       = None  # Plant type ('c3' or 'c4')
-
-        # Cumulus parameters
-        self.sw_cu      = None  # Cumulus parameterization switch
-        self.dz_h       = None  # Transition layer thickness [m]
-        
-# BEGIN -- HW 20171027
-        # self.cala       = None      # soil heat conductivity [W/(K*m)]
-        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
-# END -- HW 20171027
diff --git a/lib/ribtol/Makefile b/lib/ribtol/Makefile
deleted file mode 100644
index e23e3e1..0000000
--- a/lib/ribtol/Makefile
+++ /dev/null
@@ -1,8 +0,0 @@
-ribtol.so : ribtol.o
-	g++ -O3 -shared -Wl -z -def -o ribtol.so -lpython2.6 -lboost_python ribtol.o
-
-ribtol.o : ribtol.cpp
-	g++ -c -O3 -fPIC ribtol.cpp -I/usr/include/python2.6
-
-clean : 
-	rm -rf ribtol.o ribtol.so
diff --git a/lib/ribtol/MakefileMac b/lib/ribtol/MakefileMac
deleted file mode 100644
index bf34ea8..0000000
--- a/lib/ribtol/MakefileMac
+++ /dev/null
@@ -1,9 +0,0 @@
-# Note: boost-python needs to be installed: brew install boost-python -with-python3 -without-python
-ribtol.so : ribtol.o
-	clang++ -O3 -shared -o ribtol.so -L/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/lib -lpython3.6m -L/usr/local/lib -lboost_python3-mt -lpython ribtol.o
-
-ribtol.o : ribtol.cpp
-	clang++ -c -O3 -fPIC ribtol.cpp -I/usr/local/Cellar/python3/3.6.1/Frameworks/Python.framework/Versions/3.6/include/python3.6m -I/usr/local/include
-
-clean : 
-	rm -rf ribtol.o ribtol.so
diff --git a/lib/ribtol/ribtol.cpp b/lib/ribtol/ribtol.cpp
deleted file mode 100644
index 148b0d3..0000000
--- a/lib/ribtol/ribtol.cpp
+++ /dev/null
@@ -1,81 +0,0 @@
-// fast conversion of bulk Richardson number to Obukhov length
-
-#include 
-#include 
-#include 
-using namespace std;
-
-inline double psim(double zeta)
-{
-  double psim;
-  double x;
-  if(zeta <= 0.)
-  {
-    //x     = (1. - 16. * zeta) ** (0.25)
-    //psim  = 3.14159265 / 2. - 2. * arctan(x) + log( (1.+x) ** 2. * (1. + x ** 2.) / 8.)
-    x    = pow(1. + pow(3.6 * abs(zeta),2./3.), -0.5);
-    psim = 3. * log( (1. + 1. / x) / 2.);
-  }
-  else
-  {
-    psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35;
-  }
-  return psim;
-}
-    
-inline double psih(double zeta)
-{
-  double psih;
-  double x;
-  if(zeta <= 0.)
-  {
-    // x     = (1. - 16. * zeta) ** (0.25)
-    // psih  = 2. * log( (1. + x ** 2.) / 2. )
-    x     = pow(1. + pow(7.9 * abs(zeta), (2./3.)), -0.5);
-    psih  = 3. * log( (1. + 1. / x) / 2.);
-  }
-  else
-  {
-    psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - pow(1. + (2./3.) * zeta, 1.5) - (10./3.) / 0.35 + 1.;
-  }
-  return psih;
-}
-
-
-double ribtol(double Rib, double zsl, double z0m, double z0h)
-{
-  double L, L0;
-  double Lstart, Lend;
-  double fx, fxdif;
-
-  if(Rib > 0.)
-  {
-    L    = 1.;
-    L0   = 2.;
-  }
-  else
-  {
-    L  = -1.;
-    L0 = -2.;
-  }
-    
-  while (abs(L - L0) > 0.001)
-  {
-    L0      = L;
-    fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / pow(log(zsl / z0m) - psim(zsl / L) + psim(z0m / L), 2.);
-    Lstart  = L - 0.001 * L;
-    Lend    = L + 0.001 * L;
-    fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / pow(log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart), 2.)) - (-zsl / Lend * (log(zsl / z0h) - psih(zsl / Lend) + psih(z0h / Lend)) / pow(log(zsl / z0m) - psim(zsl / Lend) + psim(z0m / Lend), 2.)) ) / (Lstart - Lend);
-    L       = L - fx / fxdif;
-  }
-  
-  return L;
-
-}
-
-BOOST_PYTHON_MODULE(ribtol)
-{
-    using namespace boost::python;
-    def("ribtol", ribtol);
-}
-
diff --git a/lib/ribtol/ribtol.pyx b/lib/ribtol/ribtol.pyx
deleted file mode 100644
index e11a147..0000000
--- a/lib/ribtol/ribtol.pyx
+++ /dev/null
@@ -1,48 +0,0 @@
-#cython: boundscheck=False
-#cython: wraparound=False
-
-from libc.math cimport atan, log, exp, fabs
-
-cdef double psim(double zeta):
-    cdef double x, psim
-
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * atan(x) + log((1. + x)**2. * (1. + x**2.) / 8.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-      
-cdef double psih(double zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * log( (1. + x*x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
-
-def ribtol(double Rib, double zsl, double z0m, double z0h): 
-    cdef double L, L0, fx, Lstart, Lend, fxdif
-
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    
-    while (fabs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-
-        if(fabs(L) > 1e15):
-            break
-
-    return L
diff --git a/lib/ribtol/ribtol_hw.py b/lib/ribtol/ribtol_hw.py
deleted file mode 100644
index 1946cc8..0000000
--- a/lib/ribtol/ribtol_hw.py
+++ /dev/null
@@ -1,165 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Jan 12 10:46:20 2018
-
-@author: vsc42247
-"""
-
-
-
-# purpose of calc_cm_ch: calculate momentum and thermal turbulent diffusion coefficients of the surface layer with a non-iterative procedure (Wouters et al., 2012)
-
-# input:
-
-# zrib = bulk Richardson number = (g/T)* DT * z/(ua^2)
-#   with:
-#     g = 9.81 m/s2 the gravitational acceleration
-#     z = height (in meters) of the surface layer under consideration 
-#     T = (reference) temperature (in Kelvin) at height z 
-#     DT = (T - T_s) = temperature (in Kelvin) gradient between the surface and height z 
-#     u_a^2 = u^2 +  v^2 is the squared horizontal absolute wind speed 
-# zzz0m = ratio z/z0 between the height z and the momentum roughness length z0m
-# zkbm = ln(z0m/z0h), with z0m, z0h the momentum and thermal roughness length, respectively.
-
-# output: diffusion coefficients (CM and CH) which cna be used to determine surface-layer turbulent transport
-# u'w' = - CM ua^2.
-# w'T' = - CH ua DT 
-
-
-# Reference:
-# Wouters, H., De Ridder, K., and Lipzig, N. P. M.: Comprehensive
-# Parametrization of Surface-Layer Transfer Coefficients for Use
-# in Atmospheric Numerical Models, Bound.-Lay. Meteorol., 145,
-# 539–550, doi:10.1007/s10546-012-9744-3, 2012.
-
-import numpy as np
-
-def calc_cm_ch (zeta,zzz0m,zkbm):
-    krm = 0.4
-
-    #ZETA = zeta_hs2(zrib,zzz0m,zkbm)
-    FUNM,FUNH = funcsche(ZETA,zzz0m,zkbm)
-    CM = krm**2.0/FUNM/FUNM
-    CH = krm**2.0/FUNM/FUNH
-
-    # FUNMn,FUNHn = funcsche(0.,zzz0m,zkbm)
-    # CMn = krm**2.0/FUNMn/FUNMn
-    # CHn = krm**2.0/FUNMn/FUNHn
-
-    # print ZETA,FUNM,FUNH
-    # print 'CMCMN',CM/CMn
-    # print 'CHCHN',CH/CHn
-
-    return CM,CH
-
-
-def zeta_hs2(RiB,zzz0m,kBmin1):
-    #print(RiB,zzz0m,kBmin1)
-    mum=2.59
-    muh=0.95
-    nu=0.5
-    lam=1.5
-
-    betah = 5.0
-
-    zzz0h = zzz0m*np.exp(kBmin1)
-    zzzs = zzz0m*0.06 # to be changed!! r. 101 nog bekijken!!
-
-    L0M = np.log(zzz0m)
-    L0H = np.log(zzz0h)
-    facM = np.log(1.+lam/mum/zzzs)*np.exp(-mum*zzzs)/lam
-    facH = np.log(1.+lam/muh/zzzs)*np.exp(-muh*zzzs)/lam
-    L0Ms = L0M + facM 
-    L0Hs = L0H + facH
-
-    if RiB < 0.:
-        p = np.log(1.-RiB)
-        Q = -0.486 +0.219*p - 0.0331*p**2-4.93*np.exp(-L0H) - 3.65/L0H +\
-            0.38*p/L0H+ 14.8/L0H/L0H-0.946*p/L0H/L0H-10.0/L0H**3+ \
-            0.392*L0M/L0H-0.084*p*L0M/L0H+0.368*L0M/L0H/L0H
-        # print 'p: ',p
-        # print 'Q: ',Q
-        zeta = (1. + p*Q)* L0Ms**2/L0Hs * RiB
-    else:
-        betam = 4.76+7.03/zzz0m +0.24*zzz0m/zzz0h # to be changed
-        # betam = 5.0 + 1.59*10.**(-5.)*(np.exp(13.0-L0M)-1.0) \
-        #         +0.24*(np.exp(-kBmin1)-1.0) # to be changed!!
-        # print('betam',betam)
-        lL0M = np.log(L0M)
-        S0Ms = 1.-1./zzz0m + (1.+nu/mum/zzzs)*facM
-        S0Hs = 1.-1./zzz0h + (1.+nu/muh/zzzs)*facH
-        zetat = -0.316-0.515*np.exp(-L0H) + 25.8 *np.exp(-2.*L0H) + 4.36/L0H \
-                -6.39/L0H/L0H+0.834*lL0M - 0.0267*lL0M**2
-        # print('zetat',zetat)
-        RiBt = zetat *(L0Hs+ S0Hs*betah*zetat)/(L0Ms+S0Ms*betam*zetat)**2 
-        # print('RiBt',RiBt)
-
-        if (RiB > RiBt):
-            D = (L0Ms+S0Ms*betam*zetat)**3/\
-                (L0Ms*L0Hs+zetat*(2.*S0Hs * betah * L0Ms - S0Ms*betam*L0Hs))
-            zeta = zetat + D*(RiB-RiBt)
-        else:
-            r = RiB - S0Hs*betah/(S0Ms*betam)**2
-            B = S0Ms*betam*L0Hs- 2.*S0Hs*betah*L0Ms
-            C = 4.*(S0Ms*betam)**2 * L0Ms *(S0Hs*betah*L0Ms-S0Ms*betam*L0Hs)
-            zeta = - L0Ms / S0Ms/betam - B*C/(4.*(S0Ms*betam)**3 *(B**2+abs(C*r)))
-            if r != 0:
-                zeta = zeta + (B-np.sqrt(B**2+C*r) + B*C*r/(2.*(B**2+abs(C*r))))/(2.*(S0Ms*betam)**3*r)
-    # print('zeta',zeta)
-    return zeta
-
-def funcsche(zeta,zzz0,kBmin1):
-
-
-    mum=2.5
-    muh=0.9
-    nu=0.5
-    lam=1.5
-    
-    p2=3.141592/2.
-    
-    lnzzz0=np.log(zzz0)
-    zzzs=zzz0*0.06
-    zetamcorr=(1.+nu/(mum*zzzs))*zeta
-    zetam0=zeta/zzz0
-    zetahcorr=(1.+nu/(muh*zzzs))*zeta
-    zetah0=zeta/(zzz0*np.exp(kBmin1))
-    
-    if (zeta <= 0.):
-    
-        gamma=15.2
-        alfam=0.25
-        xx=(1.-gamma*zeta)**alfam
-        psim=2.*np.log((1.+xx)/2.)+np.log((1.+xx**2.)/2.)-2.*np.arctan(xx)+p2
-        xx0=(1.-gamma*zetam0)**alfam
-        psim0=2.*np.log((1.+xx0)/2.)+np.log((1.+xx0**2.)/2.)-2.*np.arctan(xx0)+p2
-        phimcorr=(1.-gamma*zetamcorr)**(-alfam)
-        
-        alfah=0.5
-        yy=(1.-gamma*zeta)**alfah
-        psih=2.*np.log((1.+yy)/2.)
-        yy0=(1.-gamma*zetah0)**alfah
-        psih0=2.*np.log((1.+yy0)/2.)
-        phihcorr=(1.-gamma*zetahcorr)**(-alfah)
-    else: 
-    
-        aa=6.1
-        bb=2.5
-        psim=-aa*np.log(zeta+(1.+zeta**bb)**(1./bb))
-        psim0=-aa*np.log(zetam0+(1.+zetam0**bb)**(1./bb))
-        phimcorr=1.+aa*(zetamcorr+zetamcorr**bb*(1.+zetamcorr**bb)**((1.-bb)/bb))/(zetamcorr+(1.+zetamcorr**bb)**(1./bb))
-        
-        cc=5.3
-        dd=1.1
-        psih=-cc*np.log(zeta+(1.+zeta**dd)**(1./dd))
-        psih0=-cc*np.log(zetah0+(1.+zetah0**dd)**(1./dd))
-        phihcorr=1.+cc*(zetahcorr+zetahcorr**dd*(1.+zetahcorr**dd)**((1.-dd)/dd))/(zetahcorr+(1.+zetahcorr**dd)**(1./dd))
-    
-    psistrm=phimcorr*(1./lam)*np.log(1.+lam/(mum*zzzs))*np.exp(-mum*zzzs)
-    psistrh=phihcorr*(1./lam)*np.log(1.+lam/(muh*zzzs))*np.exp(-muh*zzzs)
-    
-    funm=lnzzz0-psim+psim0 +psistrm
-    funh=lnzzz0+kBmin1-psih+psih0 +psistrh
-    return funm,funh
-
diff --git a/lib/ribtol/setup.py b/lib/ribtol/setup.py
deleted file mode 100644
index bfb44db..0000000
--- a/lib/ribtol/setup.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# build with "python setup.py build_ext --inplace"
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Build import cythonize
-import numpy as np
-import os
-
-os.environ["CC"] = "g++-7"
-
-setup(
-    ext_modules = cythonize((Extension("ribtol", sources=["ribtol.pyx"], include_dirs=[np.get_include()], ), ))
-)
diff --git a/model.py b/model.py
deleted file mode 100644
index 8760411..0000000
--- a/model.py
+++ /dev/null
@@ -1,2214 +0,0 @@
-# 
-# CLASS
-# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
-# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
-# Copyright (c) 2011-2015 Chiel van Heerwaarden
-# Copyright (c) 2011-2015 Bart van Stratum
-# Copyright (c) 2011-2015 Kees van den Dries
-# 
-# This file is part of CLASS
-# 
-# CLASS is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published bygamma
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-# 
-# CLASS is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-# 
-# You should have received a copy of the GNU General Public License
-# along with CLASS.  If not, see .
-#
-
-import copy as cp
-import numpy as np
-import sys
-import warnings
-import pandas as pd
-from ribtol_hw import zeta_hs2 , funcsche
-import logging
-#from SkewT.thermodynamics import Density
-#import ribtol
-
-grav = 9.81
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-
-def qsat(T,p):
-    return 0.622 * esat(T) / p
-
-
-def ribtol(Rib, zsl, z0m, z0h): 
-    Rib = np.float64(Rib)
-    zsl = np.float64(zsl)
-    z0m = np.float64(z0m)
-    z0h = np.float64(z0h)
-    #print(Rib,zsl,z0m,z0h)
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    #print(Rib,zsl,z0m,z0h)
-    while (abs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-        #print(L,fx/fxdif)
-        if(abs(L) > 1e12):
-            break
-
-    return L
-  
-def psim(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psim = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-  
-def psih(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * np.log( (1. + x*x) / 2.)
-        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
- 
-class model:
-    def __init__(self, model_input = None,debug_level=None):
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        self.logger = logging.getLogger('model')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        """ initialize the different components of the model """ 
-
-        if model_input is not None:
-            # class4gl style input
-            if 'pars' in model_input.__dict__.keys():
-
-                # we make a reference to the full input first, so we can dump it
-                # afterwards
-                self.input_c4gl = model_input
-
-                # we copy the regular parameters first. We keep the classical input
-                # format as self.input so that we don't have to change the entire
-                # model code.
-                self.input = cp.deepcopy(model_input.pars)
-
-                # we copy other sections we are interested in, such as profile
-                # data, and store it also under input
-
-                # I know we mess up a bit the structure of the class4gl_input, but
-                # we will make it clean again at the time of dumping data
-
-                # So here, we copy the profile data into self.input
-                # 1. Air circulation data 
-                if 'sw_ac' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ac']:
-                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
-                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
-
-                    # correct pressure of levels according to surface pressure
-                    # error (so that interpolation is done in a consistent way)
-
-                    p_e = self.input.Ps - self.input.sp
-                    for irow in self.input.air_ac.index[::-1]:
-                       self.input.air_ac.p.iloc[irow] =\
-                        self.input.air_ac.p.iloc[irow] + p_e
-                       p_e = p_e -\
-                       (self.input.air_ac.p.iloc[irow]+p_e)/\
-                        self.input.air_ac.p.iloc[irow] *\
-                        self.input.air_ac.delpdgrav.iloc[irow]*grav
-
-
-
-                # 2. Air circulation data 
-                if 'sw_ap' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ap']:
-                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
-
-            # standard class input
-            else:
-                self.input = cp.deepcopy(model_input)
-
-    def load_yaml_dict(self,yaml_dict):
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                for keydata,value in data.items():
-                    self.__dict__[keydata] = value
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            #elif key == 'sources':
-            #    self.__dict__[key] = data
-            elif key == 'out':
-                # lets convert it to a list of dictionaries
-                dictouttemp = pd.DataFrame(data).to_dict('list')
-            else: 
-                 warnings.warn("Key '"+key+"' is be implemented.")
-            #     self.__dict__[key] = data
-
-
-        self.tsteps = len(dictouttemp['h'])
-        self.out = model_output(self.tsteps)
-        for keydictouttemp in dictouttemp.keys():
-            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
-
-
-  
-    def run(self):
-        # initialize model variables
-        self.init()
-  
-        # time integrate model 
-        #for self.t in range(self.tsteps):
-        while self.t < self.tsteps:
-          
-            # time integrate components
-            self.timestep()
-  
-        # delete unnecessary variables from memory
-        self.exitmodel()
-    
-    def init(self):
-        # assign variables from input data
-        # initialize constants
-        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
-        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        self.rho        = 1.2                   # density of air [kg m-3]
-        self.k          = 0.4                   # Von Karman constant [-]
-        self.g          = 9.81                  # gravity acceleration [m s-2]
-        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-        self.bolz       = 5.67e-8               # Bolzman constant [-]
-        self.rhow       = 1000.                 # density of water [kg m-3]
-        self.S0         = 1368.                 # solar constant [W m-2]
-
-        # A-Gs constants and settings
-        # Plant type:       -C3-     -C4-
-        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
-        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
-        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
-        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
-        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
-        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
-        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
-        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
-        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
-        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
-        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
-
-        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
-        self.mair       =  28.9;                # molecular weight air [g mol -1]
-        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
-
-        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
-        self.wmax       =  0.55;                # upper reference value soil water [-]
-        self.wmin       =  0.005;               # lower reference value soil water [-]
-        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
-        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
-
-        # Read switches
-        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
-        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
-        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
-        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
-        self.sw_sl      = self.input.sw_sl      # surface layer switch
-        self.sw_rad     = self.input.sw_rad     # radiation switch
-        self.sw_ls      = self.input.sw_ls      # land surface switch
-        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
-        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
-
-        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
-        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
-        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-  
-        # initialize mixed-layer
-        self.h          = self.input.h          # initial ABL height [m]
-        self.Ps         = self.input.Ps         # surface pressure [Pa]
-        self.sp         = self.input.sp         # This is also surface pressure
-                                                #but derived from the global data [Pa]
-        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
-        self.ws         = None                  # large-scale vertical velocity [m s-1]
-        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
-        self.we         = -1.                   # entrainment velocity [m s-1]
-       
-         # Temperature 
-        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
-        
-        
-        self.substep    = False
-        self.substeps   = 0
-
-
-
-        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
-        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
-        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
-        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
-        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
- 
-        self.wstar      = 0.                    # convective velocity scale [m s-1]
- 
-        # 2m diagnostic variables 
-        self.T2m        = None                  # 2m temperature [K]
-        self.q2m        = None                  # 2m specific humidity [kg kg-1]
-        self.e2m        = None                  # 2m vapor pressure [Pa]
-        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
-        self.u2m        = None                  # 2m u-wind [m s-1]
-        self.v2m        = None                  # 2m v-wind [m s-1]
- 
-        # Surface variables 
-        self.thetasurf  = self.input.theta      # surface potential temperature [K]
-        self.thetavsurf = None                  # surface virtual potential temperature [K]
-        self.qsurf      = None                  # surface specific humidity [g kg-1]
-
-        # Mixed-layer top variables
-        self.P_h        = None                  # Mixed-layer top pressure [pa]
-        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
-        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
-        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
-        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
-        self.dz_h       = None                  # Transition layer thickness [-]
-        self.lcl        = None                  # Lifting condensation level [m]
-
-        # Virtual temperatures and fluxes
-        self.thetav     = None                  # initial mixed-layer potential temperature [K]
-        self.dthetav    = None                  # initial virtual temperature jump at h [K]
-        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
-        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
-       
-        
-        
-        
-        
-        
-        # Moisture 
-        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
-
-        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
-        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
-        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
-  
-        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
-        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
-        self.e          = None                  # mixed-layer vapor pressure [Pa]
-        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
-        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
-      
-        
-        
-        # CO2
-        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
-        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
-        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
-        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
-        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
-        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
-        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
-        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
-        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
-        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
-       
-        # Wind 
-        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
-        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
-        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = self.input.advu       # advection of u-wind [m s-2]
-        
-        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
-        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = self.input.advv       # advection of v-wind [m s-2]
-         
-  # BEGIN -- HW 20170606
-        # z-coordinate for vertical profiles of stratification above the mixed-layer height
-
-        if self.sw_ac:
-        # this is the data frame with the grided profile on the L60 grid
-        # (subsidence, and advection) 
-            self.air_ac      = self.input.air_ac  # full level air circulation
-                                                  # forcing
-            # self.air_ach     = self.input.air_ach # half level air circulation
-            #                                       # forcing
-            
-
-        if self.sw_ap:
-        # this is the data frame with the fitted profile (including HAGL,
-        # THTA,WSPD, SNDU,WNDV PRES ...)
-            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
-
-            # just for legacy reasons...
-            if 'z' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
-            if 'p' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
-
-            indexh = np.where(self.air_ap.z.values == self.h)
-            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
-                raise ValueError("Error input profile consistency: mixed- \
-                                 layer height needs to be equal to the second \
-                                 and third \
-                                 level of the vertical profile input!")
-            # initialize q from its profile when available
-            p_old = self.Ps
-            p_new = self.air_ap.p[indexh[0][0]]
-            
-            if ((p_old is not None) & (p_old != p_new)):
-                print("Warning: Ps input was provided ("+str(p_old)+\
-                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
-                    +str(p_new)+"Pa).")
-                                    
-            self.Ps = p_new
-            # these variables/namings are more convenient to work with in the code
-            # we will update the original variables afterwards
-            #self.air_ap['q'] = self.air_ap.QABS/1000.
-
-            self.air_ap = \
-                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
-            # we require the temperature fields, since we need to consider
-            # advection
-            # if self.sw_ac:
-            #     #self.air_ap['theta'] = self.air_ap['t'] *
-
-            #     # we consider self.sp in case of air-circulation input (for
-            #     # consistence)
-            #     self.air_ap['t'] = \
-            #                 self.air_ap.theta *  \
-            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
-            # else:
-            # we consider self.Ps in case of balloon input only 
-            self.air_ap = self.air_ap.assign(t = lambda x: \
-                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
-
-            #self.air_ap['theta'] = self.air_ap.THTA
-            if 'u' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
-            if 'v' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
-
-            for var in ['theta','q','u','v']:
-
-                
-                if self.air_ap[var][1] != self.air_ap[var][0]:
-                    raise ValueError("Error input profile consistency: two \
-                                     lowest profile levels for "+var+" should \
-                                     be equal.")
-                
-                # initialize the value from its profile when available
-                value_old = self.__dict__[var]
-                value_new = self.air_ap[var][indexh[0][0]]
-                
-                if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided \
-                                     ("+str(value_old)+ "kg kg-1), \
-                                     but it is now overwritten by the first \
-                                     level (index 0) of air_ap]var\ which is \
-                                     different (" +str(value_new)+"K).")
-                                        
-                self.__dict__[var] = value_new
-
-                # make a profile of the stratification 
-                # please note that the stratification between z_pro[i] and
-                # z_pro[i+1] is given by air_ap.GTHT[i]
-
-                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
-                # np.gradient(self.z_pro)
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
-
-
-                self.__dict__['gamma'+var] = \
-                    self.air_ap['gamma'+var][np.where(self.h >= \
-                                                     self.air_ap.z)[0][-1]]
-
-
-
-        # the variable p_pro is just for diagnosis of lifted index
-            
-            
-
-            # input Ph is wrong, so we correct it according to hydrostatic equation
-            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-
-            #if self.sw_ac:
-                # note that we use sp as surface pressure, which is determined
-                # from era-interim instead of the observations. This is to
-                # avoid possible failure of the interpolation routine
-                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
-                #                          + \
-                #                          list(self.air_ap.p[3:]))
-
-            # else:
-                # in the other case, it is updated at the time of calculting
-                # the statistics 
-
-# END -- HW 20170606      
-        #print(self.air_ap)
-
-        if self.sw_ac and not self.sw_ap:
-            raise ValueError("air circulation switch only possible when air \
-                             profiles are given")
-        
-        if self.sw_ac:
-
-            # # # we comment this out, because subsidence is calculated
-            # according to advection
-            # #interpolate subsidence towards the air_ap height coordinate
-            # self.air_ap['w'] = np.interp(self.air_ap.p,\
-            #                               self.air_ac.p,\
-            #                               self.air_ac.w) 
-            # #subsidence at the mixed-layer top
-            # self.w = self.air_ap.w[1]
-        
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-                # in case we didn't find any points, we just take the lowest one.
-                # actually, this can happen if ERA-INTERIM pressure levels are
-                # inconsistent with 
-                if in_ml.sum() == 0:
-                    warnings.warn(" no circulation points in the mixed layer \
-                                  found. We just take the bottom one.")
-                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-
-                for var in ['t','q','u','v']:
-    
-                   # calculation of the advection variables for the mixed layer
-                   # we weight by the hydrostatic thickness of each layer and
-                   # divide by the total thickness
-                   self.__dict__['adv'+var] = \
-                            ((self.air_ac['adv'+var+'_x'][in_ml] \
-                             + \
-                             self.air_ac['adv'+var+'_y'][in_ml])* \
-                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                            self.air_ac['delpdgrav'][in_ml].sum()
-
-                   # calculation of the advection variables for the profile above
-                   # (lowest 3 values are not used by class)
-                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
-                   self.air_ap['adv'+var] = \
-                           np.interp(self.air_ap.p,\
-                                     self.air_ac.p,\
-                                     self.air_ac['adv'+var+'_x']) \
-                           + \
-                           np.interp(self.air_ap.p, \
-                                       self.air_ac.p, \
-                                       self.air_ac['adv'+var+'_y'])
-
-                # as an approximation, we consider that advection of theta in the
-                # mixed layer is equal to advection of t. This is a sufficient
-                # approximation since theta and t are very similar at the surface
-                # pressure.
-                self.__dict__['advtheta'] = self.__dict__['advt']
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # self.wrho = np.interp(self.P_h,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) 
-            # self.ws   = self.air_ap.w.iloc[1]
-
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                self.air_ap = self.air_ap.assign(wp = 0.)
-                self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                              self.air_ac.p, \
-                                              self.air_ac['wp'])
-                self.air_ap = self.air_ap.assign(R = 0.)
-                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                     self.Rv*self.air_ap.q)
-                self.air_ap = self.air_ap.assign(rho = 0.)
-                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-                
-                self.air_ap = self.air_ap.assign(w = 0.)
-                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-                #print('hello w ini')
-
-                # Note: in case of sw_ac is False, we update it from prescribed
-                # divergence
-                self.ws   = self.air_ap.w[1]
-
-                # self.ws   = self.wrho/self.rho
-                # self.ws   = self.wrho/(self.P_h/ \
-                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
-                #                         self.theta) # this should be T!!!
-
-                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-                #                         + \
-                #                         self.air_ac['divU_y'][in_ml])* \
-                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                #             self.air_ac['delpdgrav'][in_ml].sum() \
-        
-
-        # Tendencies 
-        self.htend      = None                  # tendency of CBL [m s-1]
-        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
-        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
-        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
-        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
-        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
-        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
-        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
-        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
-        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
-        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
-        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
-  
-        # initialize surface layer
-        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
-        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
-        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
-        self.z0m        = self.input.z0m        # roughness length for momentum [m]
-        self.z0h        = self.input.z0h        # roughness length for scalars [m]
-        self.Cm         = 1e12                  # drag coefficient for momentum [-]
-        self.Cs         = 1e12                  # drag coefficient for scalars [-]
-        self.L          = None                  # Obukhov length [m]
-        self.Rib        = None                  # bulk Richardson number [-]
-        self.ra         = None                  # aerodynamic resistance [s m-1]
-  
-        # initialize radiation
-        self.lat        = self.input.lat        # latitude [deg]
-        #self.fc         = self.input.fc         # coriolis parameter [s-1]
-        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
-        self.lon        = self.input.lon        # longitude [deg]
-        self.doy        = self.input.doy        # day of the year [-]
-        self.tstart     = self.input.tstart     # time of the day [-]
-        self.cc         = self.input.cc         # cloud cover fraction [-]
-        self.Swin       = None                  # incoming short wave radiation [W m-2]
-        self.Swout      = None                  # outgoing short wave radiation [W m-2]
-        self.Lwin       = None                  # incoming long wave radiation [W m-2]
-        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
-        self.Q          = self.input.Q          # net radiation [W m-2]
-        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
-  
-        # initialize land surface
-        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
-        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
-        self.T2         = self.input.T2         # temperature deeper soil layer [K]
-                           
-        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
-        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
-        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
-        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
-                           
-        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
-        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
-        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
-                           
-        self.C1sat      = self.input.C1sat      
-        self.C2ref      = self.input.C2ref      
-
-        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
-        
-        self.LAI        = self.input.LAI        # leaf area index [-]
-        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
-        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = self.input.alpha      # surface albedo [-]
-  
-        self.rs         = 1.e6                  # resistance transpiration [s m-1]
-        self.rssoil     = 1.e6                  # resistance soil [s m-1]
-                           
-        self.Ts         = self.input.Ts         # surface temperature [K]
-                           
-        self.cveg       = self.input.cveg       # vegetation fraction [-]
-        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
-        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
-        self.cliq       = None                  # wet fraction [-]
-                          
-        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
-  
-        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
-        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
-        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
-  
-        self.H          = None                  # sensible heat flux [W m-2]
-        self.LE         = None                  # evapotranspiration [W m-2]
-        self.LEliq      = None                  # open water evaporation [W m-2]
-        self.LEveg      = None                  # transpiration [W m-2]
-        self.LEsoil     = None                  # soil evaporation [W m-2]
-        self.LEpot      = None                  # potential evaporation [W m-2]
-        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
-        self.G          = None                  # ground heat flux [W m-2]
-
-        # initialize A-Gs surface scheme
-        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
-
-        # initialize cumulus parameterization
-        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
-        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
-        self.ac         = 0.                    # Cloud core fraction [-]
-        self.M          = 0.                    # Cloud core mass flux [m s-1] 
-        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
-  
-        # initialize time variables
-        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
-        self.dt     = self.input.dt
-        self.dtcur      = self.dt
-        self.firsttime = True
-        self.t      = 0
- 
-        # Some sanity checks for valid input
-        if (self.c_beta is None): 
-            self.c_beta = 0                     # Zero curvature; linear response
-        assert(self.c_beta >= 0 or self.c_beta <= 1)
-
-        # initialize output
-        self.out = model_output(self.tsteps)
- 
-        self.statistics()
-  
-        # calculate initial diagnostic variables
-        if(self.sw_rad):
-            self.run_radiation()
- 
-        if(self.sw_sl):
-            for i in range(10): 
-                self.run_surface_layer()
-  
-        if(self.sw_ls):
-            self.run_land_surface()
-
-        if(self.sw_cu):
-            self.run_mixed_layer()
-            self.run_cumulus()
-        
-        if(self.sw_ml):
-            self.run_mixed_layer()
-
-    def timestep(self):
-
-        self.dtmax = +np.inf
-        self.logger.debug('before stats') 
-        self.statistics()
-
-        # run radiation model
-        self.logger.debug('before rad') 
-        if(self.sw_rad):
-            self.run_radiation()
-  
-        # run surface layer model
-        if(self.sw_sl):
-            self.logger.debug('before surface layer') 
-            self.run_surface_layer()
-        
-        # run land surface model
-        if(self.sw_ls):
-            self.logger.debug('before land surface') 
-            self.run_land_surface()
- 
-        # run cumulus parameterization
-        if(self.sw_cu):
-            self.logger.debug('before cumulus') 
-            self.run_cumulus()
-   
-        self.logger.debug('before mixed layer') 
-        # run mixed-layer model
-        if(self.sw_ml):
-            self.run_mixed_layer()
-        self.logger.debug('after mixed layer') 
- 
-        #get first profile data point above mixed layer
-        if self.sw_ap:
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                # here we correct for the fact that the upper profile also
-                # shifts in the vertical.
-
-                diffhtend = self.htend - self.air_ap.w[zidx_first]
-                if diffhtend > 0:
-                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            else:
-                if self.htend > 0:
-                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            #print(self.h,zidx_first,self.ws,self.air_ap.z)
-
-        
-        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
-        self.logger.debug('before store') 
-        self.substep =  (self.dtcur > self.dtmax)
-        if self.substep:
-            dtnext = self.dtcur - self.dtmax
-            self.dtcur = self.dtmax
-
-        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
-
-        # HW: this will be done multiple times in case of a substep is needed
-        # store output before time integration
-        if self.firsttime:
-            self.store()
-  
-        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
-        # time integrate land surface model
-        if(self.sw_ls):
-            self.integrate_land_surface()
-        self.logger.debug('before integrate mixed layer') 
-        # time integrate mixed-layer model
-        if(self.sw_ml):
-            self.integrate_mixed_layer() 
-        self.logger.debug('after integrate mixed layer') 
-        if self.substep:
-            self.dtcur = dtnext
-            self.firsttime = False
-            self.substeps += 1
-        else:
-            self.dtcur = self.dt
-            self.t += 1 
-            self.firsttime = True
-            self.substeps = 0
-        self.logger.debug('going to next step')
-        
-        
-  
-    def statistics(self):
-        # Calculate virtual temperatures 
-        self.thetav   = self.theta  + 0.61 * self.theta * self.q
-        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
-        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
-        # Mixed-layer top properties
-        self.P_h    = self.Ps - self.rho * self.g * self.h
-        # else:
-            # in the other case, it is updated at the time that the profile is
-            # updated (and at the initialization
-
-        self.T_h    = self.theta - self.g/self.cp * self.h
-
-        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
-        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
-
-        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
-
-        # Find lifting condensation level iteratively
-        if(self.t == 0):
-            self.lcl = self.h
-            RHlcl = 0.5
-        else:
-            RHlcl = 0.9998 
-
-        itmax = 30
-        it = 0
-        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
-            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
-            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
-        else:
-            self.q2_h   = 0.
-            self.CO22_h = 0.
-
-        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
-        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
-        self.M      = self.ac * self.wstar
-        self.wqM    = self.M * self.q2_h**0.5
-
-        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
-        if(self.dCO2 < 0):
-            self.wCO2M  = self.M * self.CO22_h**0.5
-        else:
-            self.wCO2M  = 0.
-
-    def run_mixed_layer(self):
-        if(not self.sw_sl):
-            # decompose ustar along the wind components
-            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
-            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
-
-
-
-        # calculate large-scale vertical velocity (subsidence)
-        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
-            self.ws = -self.divU * self.h
-        # else:
-        #     in case the air circulation switch is turned on, subsidence is
-        #     calculated from the circulate profile at the initialization and
-        #     in the integrate_mixed_layer routine
-              
-        # calculate compensation to fix the free troposphere in case of subsidence 
-        if(self.sw_fixft):
-            w_th_ft  = self.gammatheta * self.ws
-            w_q_ft   = self.gammaq     * self.ws
-            w_CO2_ft = self.gammaCO2   * self.ws 
-        else:
-            w_th_ft  = 0.
-            w_q_ft   = 0.
-            w_CO2_ft = 0. 
-      
-        # calculate mixed-layer growth due to cloud top radiative divergence
-        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
-       
-        # calculate convective velocity scale w* 
-        if(self.wthetav > 0.):
-            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
-        else:
-            self.wstar  = 1e-6;
-      
-        # Virtual heat entrainment flux 
-        self.wthetave    = -self.beta * self.wthetav 
-        
-        # compute mixed-layer tendencies
-        if(self.sw_shearwe):
-            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
-        else:
-            self.we    = -self.wthetave / self.dthetav
-        # Don't allow boundary layer shrinking if wtheta < 0 
-        if(self.we < 0):
-            self.we = 0.
-
-        # Calculate entrainment fluxes
-        self.wthetae     = -self.we * self.dtheta
-        self.wqe         = -self.we * self.dq
-        self.wCO2e       = -self.we * self.dCO2
-        
-        htend_pre       = self.we + self.ws + self.wf - self.M
-        
-        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-        
- 
-        #print('thetatend_pre',thetatend_pre)
-        
-        #preliminary boundary-layer top chenage
-        #htend_pre = self.we + self.ws + self.wf - self.M
-        #preliminary change in temperature jump
-        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
-                          thetatend_pre + w_th_ft
-        
-        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
-        l_entrainment = True
-
-        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
-            l_entrainment = False
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! temperature jump is at the lower limit \
-                          and is not growing: entrainment is disabled for this (sub)timestep.") 
-        elif dtheta_pre < 0.1:
-            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
-            l_entrainment = True
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          " Warning! Potential temperature jump at mixed- \
-                          layer height would become too low limiting timestep \
-                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
-            self.dtmax = min(self.dtmax,dtmax_new)
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "next subtimestep, entrainment will be disabled")
-            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
-
-
-
-        # when entrainment is disabled, we just use the simplified formulation
-        # as in Wouters et al., 2013 (section 2.2.1)
-
-        self.dthetatend = l_entrainment*dthetatend_pre + \
-                        (1.-l_entrainment)*0.
-        self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
-        self.htend = l_entrainment*htend_pre + \
-                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
-        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
-        #stop
-
-
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
-
-
-        # self.qtend = l_entrainment*qtend_pre + \
-        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
-        # self.CO2tend = l_entrainment*CO2tend_pre + \
-        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-
-
-        #     # part of the timestep for which the temperature mixed-layer jump
-        #     # was changing, and for which entrainment took place. For the other
-        #     # part, we don't assume entrainment anymore, and we use the
-        #     # simplified formulation  of Wouters et al., 2013
-
-        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
-        #   
-        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
-        #                      self.dthetatend + w_th_ft) + \
-        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
-        #     self.htend = fac*self.htend + \
-        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
-        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
-        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-
-        # else:
-        #     #self.htend = htend_pre
-        #     self.dthetatend = dthetatend_pre
-        #     self.thetatend = thetatend_pre
-        
-        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
-     
-        # assume u + du = ug, so ug - u = du
-        if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
-  
-            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
-        
-        # tendency of the transition layer thickness
-        if(self.ac > 0 or self.lcl - self.h < 300):
-            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
-        else:
-            self.dztend = 0.
-
-   
-    def integrate_mixed_layer(self):
-        # set values previous time step
-        h0      = self.h
-        
-        theta0  = self.theta
-        dtheta0 = self.dtheta
-        q0      = self.q
-        dq0     = self.dq
-        CO20    = self.CO2
-        dCO20   = self.dCO2
-        
-        u0      = self.u
-        du0     = self.du
-        v0      = self.v
-        dv0     = self.dv
-
-        dz0     = self.dz_h
-  
-        # integrate mixed-layer equations
-        
-            
-
-# END -- HW 20170606        
-        self.h        = h0      + self.dtcur * self.htend
-        # print(self.h,self.htend)
-        # stop
-        self.theta    = theta0  + self.dtcur * self.thetatend
-        #print(dtheta0,self.dtcur,self.dthetatend)
-        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
-        self.q        = q0      + self.dtcur * self.qtend
-        self.dq       = dq0     + self.dtcur * self.dqtend
-        self.CO2      = CO20    + self.dtcur * self.CO2tend
-        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
-        self.dz_h     = dz0     + self.dtcur * self.dztend
-            
-        # Limit dz to minimal value
-        dz0 = 50
-        if(self.dz_h < dz0):
-            self.dz_h = dz0 
-  
-        if(self.sw_wind):
-            self.u        = u0      + self.dtcur * self.utend
-            self.du       = du0     + self.dtcur * self.dutend
-            self.v        = v0      + self.dtcur * self.vtend
-            self.dv       = dv0     + self.dtcur * self.dvtend
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-            for var in ['t','q','u','v']:
-                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
-
-            # take into account advection for the whole profile
-                
-                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
-
-            var = 'z'
-            #print(self.air_ap[var])
-                #     print(self.air_ap['adv'+var])
-
-
-
-
-            #moving the profile vertically according to the vertical wind
-                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
-
-
-            # air_apvarold = pd.Series(np.array(self.air_ap.z))
-            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
-            # stop
-
-
-                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
-                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
-
-            #As t is updated, we also need to recalculate theta (and R)
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-
-            # air_aptheta_old = pd.Series(self.air_ap['theta'])
-            self.air_ap['theta'] = \
-                        self.air_ap.t * \
-                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
-                                         self.dtcur * self.air_ap.w[zidx_first:]
-
-#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
-#            print(self.t, self.dtcur,self.dt,self.htend)
-
-            # # the pressure levels of the profiles are recalculated according to
-            # # there new height (after subsidence)
-            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
-            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
-            #         * self.dtcur *  self.air_ap.w[zidx_first:]
-
-            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
-                    self.dtcur * self.air_ap.wp[zidx_first:]
-
-            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
-        # note that theta and q itself are updatet by class itself
-
-    
-        if self.sw_ap:
-            # Just for model consistency preservation purposes, we set the
-            # theta variables of the mixed-layer to nan values, since the
-            # mixed-layer values should overwritte by the mixed-layer
-            # calculations of class.
-            self.air_ap['theta'][0:3] = np.nan 
-            self.air_ap['p'][0:3] = np.nan 
-            self.air_ap['q'][0:3] = np.nan 
-            self.air_ap['u'][0:3] = np.nan 
-            self.air_ap['v'][0:3] = np.nan 
-            self.air_ap['t'][0:3] = np.nan 
-            self.air_ap['z'][0:3] = np.nan 
-
-            # Update the vertical profiles: 
-            #   - new mixed layer properties( h, theta, q ...)
-            #   - any data points below the new ixed-layer height are removed
-
-            # Three data points at the bottom that describe the mixed-layer
-            # properties
-            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
-                                           # columns as air_ap
-            # air_ap_head['z'].iloc[0] = 2.
-            # air_ap_head['z'].iloc[1] = self.__dict__['h']
-            # air_ap_head['z'].iloc[2] = self.__dict__['h']
-            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
-                        [2.,self.__dict__['h'],self.__dict__['h']]
-            for var in ['theta','q','u','v']:
-
-                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
-                        [self.__dict__[var], \
-                         self.__dict__[var], \
-                         self.__dict__[var] + self.__dict__['d'+var]]
-                
-            #print(self.air_ap)
-
-            # This is the remaining profile considering the remaining
-            # datapoints above the mixed layer height
-            air_ap_tail = self.air_ap.iloc[3:]
-            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
-
-            # print('h',self.h)
-            # # only select samples monotonically increasing with height
-            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            # air_ap_tail = pd.DataFrame()
-            # theta_low = self.theta
-            # z_low =     self.h
-            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            # for ibottom in range(1,len(air_ap_tail_orig)):
-            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
-            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-            # make theta increase strong enough to avoid numerical
-            # instability
-            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            air_ap_tail = pd.DataFrame()
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            theta_low = self.theta
-            z_low =     self.h
-            ibottom = 0
-            itop = 0
-            # print(air_ap_tail_orig)
-            # stop
-
-            # HW: this is the lower limit that we use for gammatheta, which is
-            # there to avoid model crashes. Besides on this limit, the upper
-            # air profile is modified in a way that is still conserves total
-            # quantities of moisture and temperature. The limit is set by trial
-            # and error. The numerics behind the crash should be investigated
-            # so that a cleaner solution can be provided.
-            gammatheta_lower_limit = 0.002
-            while ((itop in range(0,1)) or (itop != ibottom)):
-                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-                if (
-                    #(z_mean > (z_low+0.2)) and \
-                    #(theta_mean > (theta_low+0.02) ) and \
-                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
-                  (itop >= (len(air_ap_tail_orig)-1)) \
-                   :
-
-                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                    ibottom = itop+1
-                    theta_low = air_ap_tail.theta.iloc[-1]
-                    z_low =     air_ap_tail.z.iloc[-1]
-    
-
-                itop +=1
-                # elif  (itop > len(air_ap_tail_orig)-10):
-                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-                #print(itop,ibottom)
-
-            if itop > 1:
-                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! Temperature profile was too steep. \
-                                  Modifying profile: "+ \
-                                  str(itop - 1)+ " measurements were dropped \
-                                  and replaced with its average \
-                                  Modifying profile. \
-                                  mean with next profile point(s).") 
-
-
-            self.air_ap = pd.concat((air_ap_head,\
-                                     air_ap_tail,\
-                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
-                                                                      axis=1)
-
-            if  self.sw_ac:
-                qvalues = \
-                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
-
-                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
-                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
-                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-                self.P_h    = self.Ps - self.rho * self.g * self.h
-                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
-                        [self.Ps,  self.P_h, self.P_h-0.1]
-
-                self.air_ap.t = \
-                            self.air_ap.theta * \
-                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
-
-
-        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
-
-
-
-
-        # else:
-            # in the other case, it is updated at the time the statistics are
-            # calculated 
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if in_ml.sum() == 0:
-                warnings.warn(" no circulation points in the mixed layer \
-                              found. We just take the bottom one.")
-                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-            for var in ['t','q','u','v']:
-
-                # calculation of the advection variables for the mixed-layer
-                # these will be used for the next timestep
-                # Warning: w is excluded for now.
-
-                self.__dict__['adv'+var] = \
-                        ((self.air_ac['adv'+var+'_x'][in_ml] \
-                         + \
-                         self.air_ac['adv'+var+'_y'][in_ml])* \
-                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                        self.air_ac['delpdgrav'][in_ml].sum()
-
-                # calculation of the advection variables for the profile above
-                # the mixed layer (also for the next timestep)
-                self.air_ap['adv'+var] = \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p,\
-                                              self.air_ac['adv'+var+'_x']) \
-                                    + \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p, \
-                                              self.air_ac['adv'+var+'_y'])
-                # if var == 't':
-                #     print(self.air_ap['adv'+var])
-                #     stop
-
-            # as an approximation, we consider that advection of theta in the
-            # mixed layer is equal to advection of t. This is a sufficient
-            # approximation since theta and t are very similar at the surface
-            # pressure.
-
-            self.__dict__['advtheta'] = self.__dict__['advt']
-
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            # update the vertical wind profile
-            self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                          self.air_ac.p, \
-                                          self.air_ac['wp'])
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-            
-            air_apwold = self.air_ap['w']
-            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-            #print('hello w upd')
-
-            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # # self.wrho = np.interp(self.P_h,\
-            # #                      self.air_ach.p,\
-            # #                      self.air_ach['wrho']) \
-
-
-
-            # Also update the vertical wind at the mixed-layer height
-            # (subsidence)
-            self.ws   = self.air_ap.w[1]
-        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
-
-            ## Finally, we update he 
-            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-            #                        + \
-            #                        self.air_ac['divU_y'][in_ml])* \
-            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-            #            self.air_ac['delpdgrav'][in_ml].sum() 
-            
-
-        if self.sw_ap:
-            for var in ['theta','q','u','v']:
-
-                # update of the slope (gamma) for the different variables, for
-                # the next timestep!
-
-                # there is an warning message that tells about dividing through
-                # zero, which we ignore
-
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                    # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap['gamma'+var] = gammavar
-
-                # Based on the above, update the gamma value at the mixed-layer
-                # top
-                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
-                                                                     self.air_ap.z)[0][-1]]
-
-            
-    def run_radiation(self):
-        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
-        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
-        sinlea = max(sinlea, 0.0001)
-        
-        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
-  
-        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
-  
-        self.Swin  = self.S0 * Tr * sinlea
-        self.Swout = self.alpha * self.S0 * Tr * sinlea
-        
-        
-        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
-        self.Lwout = self.bolz * self.Ts ** 4.
-          
-        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
-        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
-  
-    def run_surface_layer(self):
-        # HW: I had to raise the minimum wind speed to make the simulation with
-        # the non-iterative solution stable (this solution was a wild guess, so I don't
-        # know the exact problem of the instability in case of very low wind
-        # speeds yet)
-        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        # version of 20180730 where there are still some runs crashing. Maybe
-        # an upper limit should be set on the monin-obukhov length instead of
-        # a lower limmit on the wind speed?
-        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        
-        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
-        qsatsurf       = qsat(self.thetasurf, self.Ps)
-        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
-        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
-
-        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
-  
-        zsl       = 0.1 * self.h
-        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
-        
-
-
-        if self.sw_lit:
-            self.Rib  = min(self.Rib, 0.2)
-            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
-            self.zeta  = zsl/self.L
-            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
-            
-        
-            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
-            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
-            
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-        
-     
-            # diagnostic meteorological variables
-            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
-            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
-            
-            # diagnostic meteorological variables
-        else:
-            
-            ## circumventing any iteration with Wouters et al., 2012
-            self.zslz0m = np.max((zsl/self.z0m,10.))
-            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
-            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
-            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
-            self.L = zsl/self.zeta
-            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
-        
-            self.Cm = self.k**2.0/funm/funm
-            self.Cs = self.k**2.0/funm/funh
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-            
-            # extrapolation from mixed layer (instead of from surface) to 2meter
-            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
-            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
-            self.u2m    =                - self.uw     / self.ustar / self.k * funm
-            self.v2m    =                - self.vw     / self.ustar / self.k * funm
-        
-        
-        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
-        self.e2m    = self.q2m * self.Ps / 0.622
-     
-    def ribtol(self, Rib, zsl, z0m, z0h): 
-        if(Rib > 0.):
-            L    = 1.
-            L0   = 2.
-        else:
-            L  = -1.
-            L0 = -2.
-        #print(Rib,zsl,z0m,z0h)
-        
-        while (abs(L - L0) > 0.001):
-            L0      = L
-            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
-            Lstart  = L - 0.001*L
-            Lend    = L + 0.001*L
-            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
-                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-            L       = L - fx / fxdif
-            #print(L)
-            if(abs(L) > 1e12):
-                break
-
-        return L
-      
-    def psim(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psim = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-        return psim
-      
-    def psih(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psih  = 2. * np.log( (1. + x*x) / 2.)
-            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-        return psih
- 
-    def jarvis_stewart(self):
-        # calculate surface resistances using Jarvis-Stewart model
-        if(self.sw_rad):
-            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
-        else:
-            f1 = 1.
-  
-        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
-            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
-        else:
-            f2 = 1.e8
- 
-        # Limit f2 in case w2 > wfc, where f2 < 1
-        f2 = max(f2, 1.);
- 
-        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
-        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
-  
-        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
-
-    def factorial(self,k):
-        factorial = 1
-        for n in range(2,k+1):
-            factorial = factorial * float(n)
-        return factorial;
-
-    def E1(self,x):
-        E1sum = 0
-        for k in range(1,100):
-            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
-        return -0.57721566490153286060 - np.log(x) - E1sum
- 
-    def ags(self):
-        # Select index for plant type
-        if(self.c3c4 == 'c3'):
-            c = 0
-        elif(self.c3c4 == 'c4'):
-            c = 1
-        else:
-            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
-
-        # calculate CO2 compensation concentration
-        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
-
-        # calculate mesophyll conductance
-        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
-                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
-        gm            = gm / 1000. # conversion from mm s-1 to m s-1
-  
-        # calculate CO2 concentration inside the leaf (ci)
-        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
-        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
-  
-        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
-        D0            = (self.f0[c] - fmin) / self.ad[c]
-  
-        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
-        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
-        ci            = cfrac * (co2abs - CO2comp) + CO2comp
-  
-        # calculate maximal gross primary production in high light conditions (Ag)
-        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
-  
-        # calculate effect of soil moisture stress on gross assimilation rate
-        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
-  
-        # calculate stress function
-        if (self.c_beta == 0):
-            fstr = betaw;
-        else:
-            # Following Combe et al (2016)
-            if (self.c_beta < 0.25):
-                P = 6.4 * self.c_beta
-            elif (self.c_beta < 0.50):
-                P = 7.6 * self.c_beta - 0.3
-            else:
-                P = 2**(3.66 * self.c_beta + 0.34) - 1
-            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
-  
-        # calculate gross assimilation rate (Am)
-        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
-        Rdark        = (1. / 9.) * Am
-        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
-  
-        # calculate  light use efficiency
-        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
-  
-        # calculate gross primary productivity
-        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
-  
-        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
-        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
-        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
-  
-        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
-        a1           = 1. / (1. - self.f0[c])
-        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
-  
-        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
-  
-        # calculate surface resistance for moisture and carbon dioxide
-        self.rs      = 1. / (1.6 * gcco2)
-        rsCO2        = 1. / gcco2
-  
-        # calculate net flux of CO2 into the plant (An)
-        An           = -(co2abs - ci) / (self.ra + rsCO2)
-  
-        # CO2 soil surface flux
-        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
-        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
-  
-        # CO2 flux
-        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
-        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
-        self.wCO2    = self.wCO2A + self.wCO2R
- 
-    def run_land_surface(self):
-        # compute ra
-        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
-        #print('ueff',self.u,self.v,self.wstar)
-
-        if(self.sw_sl):
-          self.ra = (self.Cs * ueff)**-1.
-        else:
-          self.ra = ueff / max(1.e-3, self.ustar)**2.
-
-        #print('ra',self.ra,self.ustar,ueff)
-
-        # first calculate essential thermodynamic variables
-        self.esat    = esat(self.theta)
-        self.qsat    = qsat(self.theta, self.Ps)
-        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
-        self.dqsatdT = 0.622 * desatdT / self.Ps
-        self.e       = self.q * self.Ps / 0.622
-
-        if(self.ls_type == 'js'): 
-            self.jarvis_stewart() 
-        elif(self.ls_type == 'ags'):
-            self.ags()
-        else:
-            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
-
-        # recompute f2 using wg instead of w2
-        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
-          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
-        else:
-          f2        = 1.e8
-        self.rssoil = self.rssoilmin * f2 
- 
-        Wlmx = self.LAI * self.Wmax
-        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
-        self.cliq = min(1., self.Wl / Wlmx) 
-     
-        # calculate skin temperature implictly
-        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
-            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
-            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
-            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
-
-        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
-        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
-        #print('Ts',self.rs)
-
-        esatsurf      = esat(self.Ts)
-        self.qsatsurf = qsat(self.Ts, self.Ps)
-
-        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-  
-        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
-  
-        self.LE     = self.LEsoil + self.LEveg + self.LEliq
-        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
-        #print('H',self.ra,self.Ts,self.theta)
-        self.G      = self.Lambda * (self.Ts - self.Tsoil)
-        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
-        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
-        
-        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
-  
-        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
-   
-        d1          = 0.1
-        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
-        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
-        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
-        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
-  
-        # calculate kinematic heat fluxes
-        self.wtheta   = self.H  / (self.rho * self.cp)
-        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
-        self.wq       = self.LE / (self.rho * self.Lv)
- 
-    def integrate_land_surface(self):
-        # integrate soil equations
-        Tsoil0        = self.Tsoil
-        wg0           = self.wg
-        Wl0           = self.Wl
-  
-        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
-        self.wg       = wg0     + self.dtcur * self.wgtend
-        self.Wl       = Wl0     + self.dtcur * self.Wltend
-  
-    # store model output
-    def store(self):
-        t                      = self.t
-        
-        self.out.time[t]          = t * self.dt / 3600. + self.tstart
-
-        # in case we are at the end of the simulation, we store the vertical
-        # profiles to the output
-        
-        # if t == (len(self.out.time) - 1):
-        #     self.out.air_ac = self.air_ac
-        #     self.out.air_ap = self.air_ap
-
-        
-        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
-        #  for key in self.out.__dict__.keys():
-        #      if key in self.__dict__:
-        #          self.out.__dict__[key][t]  = self.__dict__[key]
-        
-        self.out.h[t]          = self.h
-        
-        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
-        
-        self.out.gammatheta[t] = self.gammatheta
-        self.out.gammau[t]     = self.gammau
-        self.out.gammav[t]     = self.gammav
-        self.out.gammaq[t]     = self.gammaq
-        self.out.theta[t]      = self.theta
-        self.out.thetav[t]     = self.thetav
-        self.out.dtheta[t]     = self.dtheta
-        self.out.dthetav[t]    = self.dthetav
-        self.out.wtheta[t]     = self.wtheta
-        self.out.wthetav[t]    = self.wthetav
-        self.out.wthetae[t]    = self.wthetae
-        self.out.wthetave[t]   = self.wthetave
-        
-        self.out.q[t]          = self.q
-        self.out.dq[t]         = self.dq
-        self.out.wq[t]         = self.wq
-        self.out.wqe[t]        = self.wqe
-        self.out.wqM[t]        = self.wqM
-      
-        self.out.qsat[t]       = self.qsat
-        self.out.e[t]          = self.e
-        self.out.esat[t]       = self.esat
-      
-        fac = (self.rho*self.mco2)/self.mair
-        self.out.CO2[t]        = self.CO2
-        self.out.dCO2[t]       = self.dCO2
-        self.out.wCO2[t]       = self.wCO2  * fac
-        self.out.wCO2e[t]      = self.wCO2e * fac
-        self.out.wCO2R[t]      = self.wCO2R * fac
-        self.out.wCO2A[t]      = self.wCO2A * fac
-
-        self.out.u[t]          = self.u
-        self.out.du[t]         = self.du
-        self.out.uw[t]         = self.uw
-        
-        self.out.v[t]          = self.v
-        self.out.dv[t]         = self.dv
-        self.out.vw[t]         = self.vw
-        
-        self.out.T2m[t]        = self.T2m
-        self.out.q2m[t]        = self.q2m
-        self.out.u2m[t]        = self.u2m
-        self.out.v2m[t]        = self.v2m
-        self.out.e2m[t]        = self.e2m
-        self.out.esat2m[t]     = self.esat2m
-
-
-        self.out.Tsoil[t]      = self.Tsoil
-        self.out.T2[t]         = self.T2
-        self.out.Ts[t]         = self.Ts
-        self.out.wg[t]         = self.wg
-        
-        self.out.thetasurf[t]  = self.thetasurf
-        self.out.thetavsurf[t] = self.thetavsurf
-        self.out.qsurf[t]      = self.qsurf
-        self.out.ustar[t]      = self.ustar
-        self.out.Cm[t]         = self.Cm
-        self.out.Cs[t]         = self.Cs
-        self.out.L[t]          = self.L
-        self.out.Rib[t]        = self.Rib
-  
-        self.out.Swin[t]       = self.Swin
-        self.out.Swout[t]      = self.Swout
-        self.out.Lwin[t]       = self.Lwin
-        self.out.Lwout[t]      = self.Lwout
-        self.out.Q[t]          = self.Q
-  
-        self.out.ra[t]         = self.ra
-        self.out.rs[t]         = self.rs
-        self.out.H[t]          = self.H
-        self.out.LE[t]         = self.LE
-        self.out.LEliq[t]      = self.LEliq
-        self.out.LEveg[t]      = self.LEveg
-        self.out.LEsoil[t]     = self.LEsoil
-        self.out.LEpot[t]      = self.LEpot
-        self.out.LEref[t]      = self.LEref
-        self.out.G[t]          = self.G
-
-        self.out.zlcl[t]       = self.lcl
-        self.out.RH_h[t]       = self.RH_h
-
-        self.out.ac[t]         = self.ac
-        self.out.M[t]          = self.M
-        self.out.dz[t]         = self.dz_h
-        self.out.substeps[t]   = self.substeps
-  
-    # delete class variables to facilitate analysis in ipython
-    def exitmodel(self):
-        del(self.Lv)
-        del(self.cp)
-        del(self.rho)
-        del(self.k)
-        del(self.g)
-        del(self.Rd)
-        del(self.Rv)
-        del(self.bolz)
-        del(self.S0)
-        del(self.rhow)
-  
-        del(self.t)
-        del(self.dt)
-        del(self.tsteps)
-         
-        del(self.h)          
-        del(self.Ps)        
-        del(self.fc)        
-        del(self.ws)
-        del(self.we)
-        
-        del(self.theta)
-        del(self.dtheta)
-        del(self.gammatheta)
-        del(self.advtheta)
-        del(self.beta)
-        del(self.wtheta)
-    
-        del(self.T2m)
-        del(self.q2m)
-        del(self.e2m)
-        del(self.esat2m)
-        del(self.u2m)
-        del(self.v2m)
-        
-        del(self.thetasurf)
-        del(self.qsatsurf)
-        del(self.thetav)
-        del(self.dthetav)
-        del(self.thetavsurf)
-        del(self.qsurf)
-        del(self.wthetav)
-        
-        del(self.q)
-        del(self.qsat)
-        del(self.dqsatdT)
-        del(self.e)
-        del(self.esat)
-        del(self.dq)
-        del(self.gammaq)
-        del(self.advq)
-        del(self.wq)
-        
-        del(self.u)
-        del(self.du)
-        del(self.gammau)
-        del(self.advu)
-        
-        del(self.v)
-        del(self.dv)
-        del(self.gammav)
-        del(self.advv)
-  
-        del(self.htend)
-        del(self.thetatend)
-        del(self.dthetatend)
-        del(self.qtend)
-        del(self.dqtend)
-        del(self.utend)
-        del(self.dutend)
-        del(self.vtend)
-        del(self.dvtend)
-     
-        del(self.Tsoiltend) 
-        del(self.wgtend)  
-        del(self.Wltend) 
-  
-        del(self.ustar)
-        del(self.uw)
-        del(self.vw)
-        del(self.z0m)
-        del(self.z0h)        
-        del(self.Cm)         
-        del(self.Cs)
-        del(self.L)
-        del(self.Rib)
-        del(self.ra)
-  
-        del(self.lat)
-        del(self.lon)
-        del(self.doy)
-        del(self.tstart)
-   
-        del(self.Swin)
-        del(self.Swout)
-        del(self.Lwin)
-        del(self.Lwout)
-        del(self.cc)
-  
-        del(self.wg)
-        del(self.w2)
-        del(self.cveg)
-        del(self.cliq)
-        del(self.Tsoil)
-        del(self.T2)
-        del(self.a)
-        del(self.b)
-        del(self.p)
-        del(self.CGsat)
-  
-        del(self.wsat)
-        del(self.wfc)
-        del(self.wwilt)
-  
-        del(self.C1sat)
-        del(self.C2ref)
-  
-        del(self.LAI)
-        del(self.rs)
-        del(self.rssoil)
-        del(self.rsmin)
-        del(self.rssoilmin)
-        del(self.alpha)
-        del(self.gD)
-  
-        del(self.Ts)
-  
-        del(self.Wmax)
-        del(self.Wl)
-  
-        del(self.Lambda)
-        
-        del(self.Q)
-        del(self.H)
-        del(self.LE)
-        del(self.LEliq)
-        del(self.LEveg)
-        del(self.LEsoil)
-        del(self.LEpot)
-        del(self.LEref)
-        del(self.G)
-  
-        del(self.sw_ls)
-        del(self.sw_rad)
-        del(self.sw_sl)
-        del(self.sw_wind)
-        del(self.sw_shearwe)
-
-# class for storing mixed-layer model output data
-class model_output:
-    def __init__(self, tsteps):
-        self.time          = np.zeros(tsteps)    # time [s]
-
-        # mixed-layer variables
-        self.h          = np.zeros(tsteps)    # ABL height [m]
-        
-        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammau     = np.zeros(tsteps)
-        self.gammav     = np.zeros(tsteps)
-        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
-        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
-        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
-        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
-        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
-        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
-        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
-        
-        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
-        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
-        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
-        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
-
-        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
-        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
-        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
-
-        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
-        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
-        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
-        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
-        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
-        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
-        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
-        
-        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
-        
-        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
-
-        # diagnostic meteorological variables
-        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
-        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
-        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
-        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
-        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
-        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
-
-        # ground variables
-        self.Tsoil       = np.zeros(tsteps)
-        self.T2          = np.zeros(tsteps)
-        self.Ts          = np.zeros(tsteps)
-        self.wg          = np.zeros(tsteps)
-
-        # surface-layer variables
-        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
-        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
-        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
-        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
-        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
-        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
-        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
-        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
-        self.L          = np.zeros(tsteps)    # Obukhov length [m]
-        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
-
-        # radiation variables
-        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
-        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
-        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
-        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
-        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
-
-        # land surface variables
-        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
-        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
-        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
-        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
-        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
-        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
-        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
-        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
-        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
-        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
-
-        # Mixed-layer top variables
-        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
-        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
-
-        # cumulus variables
-        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
-        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
-        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
-        
-        
-        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
-
-# class for storing mixed-layer model input data
-class model_input:
-    def __init__(self):
-
-        # # comment not valid
-        # we comment out the initialization, because there is a problem when
-        # inheriting values from one the another class4gl_iput. We also expect
-        # that the user specifies all the required parmameters (if not, an error
-        # is raised). 
-
-        # general model variables
-        self.runtime    = None  # duration of model run [s]
-        self.dt         = None  # time step [s]
-
-        # mixed-layer variables
-        self.sw_ml      = None  # mixed-layer model switch
-        self.sw_shearwe = None  # Shear growth ABL switch
-        self.sw_fixft   = None  # Fix the free-troposphere switch
-        self.h          = None  # initial ABL height [m]
-        self.Ps         = None  # surface pressure [Pa]
-        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
-        self.fc         = None  # Coriolis parameter [s-1]
-        
-        self.theta      = None  # initial mixed-layer potential temperature [K]
-        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
-
-        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
-
-        self.dtheta     = None  # initial temperature jump at h [K]
-        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = None  # advection of heat [K s-1]
-        self.beta       = None  # entrainment ratio for virtual heat [-]
-        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
-        
-        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
-        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
-        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
-
-        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = None  # advection of moisture [kg kg-1 s-1]
-        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
-
-        self.CO2        = None  # initial mixed-layer potential temperature [K]
-        self.dCO2       = None  # initial temperature jump at h [K]
-        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advCO2     = None  # advection of heat [K s-1]
-        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
-        
-        self.sw_wind    = None  # prognostic wind switch
-        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.du         = None  # initial u-wind jump at h [m s-1]
-        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = None  # advection of u-wind [m s-2]
-
-        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = None  # initial u-wind jump at h [m s-1]
-        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = None  # advection of v-wind [m s-2]
-
-        # surface layer variables
-        self.sw_sl      = None  # surface layer switch
-        self.ustar      = None  # surface friction velocity [m s-1]
-        self.z0m        = None  # roughness length for momentum [m]
-        self.z0h        = None  # roughness length for scalars [m]
-        self.Cm         = None  # drag coefficient for momentum [-]
-        self.Cs         = None  # drag coefficient for scalars [-]
-        self.L          = None  # Obukhov length [-]
-        self.Rib        = None  # bulk Richardson number [-]
-
-        # radiation parameters
-        self.sw_rad     = None  # radiation switch
-        self.lat        = None  # latitude [deg]
-        self.lon        = None  # longitude [deg]
-        self.doy        = None  # day of the year [-]
-        self.tstart     = None  # time of the day [h UTC]
-        self.cc         = None  # cloud cover fraction [-]
-        self.Q          = None  # net radiation [W m-2] 
-        self.dFz        = None  # cloud top radiative divergence [W m-2] 
-
-        # land surface parameters
-        self.sw_ls      = None  # land surface switch
-        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
-        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = None  # temperature top soil layer [K]
-        self.T2         = None  # temperature deeper soil layer [K]
-        
-        self.a          = None  # Clapp and Hornberger retention curve parameter a
-        self.b          = None  # Clapp and Hornberger retention curve parameter b
-        self.p          = None  # Clapp and Hornberger retention curve parameter p 
-        self.CGsat      = None  # saturated soil conductivity for heat
-        
-        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
-        self.wfc        = None  # volumetric water content field capacity [-]
-        self.wwilt      = None  # volumetric water content wilting point [-]
-        
-        self.C1sat      = None 
-        self.C2ref      = None
-
-        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
-        
-        self.LAI        = None  # leaf area index [-]
-        self.gD         = None  # correction factor transpiration for VPD [-]
-        self.rsmin      = None  # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = None  # surface albedo [-]
-        
-        self.Ts         = None  # initial surface temperature [K]
-        
-        self.cveg       = None  # vegetation fraction [-]
-        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
-        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
-        
-        self.Lambda     = None  # thermal diffusivity skin layer [-]
-
-        # A-Gs parameters
-        self.c3c4       = None  # Plant type ('c3' or 'c4')
-
-        # Cumulus parameters
-        self.sw_cu      = None  # Cumulus parameterization switch
-        self.dz_h       = None  # Transition layer thickness [m]
-        
-# BEGIN -- HW 20171027
-        # self.cala       = None      # soil heat conductivity [W/(K*m)]
-        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
-# END -- HW 20171027
diff --git a/runmodel.py b/runmodel.py
deleted file mode 100644
index fc4fd19..0000000
--- a/runmodel.py
+++ /dev/null
@@ -1,130 +0,0 @@
-#
-# Example of how to run the Python code, and access the output
-# This case is identical to the default setup of CLASS (the version with interface) 
-#
-
-from pylab import *
-from model import *
-
-""" 
-Create empty model_input and set up case
-"""
-run1input = model_input()
-
-run1input.dt         = 60.       # time step [s]
-run1input.runtime    = 12*3600    # total run time [s]
-
-# mixed-layer input
-run1input.sw_ml      = True      # mixed-layer model switch
-run1input.sw_shearwe = False     # shear growth mixed-layer switch
-run1input.sw_fixft   = False     # Fix the free-troposphere switch
-run1input.h          = 200.      # initial ABL height [m]
-run1input.Ps         = 101300.   # surface pressure [Pa]
-run1input.divU       = 0.        # horizontal large-scale divergence of wind [s-1]
-run1input.fc         = 1.e-4     # Coriolis parameter [m s-1]
-
-run1input.theta      = 288.      # initial mixed-layer potential temperature [K]
-run1input.dtheta     = 1.        # initial temperature jump at h [K]
-run1input.gammatheta = 0.006     # free atmosphere potential temperature lapse rate [K m-1]
-run1input.advtheta   = 0.        # advection of heat [K s-1]
-run1input.beta       = 0.2       # entrainment ratio for virtual heat [-]
-run1input.wtheta     = 0.1       # surface kinematic heat flux [K m s-1]
-
-run1input.q          = 0.008     # initial mixed-layer specific humidity [kg kg-1]
-run1input.dq         = -0.001    # initial specific humidity jump at h [kg kg-1]
-run1input.gammaq     = 0.        # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-run1input.advq       = 0.        # advection of moisture [kg kg-1 s-1]
-run1input.wq         = 0.1e-3    # surface kinematic moisture flux [kg kg-1 m s-1]
-
-run1input.CO2        = 422.      # initial mixed-layer CO2 [ppm]
-run1input.dCO2       = -44.      # initial CO2 jump at h [ppm]
-run1input.gammaCO2   = 0.        # free atmosphere CO2 lapse rate [ppm m-1]
-run1input.advCO2     = 0.        # advection of CO2 [ppm s-1]
-run1input.wCO2       = 0.        # surface kinematic CO2 flux [ppm m s-1]
-
-run1input.sw_wind    = False     # prognostic wind switch
-run1input.u          = 6.        # initial mixed-layer u-wind speed [m s-1]
-run1input.du         = 4.        # initial u-wind jump at h [m s-1]
-run1input.gammau     = 0.        # free atmosphere u-wind speed lapse rate [s-1]
-run1input.advu       = 0.        # advection of u-wind [m s-2]
-
-run1input.v          = -4.0      # initial mixed-layer u-wind speed [m s-1]
-run1input.dv         = 4.0       # initial u-wind jump at h [m s-1]
-run1input.gammav     = 0.        # free atmosphere v-wind speed lapse rate [s-1]
-run1input.advv       = 0.        # advection of v-wind [m s-2]
-
-run1input.sw_sl      = False     # surface layer switch
-run1input.ustar      = 0.3       # surface friction velocity [m s-1]
-run1input.z0m        = 0.02      # roughness length for momentum [m]
-run1input.z0h        = 0.002     # roughness length for scalars [m]
-
-run1input.sw_rad     = False     # radiation switch
-run1input.lat        = 51.97     # latitude [deg]
-run1input.lon        = -4.93     # longitude [deg]
-run1input.doy        = 268.      # day of the year [-]
-run1input.tstart     = 6.8       # time of the day [h UTC]
-run1input.cc         = 0.0       # cloud cover fraction [-]
-run1input.Q          = 400.      # net radiation [W m-2] 
-run1input.dFz        = 0.        # cloud top radiative divergence [W m-2] 
-
-run1input.sw_ls      = False     # land surface switch
-run1input.ls_type    = 'js'      # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-run1input.wg         = 0.21      # volumetric water content top soil layer [m3 m-3]
-run1input.w2         = 0.21      # volumetric water content deeper soil layer [m3 m-3]
-run1input.cveg       = 0.85      # vegetation fraction [-]
-run1input.Tsoil      = 285.      # temperature top soil layer [K]
-run1input.T2         = 286.      # temperature deeper soil layer [K]
-run1input.a          = 0.219     # Clapp and Hornberger retention curve parameter a
-run1input.b          = 4.90      # Clapp and Hornberger retention curve parameter b
-run1input.p          = 4.        # Clapp and Hornberger retention curve parameter c
-run1input.CGsat      = 3.56e-6   # saturated soil conductivity for heat
-
-run1input.wsat       = 0.472     # saturated volumetric water content ECMWF config [-]
-run1input.wfc        = 0.323     # volumetric water content field capacity [-]
-run1input.wwilt      = 0.171     # volumetric water content wilting point [-]
-
-run1input.C1sat      = 0.132     
-run1input.C2ref      = 1.8
-
-run1input.LAI        = 2.        # leaf area index [-]
-run1input.gD         = 0.0       # correction factor transpiration for VPD [-]
-run1input.rsmin      = 110.      # minimum resistance transpiration [s m-1]
-run1input.rssoilmin  = 50.       # minimun resistance soil evaporation [s m-1]
-run1input.alpha      = 0.25      # surface albedo [-]
-
-run1input.Ts         = 290.      # initial surface temperature [K]
-
-run1input.Wmax       = 0.0002    # thickness of water layer on wet vegetation [m]
-run1input.Wl         = 0.0000    # equivalent water layer depth for wet vegetation [m]
-
-run1input.Lambda     = 5.9       # thermal diffusivity skin layer [-]
-
-run1input.c3c4       = 'c3'      # Plant type ('c3' or 'c4')
-
-run1input.sw_cu      = False     # Cumulus parameterization switch
-run1input.dz_h       = 150.      # Transition layer thickness [m]
-
-"""
-Init and run the model
-"""
-r1 = model(run1input)
-r1.run()
-
-"""
-Plot output
-"""
-figure()
-subplot(131)
-plot(r1.out.t, r1.out.h)
-xlabel('time [h]')
-ylabel('h [m]')
-
-subplot(132)
-plot(r1.out.t, r1.out.theta)
-xlabel('time [h]')
-ylabel('theta [K]')
-
-subplot(133)
-plot(r1.out.t, r1.out.q*1000.)
-xlabel('time [h]')
-ylabel('q [g kg-1]')

From e530f499ae4d84c850de3e3a7f078171faa89dbf Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 21 Aug 2018 22:06:27 +0200
Subject: [PATCH 013/129]  almost initial version

---
 data_ground.py | 393 -------------------------------------------------
 1 file changed, 393 deletions(-)
 delete mode 100644 data_ground.py

diff --git a/data_ground.py b/data_ground.py
deleted file mode 100644
index d4e0b5a..0000000
--- a/data_ground.py
+++ /dev/null
@@ -1,393 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: vsc42247
-
-Purpose: Set surface conditions for the CLASS boundary-layer model
-"""
-
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-import pynacolada as pcd
-import pandas as pd
-
-def get_class4gl_ground(class_settings,**kwargs):   
-    
-    key = "IGBPDIS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-    
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        print('reading soil water saturation from '+input_fn)
-
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wsat'] = input_nc.variables['wsat'][ilon,ilat]
-        input_nc.close()
-
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc"
-        print('reading soil water field capacity from '+input_fn)
-    
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wfc'] = input_nc.variables['wfc'][ilon,ilat]
-        input_nc.close()
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc"
-        print('reading soil wilting point from '+input_fn)
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wwilt'] = input_nc.variables['wwp'][ilon,ilat]
-        input_nc.close()
-        
-    key = "GLEAM"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-        
-        #INPUT_gleam = gleam() 
-        #INPUT_gleam.path = "/kyukon/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/" 
-        
-        gleam_path = "/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/"
-        print('reading soil-water content for "+str(class_settings,datetime.year)+" from '+gleam_path)
-        
-        gleam_files = {}
-        
-        gleam_vars = ['SMroot','SMsurf']
-        
-        for VAR in gleam_vars:
-            gleam_files[VAR] = nc4.Dataset(gleam_path+'/'+str(class_settings.datetime.year)+'/'+VAR+'_'+str(class_settings.datetime.year)+'_GLEAM_v3.1a.nc','r')
-        
-
-        year = class_settings.datetime.year
-        day = class_settings.datetime.day
-        hour = class_settings.datetime.hour
-  
-        ilat = np.where(gleam_files['SMsurf'].variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(gleam_files['SMsurf'].variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        VAR = 'SMsurf'; class_settings.wg = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
-        VAR = 'SMroot'; class_settings.w2 = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
-        
-        for VAR in gleam_vars:
-            gleam_files[VAR].close()
-    
-    key = "MOD44B"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-    
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc"
-        print('initializing vegetation fraction from '+input_fn)
-        var = 'cveg'
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__[var] = input_nc.variables['fv'][ilon,ilat]
-        input_nc.close()
-        
-    key = "DSMW"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-         # Procedure of the thermal properties:
-         # 1. determine soil texture from DSMW
-         # 2. soil type with look-up table (according to DWD/EXTPAR)
-         # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987) 
-         #    with parameter look-up table from Noilhan and Planton (1989). 
-         #    Note: The look-up table is inspired on DWD/COSMO
-                 
-       
-        #preparing for soil thermal properties
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc"
-        
-        print("deriving soil thermal properties for the force-restore methodes from the soil texture file "+ input_fn)
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        DSMW = input_nc.variables['DSMW'][ilat,ilon]
-        
-        
-        #EXTPAR: zfine   = soil_texslo(soil_unit)%tex_fine
-        SP = {}; SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code']
-        for SPKEY in SPKEYS: 
-            SP[SPKEY] = np.array(input_nc.variables[SPKEY][DSMW])
-        input_nc.close()
-        
-        SP['texture'] = (0.5*SP['tex_medium']+1.0*SP['tex_coarse']) /(SP['tex_coarse']+SP['tex_medium']+SP['tex_fine'])
-        
-        if pd.isnull(SP['texture']):
-            print('Warning, texture is invalid> Setting to Ocean')
-            SP['itex'] = 9
-        
-        else:
-            SP['itex'] = int(SP['texture']*100)
-        
-        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-        SP['isoil'] = np.zeros_like(SP['itex'],dtype=np.int)
-        LOOKUP = [
-                  [0 ,7],# fine textured, clay (soil type 7)
-                  [20,6],# medium to fine textured, loamy clay (soil type 6)
-                  [40,5],# medium textured, loam (soil type 5)
-                  [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                  [80,3],# coarse textured, sand (soil type 3)
-                ]
-        for iitex,iisoil in LOOKUP: 
-            SP['isoil'][SP['itex'] >= iitex ] = iisoil 
-        
-        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-        LOOKUP = [
-                  [9001, 1 ], # ice, glacier (soil type 1) 
-                  [9002, 2 ], # rock, lithosols (soil type 2)
-                  [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                  [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                  [9,    9 ], # undefined (ocean)
-                  [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                  [9000, 9 ], # undefined (inland lake)
-                  [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                  [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                ]
-        # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-        for icode,iisoil in LOOKUP: 
-            SP['isoil'][SP['code'] == icode] = iisoil 
-        
-        #adopted from data_soil.f90 (COSMO5.0)
-        SP_LOOKUP = { 
-          # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea  
-          # (by index)                                           loam                    loam                                water      ice
-          'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-          'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-          'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-          'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-          'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-          'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-          'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-          'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-          'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-          'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-          'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-          'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-          'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-          'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-          'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-          'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-          'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-          'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-          'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-          #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-          'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , np.nan    , np.nan   ,  np.nan  ],
-          #error in table 2 of NP89: values need to be multiplied by e-6
-          'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-          'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , np.nan    , np.nan   ,  np.nan  ],
-          'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , np.nan    , np.nan   ,  np.nan  ],
-          'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , np.nan    , np.nan   ,  np.nan  ],
-          'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , np.nan    , np.nan   ,  np.nan  ],
-        }
-        
-        for SPKEY in SP_LOOKUP.keys(): 
-            SP[SPKEY] = np.zeros_like(SP['isoil'],dtype=np.float)
-        
-        for i in range(11):
-            SELECT = (SP['isoil'] == i)
-            for SPKEY in SP_LOOKUP.keys(): 
-                SP[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-        
-        for SPKEY in list(SP_LOOKUP.keys())[-6:]: 
-            var = SPKEY
-            class_settings.__dict__[var] = np.float(SP[SPKEY])
-            
-        # only print the last parameter value in the plot
-        
-        #inputs.append(cp.deepcopy(class_settings))
-        #var = 'cala'
-        #class_settings.__dict__[var] = np.float(SP['cala0'])
-        #valnew = class_settings.__dict__[var]
-        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-        
-        #inputs.append(cp.deepcopy(class_settings))
-        #var = 'crhoc'
-        #class_settings.__dict__[var] = np.float(SP['crhoc'])
-        #valnew = class_settings.__dict__[var]
-        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-        
-    key = "CERES"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
-        
-        CERES_start_date = dt.datetime(2000,3,1)
-        DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-        DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-        print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-            
-        var = 'cc'
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        print(class_settings.lat,class_settings.lon)
-        
-        class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:(idatetime+class_settings.runtime),ilat,ilon])/100.
-   
-        input_nc.close()
-    
-    key = "GIMMS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
-       
-    
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-        print("Reading Leag Area Index from "+input_fn)
-        var = 'LAI'
-        
-        #plt.plot
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-        
-        print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-        tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-        
-        if np.isnan(tarray[idatetime]):
-            print("interpolating GIMMS cveg nan value")
-            
-            mask = np.isnan(tarray)
-            if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-            else:
-                print("Warning. Could not interpolate GIMMS cveg nan value")
-                
-        class_settings.__dict__[var] = tarray[idatetime]
-        
-        input_nc.close()
- 
-    key = "IGBPDIS_ALPHA"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        var = 'alpha'
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-        print("Reading albedo from "+input_fn)
-    
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        
-        landfr = {}
-        for ltype in ['W','B','H','TC']:   
-            landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-        
-        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-        
-        alpha=0.
-        for ltype in landfr.keys():
-            alpha += landfr[ltype]*aweights[ltype]
-        
-        
-        class_settings.__dict__[var] = alpha
-        input_nc.close()        
-        
-        
-    key = "ERAINT_ST"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-        print("Reading soil temperature from "+input_fn)
-        
-        var = 'Tsoil'
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-        
-        
-        class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-        
-        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-        var = 'T2'
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-        
-        
-        class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-        
-        
-        input_nc.close()
-        
-        
-    
-    #inputs.append(cp.deepcopy(class_settings))
-    #var = 'T2'
-    #valold = class_settings.__dict__[var]
-    #
-    #class_settings.__dict__[var] = 305.
-    #class_settings.__dict__['Tsoil'] = 302.
-    #valnew = class_settings.__dict__[var]
-    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-    
-    
-    
-    #inputs.append(cp.deepcopy(class_settings))
-    #
-    #var = 'Lambda'
-    #valold = class_settings.__dict__[var]
-    
-    ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book. 
-    ## I need to ask Chiel.
-    ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-    #
-    #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg'] 
-    #class_settings.__dict__[var] = valnew
-    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-    
-    
-    
-    key = "GLAS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-        print("Reading canopy height for determining roughness length from "+input_fn)
-        var = 'z0m'
-    
-        
-        #plt.plot
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-        
-        lowerlimit = 0.01
-        if testval < lowerlimit:
-            print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-            class_settings.__dict__[var] = lowerlimit
-        else:
-            class_settings.__dict__[var] = testval
-        
-        class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-        
-        
-        input_nc.close()
-        

From 945a6513c28ee51f9884eb7f37abcba5a6556261 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 10:00:52 +0200
Subject: [PATCH 014/129] get the simultions.py working for first test release

---
 class4gl/__init__.py                          |   3 +-
 class4gl/__pycache__/class4gl.cpython-36.pyc  | Bin 28681 -> 28782 bytes
 .../__pycache__/data_global.cpython-36.pyc    | Bin 0 -> 17867 bytes
 .../interface_functions.cpython-36.pyc        | Bin 0 -> 10575 bytes
 .../interface_multi.cpython-36.pyc            | Bin 0 -> 31044 bytes
 class4gl/__pycache__/model.cpython-36.pyc     | Bin 36319 -> 36319 bytes
 class4gl/class4gl.py                          |   9 ++-
 class4gl/data_air.py                          |  17 ++++--
 class4gl/interface_functions.py               |   5 +-
 class4gl/model.py                             |   2 +-
 class4gl/ribtol/__init__.py                   |   2 +-
 .../__pycache__/__init__.cpython-36.pyc       | Bin 0 -> 294 bytes
 .../__pycache__/ribtol_hw.cpython-36.pyc      | Bin 0 -> 3455 bytes
 class4gl/simulations/simulations.py           |  57 +++++++++---------
 14 files changed, 56 insertions(+), 39 deletions(-)
 create mode 100644 class4gl/__pycache__/data_global.cpython-36.pyc
 create mode 100644 class4gl/__pycache__/interface_functions.cpython-36.pyc
 create mode 100644 class4gl/__pycache__/interface_multi.cpython-36.pyc
 create mode 100644 class4gl/ribtol/__pycache__/__init__.cpython-36.pyc
 create mode 100644 class4gl/ribtol/__pycache__/ribtol_hw.cpython-36.pyc

diff --git a/class4gl/__init__.py b/class4gl/__init__.py
index a21583b..f9c2212 100644
--- a/class4gl/__init__.py
+++ b/class4gl/__init__.py
@@ -1,4 +1,5 @@
-from . import model,class4gl,interface_multi,data_air,data_global
+# from . import model,class4gl,interface_multi,data_air,data_global
+from ribtol import *
 
 __version__ = '0.1.0'
 
diff --git a/class4gl/__pycache__/class4gl.cpython-36.pyc b/class4gl/__pycache__/class4gl.cpython-36.pyc
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diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 7baaa51..c87e0be 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -35,7 +35,14 @@
 from model import model_input
 from model import qsat
 #from data_soundings import wyoming 
-import Pysolar
+
+import importlib
+spam_loader = importlib.find_loader('Pysolar')
+found = spam_loader is not None
+if found:
+    import Pysolar
+else:
+    import pysolar as Pysolar
 import yaml
 import logging
 import warnings
diff --git a/class4gl/data_air.py b/class4gl/data_air.py
index 1c51deb..3860319 100644
--- a/class4gl/data_air.py
+++ b/class4gl/data_air.py
@@ -7,8 +7,17 @@
 import io
 import os
 import calendar
-import Pysolar
-import Pysolar.util
+
+import importlib
+spam_loader = importlib.find_loader('Pysolar')
+found = spam_loader is not None
+if found:
+    import Pysolar
+else:
+    import pysolar as Pysolar
+import Pysolar.util as Pysolarutil
+
+
 
 
 
@@ -360,7 +369,7 @@ def get_values_air_input(self,latitude=None,longitude=None):
             PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
             PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
             PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolarutil.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
             # This is the nearest datetime when sun is up (for class)
             PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
             # apply the same time shift for UTC datetime
@@ -371,7 +380,7 @@ def get_values_air_input(self,latitude=None,longitude=None):
             PARAMS['ldatetime'] = dt.datetime(1900,1,1)
             PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
             PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolarutil.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
             PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
             PARAMS['datetime_daylight'] =PARAMS['datetime'].value
 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 3e483f3..27f44b3 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -163,14 +163,12 @@ def __init__(self,path,suffix='ini',refetch_stations=False):
             self.table.to_csv(self.file)
         
         self.table = self.table.set_index('STNID')
-        #print(self.table)
 
     def get_stations(self,suffix):
         stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
         if len(stations_list_files) == 0:
             stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
         stations_list_files.sort()
-        print(stations_list_files)
         if len(stations_list_files) == 0:
             raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
         stations_list = []
@@ -190,7 +188,7 @@ def get_stations(self,suffix):
             yamlgen.close()
             thisfile.close()
     
-        print(stations_list)
+        #print(stations_list)
         return pd.DataFrame(stations_list)
 
 class stations_iterator(object):
@@ -345,6 +343,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
             # we try the old single-chunk filename format first (usually for
             # original profile pairs)
+            print('hello',STNID)
             fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
             if os.path.isfile(fn):
                 chunk = 0
diff --git a/class4gl/model.py b/class4gl/model.py
index 8760411..e6eeb07 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -27,7 +27,7 @@
 import sys
 import warnings
 import pandas as pd
-from ribtol_hw import zeta_hs2 , funcsche
+from ribtol.ribtol_hw import zeta_hs2 , funcsche
 import logging
 #from SkewT.thermodynamics import Density
 #import ribtol
diff --git a/class4gl/ribtol/__init__.py b/class4gl/ribtol/__init__.py
index a21583b..02a6a7d 100644
--- a/class4gl/ribtol/__init__.py
+++ b/class4gl/ribtol/__init__.py
@@ -1,4 +1,4 @@
-from . import model,class4gl,interface_multi,data_air,data_global
+from . import ribtol_hw
 
 __version__ = '0.1.0'
 
diff --git a/class4gl/ribtol/__pycache__/__init__.cpython-36.pyc b/class4gl/ribtol/__pycache__/__init__.cpython-36.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..ef32d81b7b590483a5671bdd87c06a3b3edb7c2e
GIT binary patch
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diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 719f9a5..b4257df 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -17,7 +17,6 @@
 parser.add_argument('--first-station-row')
 parser.add_argument('--last-station-row')
 parser.add_argument('--station-id') # run a specific station id
-parser.add_argument('--dataset')
 parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--error-handling',default='dump_on_success')
@@ -55,22 +54,23 @@
 }
 
 
-#SET = 'GLOBAL'
-SET = args.dataset
+# #SET = 'GLOBAL'
+# SET = args.dataset
 
-path_soundingsSET = args.path_soundings+'/'+SET+'/'
+# path_soundingsSET = args.path_soundings+'/'+SET+'/'
 
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
+print("getting stations")
+all_stations = stations(args.path_soundings,suffix='morning',refetch_stations=False)
 
 if args.global_chunk is not None:
+    
+    all_records_morning = get_records(all_stations.table,\
+                                  args.path_soundings,\
+                                  subset='morning',
+                                  refetch_records=False,
+                                  )
     totalchunks = 0
-    stations_iter = all_stations.iterrows()
+    stations_iter = all_stations.table.iterrows()
     in_current_chunk = False
     while not in_current_chunk:
         istation,current_station = stations_iter.__next__()
@@ -86,27 +86,28 @@
 
 else:
     if args.station_id is not None:
+        print("Selecting station by ID")
+        print(all_stations.table)
         stations_iter = stations_iterator(all_stations)
-        STNID,run_station = stations_iterator.set_STNID(STNID)
-        run_stations = pd.DataFrame(run_station)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        run_stations = pd.DataFrame([run_station])
     else:
-        run_stations = pd.DataFrame(all_stations)
+        print("Selecting stations from a row range in the table")
+        run_stations = pd.DataFrame(all_stations.table)
         if args.last_station_row is not None:
             run_stations = run_stations.iloc[:(int(args.last_station)+1)]
         if args.first_station_row is not None:
             run_stations = run_stations.iloc[int(args.first_station):]
-        run_station_chunk = args.station_chunk
+    run_station_chunk = args.station_chunk
 
 #print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
 records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
+                              args.path_soundings,\
                               subset='morning',
                               refetch_records=False,
                               )
 records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
+                                args.path_soundings,\
                                 subset='afternoon',
                                 refetch_records=False,
                                 )
@@ -119,7 +120,7 @@
 experiments = args.experiments.split(';')
 for expname in experiments:
     exp = EXP_DEFS[expname]
-    path_exp = args.path_experiments+'/'+SET+'_'+expname+'/'
+    path_exp = args.path_experiments+'/'+expname+'/'
 
     os.system('mkdir -p '+path_exp)
     for istation,current_station in run_stations.iterrows():
@@ -128,8 +129,8 @@
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            file_morning = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -166,10 +167,12 @@
                     c4gli_morning.update(source=expname, pars=exp)
 
                     c4gl = class4gl(c4gli_morning)
+                    print(args.error_handling)
 
                     if args.error_handling == 'dump_always':
                         try:
                             c4gl.run()
+                            print('run succesfull')
                         except:
                             print('run not succesfull')
                         onerun = True
@@ -184,12 +187,10 @@
                         onerun = True
                     # in this case, only the file will dumped if the runs were
                     # successful
-                    elif args.error_handling == 'dump_on_succes':
+                    elif args.error_handling == 'dump_on_success':
                         try:
                             c4gl.run()
-                            print('run not succesfull')
-                            onerun = True
-
+                            print('run succesfull')
                             c4gli_morning.dump(file_ini)
                             
                             
@@ -201,7 +202,7 @@
                         except:
                             print('run not succesfull')
 
-                #iexp = iexp +1
+
             file_ini.close()
             file_mod.close()
             file_morning.close()

From 14043c58bb8547747d8673f53d32ebb454bc27b6 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 14:11:54 +0200
Subject: [PATCH 015/129] add temporary file

---
 class4gl/.interface_functions.py.swp          | Bin 0 -> 40960 bytes
 .../interface_functions.cpython-36.pyc        | Bin 10575 -> 10538 bytes
 class4gl/interface_functions.py               |  50 ++++++++++--------
 class4gl/simulations/.simulations.py.swp      | Bin 0 -> 28672 bytes
 class4gl/simulations/simulations.py           |   9 +++-
 5 files changed, 34 insertions(+), 25 deletions(-)
 create mode 100644 class4gl/.interface_functions.py.swp
 create mode 100644 class4gl/simulations/.simulations.py.swp

diff --git a/class4gl/.interface_functions.py.swp b/class4gl/.interface_functions.py.swp
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diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 27f44b3..ddbf228 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -7,6 +7,10 @@
 from contextlib import suppress
 from time import sleep
 
+import tempfile
+
+
+
 
 sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl,units
@@ -47,32 +51,33 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
     filename = yaml_file.name
     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
     #yaml_file = open(filename)
+    shortfn = filename.split('/')[-1]
 
     #print('going to next observation',filename)
     yaml_file.seek(index_start)
 
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
-    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
+    filebuffer = open(gettempdir()+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
     filebuffer.write(buf)
     filebuffer.close()
     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
     
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
 
     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
     print(command)
     os.system(command)
-    jsonstream = open(filename+'.buffer.json.'+str(index_start))
+    jsonstream = open(gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
     record_dict = json.load(jsonstream)
     jsonstream.close()
-    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
+    os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.yaml.'+str(index_start))
 
 
     if mode =='mod':
         modelout = class4gl()
         modelout.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
 
         return modelout
     elif mode == 'ini':
@@ -99,9 +104,9 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
         #os.system('rm '+filename+'.buffer.json.'+str(index_start))
 
         c4gli = class4gl_input()
-        print(c4gli.logger,'hello')
+        #print(c4gli.logger,'hello')
         c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
+        os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
         return c4gli
 
 
@@ -365,14 +370,13 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
             # yamlfilenames = glob.glob(globyamlfilenames)
             # yamlfilenames.sort()
         else:
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
+            fn = format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
             dictfnchunks.append(dict(fn=fn,chunk=getchunk))
             
         if len(dictfnchunks) > 0:
             for dictfnchunk in dictfnchunks:
                 yamlfilename = dictfnchunk['fn']
                 chunk = dictfnchunk['chunk']
-                print(chunk)
 
                 #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
                 pklfilename = yamlfilename.replace('.yaml','.pkl')
@@ -380,25 +384,25 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                 #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
                 #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
                 generate_pkl = False
-                if not os.path.isfile(pklfilename): 
+                if not os.path.isfile(path_yaml+'/'+pklfilename): 
                     print('pkl file does not exist. I generate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
+                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                     generate_pkl = True
-                elif not (os.path.getmtime(yamlfilename) <  \
+                elif not (os.path.getmtime(path_yaml+'/'+yamlfilename) <  \
                     os.path.getmtime(pklfilename)):
                     print('pkl file older than yaml file, so I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
+                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                     generate_pkl = True
 
                 if refetch_records:
                     print('refetch_records flag is True. I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
+                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                     generate_pkl = True
                 if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(pklfilename)])
+                    records = pd.concat([records,pd.read_pickle(path_yaml+'/'+pklfilename)])
                    # irecord = 0
                 else:
-                    with open(yamlfilename) as yaml_file:
+                    with open(path_yaml+'/'+yamlfilename) as yaml_file:
 
                         dictout = {}
 
@@ -416,7 +420,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             current_tell = next_tell
                             next_record_found = False
                             yaml_file.seek(current_tell)
-                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            filebuffer = open(gettempdir()+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
                             linebuffer = ''
                             while ( (not next_record_found) and (not end_of_file)):
                                 filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
@@ -431,14 +435,14 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
                             
                             #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
                             print(command)
                             
                             os.system(command)
                             #jsonoutput = subprocess.check_output(command,shell=True) 
                             #print(jsonoutput)
                             #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
+                            jsonstream = open(gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell))
                             record = json.load(jsonstream)
                             dictouttemp = {}
                             for key,value in record['pars'].items():
@@ -456,19 +460,19 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             dictouttemp['chunk'] = chunk
                             dictouttemp['index_start'] = index_start
                             dictouttemp['index_end'] = index_end
-                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
+                            os.system('rm '+gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell))
                             for key,value in dictouttemp.items():
                                 if key not in dictout.keys():
                                     dictout[key] = {}
                                 dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
                             print(' obs record registered')
                             jsonstream.close()
-                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                            os.system('rm '+gettempdir()+yamlfilename+'.buffer.yaml.'+str(current_tell))
                     records_station = pd.DataFrame.from_dict(dictout)
                     records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+pklfilename+') for station '\
+                    print('writing table file ('+path_yaml+'/'pklfilename+') for station '\
                           +str(STNID))
-                    records_station.to_pickle(pklfilename)
+                    records_station.to_pickle(path_yaml+'/'pklfilename)
                     # else:
                     #     os.system('rm '+pklfilename)
                     records = pd.concat([records,records_station])
diff --git a/class4gl/simulations/.simulations.py.swp b/class4gl/simulations/.simulations.py.swp
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zY|%AT9*L^v;~U}wE|H=^2YzEK^~@L&ul?j$>S1;%b{drHg*=hcJS4JYx
Date: Wed, 22 Aug 2018 14:37:44 +0200
Subject: [PATCH 016/129] add use of system temporary directory

---
 class4gl/.interface_functions.py.swp          | Bin 40960 -> 40960 bytes
 .../interface_functions.cpython-36.pyc        | Bin 10538 -> 10781 bytes
 class4gl/interface_functions.py               |  25 +++++++++---------
 class4gl/simulations/.simulations.py.swp      | Bin 28672 -> 28672 bytes
 class4gl/simulations/simulations.py           |   2 ++
 5 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/class4gl/.interface_functions.py.swp b/class4gl/.interface_functions.py.swp
index d8a5b3db951310f43ab0d7fddd8bc656eb41f3d5..95717cf2146cfdf5077234636933be7e4d0072d3 100644
GIT binary patch
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diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index ddbf228..42dba38 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -7,7 +7,7 @@
 from contextlib import suppress
 from time import sleep
 
-import tempfile
+from tempfile import gettempdir
 
 
 
@@ -348,9 +348,8 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
             # we try the old single-chunk filename format first (usually for
             # original profile pairs)
-            print('hello',STNID)
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
-            if os.path.isfile(fn):
+            fn = format(STNID,'05d')+'_'+subset+'.yaml'
+            if os.path.isfile(path_yaml+'/'+fn):
                 chunk = 0
                 dictfnchunks.append(dict(fn=fn,chunk=chunk))
 
@@ -359,8 +358,8 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                 chunk = 0
                 end_of_chunks = False
                 while not end_of_chunks:
-                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(fn):
+                    fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                    if os.path.isfile(path_yaml+'/'+fn):
                         dictfnchunks.append(dict(fn=fn,chunk=chunk))
                     else:
                         end_of_chunks = True
@@ -389,7 +388,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                           path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                     generate_pkl = True
                 elif not (os.path.getmtime(path_yaml+'/'+yamlfilename) <  \
-                    os.path.getmtime(pklfilename)):
+                    os.path.getmtime(path_yaml+'/'+pklfilename)):
                     print('pkl file older than yaml file, so I regenerate "'+\
                           path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                     generate_pkl = True
@@ -435,14 +434,14 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
                             
                             #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
                             print(command)
                             
                             os.system(command)
                             #jsonoutput = subprocess.check_output(command,shell=True) 
                             #print(jsonoutput)
                             #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell))
+                            jsonstream = open(gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
                             record = json.load(jsonstream)
                             dictouttemp = {}
                             for key,value in record['pars'].items():
@@ -460,19 +459,19 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             dictouttemp['chunk'] = chunk
                             dictouttemp['index_start'] = index_start
                             dictouttemp['index_end'] = index_end
-                            os.system('rm '+gettempdir()+yamlfilename+'.buffer.json.'+str(current_tell))
+                            os.system('rm '+gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
                             for key,value in dictouttemp.items():
                                 if key not in dictout.keys():
                                     dictout[key] = {}
                                 dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
                             print(' obs record registered')
                             jsonstream.close()
-                            os.system('rm '+gettempdir()+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                            os.system('rm '+gettempdir()+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
                     records_station = pd.DataFrame.from_dict(dictout)
                     records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+path_yaml+'/'pklfilename+') for station '\
+                    print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
                           +str(STNID))
-                    records_station.to_pickle(path_yaml+'/'pklfilename)
+                    records_station.to_pickle(path_yaml+'/'+pklfilename)
                     # else:
                     #     os.system('rm '+pklfilename)
                     records = pd.concat([records,records_station])
diff --git a/class4gl/simulations/.simulations.py.swp b/class4gl/simulations/.simulations.py.swp
index 2979bd721914c462302dcaf5108d3deb10ed2a82..fa508cc9ce2f5a68fdff618e8ecc170da0d80236 100644
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Date: Wed, 22 Aug 2018 16:34:49 +0200
Subject: [PATCH 017/129] test

---
 .../ribtol/__pycache__/__init__.cpython-36.pyc   | Bin 294 -> 0 bytes
 .../ribtol/__pycache__/ribtol_hw.cpython-36.pyc  | Bin 3455 -> 0 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 class4gl/ribtol/__pycache__/__init__.cpython-36.pyc
 delete mode 100644 class4gl/ribtol/__pycache__/ribtol_hw.cpython-36.pyc

diff --git a/class4gl/ribtol/__pycache__/__init__.cpython-36.pyc b/class4gl/ribtol/__pycache__/__init__.cpython-36.pyc
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From afdf5aaa3c5db03008365bff021cc0e9cd3d623a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 16:58:49 +0200
Subject: [PATCH 018/129] make interface work

---
 class4gl/.interface_functions.py.swp     | Bin 40960 -> 0 bytes
 class4gl/evaluation/evaluation.py        | 303 +++++++++++++++++++++++
 class4gl/interface_multi.py              |  82 +++---
 class4gl/simulations/.simulations.py.swp | Bin 28672 -> 0 bytes
 4 files changed, 345 insertions(+), 40 deletions(-)
 delete mode 100644 class4gl/.interface_functions.py.swp
 create mode 100644 class4gl/evaluation/evaluation.py
 delete mode 100644 class4gl/simulations/.simulations.py.swp

diff --git a/class4gl/.interface_functions.py.swp b/class4gl/.interface_functions.py.swp
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nU;NVkCL@1E!~QM5_%<^8rqQ= xmin) & (x < xmax))
+        ybin = y[in_bin]
+        xbin = x[in_bin]
+        if len(ybin) > 20:
+            k = kde.gaussian_kde((ybin))
+            zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+    zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+    zi_int = zi.cumsum(axis=1) 
+                 #  label=key+", "+\
+                 #                    'R = '+str(round(PR[0],3))+', '+\
+                 #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                 #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+    axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+            colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+    axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+            colors=['darkred'],alpha=0.5,)
+
+
+    latex = {}
+    latex['dthetadt'] =  r'$d \theta / dt $'
+    latex['dqdt'] =      r'$d q / dt $'
+    latex['dhdt'] =      r'$d h / dt $'
+
+    axes[varkey].set_xlabel('observations')     
+    axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+
+    PR = pearsonr(mod,obs)[0]
+    RMSE = rmse(obs,mod)                                               
+    BIAS = np.mean(mod) - np.mean(obs)
+    STD = mod.std()
+
+    axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+                                  'R = '+str(round(PR,3))+', '+\
+                                  'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                  'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+                         s=0.1,alpha=0.14,color='k')
+    axes[varkey].legend(fontsize=5)
+                   
+    axes[varkey].set_xlabel('observations')     
+    if i==0:                                    
+        axes[varkey].set_ylabel('model')                                            
+    abline(1,0,axis=axes[varkey])
+    i +=1
+
+
+
+# legend for different forcing simulations (colors)
+ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+leg = []
+for ikey,key in enumerate(EXPS.keys()):
+    leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+    leg.append(leg1)
+ax.axis('off')
+#leg1 =
+ax.legend(leg,list(EXPS.keys()),loc=2,fontsize=10)
+
+
+# # legend for different stations (symbols)
+# ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# leg = []
+# isymbol = 0
+# for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+#     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+#     leg.append(leg1)
+#     isymbol += 1
+# 
+# # symbol for all stations
+# leg1, = ax.plot([],'ko',markersize=10)
+# leg.append(leg1)
+
+
+# ax.axis('off')
+# ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+
+
+fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+
+
+#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+fig.show()  
+
+
+
+
+
+
+
+
+
+
+
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 83148e5..6f70487 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -445,7 +445,7 @@ def update_record(self):
         key = self.frames['worldmap']['inputkey']
         # only redraw the map if the current world map has a time
         # dimension
-        if 'time' in self.globaldata.datasets[key].page[key].dims:
+        if (self.globaldata is not None) and ('time' in self.globaldata.datasets[key].page[key].dims):
             self.goto_datetime_worldmap(
                 self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
                 'after')
@@ -747,20 +747,21 @@ def plot(self):
 
     def goto_datetime_worldmap(self,DT,shift=None):
         DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
-            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
-            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
-            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
-                self.frames['worldmap']['iDT'] += 1
-            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
-                self.frames['worldmap']['iDT'] -= 1 
-            # for gleam, we take the values of the previous day
-            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
-                self.frames['worldmap']['iDT'] -= 2 
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-        #else:
-        #    self.frames['worldmap'].pop('DT')
+        if self.globaldata is not None:
+            if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
+                self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
+                DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
+                self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
+                if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
+                    self.frames['worldmap']['iDT'] += 1
+                elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
+                    self.frames['worldmap']['iDT'] -= 1 
+                # for gleam, we take the values of the previous day
+                if self.frames['worldmap']['inputkey'] in ['wg','w2']:
+                    self.frames['worldmap']['iDT'] -= 2 
+                self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
+            #else:
+            #    self.frames['worldmap'].pop('DT')
 
     def next_datetime(self,event=None):
         if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
@@ -824,28 +825,29 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
         frames = self.frames
         fig = self.fig
  
-        if (only is None) or ('worldmap' in only):
-            globaldata = self.globaldata
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
-            else:
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
-            keystotranspose = ['lat','lon']
-            for key in dict(datasetxr.dims).keys():
-                if key not in keystotranspose:
-                    keystotranspose.append(key)
-
-            datasetxr = datasetxr.transpose(*keystotranspose)
-            datasetxr = datasetxr.sortby('lat',ascending=False)
-
-            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
-            lonleft = lonleft - 360.
-            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
-            label = 'worldmap'
-            axes[label].clear()
-            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
-            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+        if globaldata is not None:
+            if (only is None) or ('worldmap' in only):
+                globaldata = self.globaldata
+                if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                    globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
+                    datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
+                else:
+                    datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+                keystotranspose = ['lat','lon']
+                for key in dict(datasetxr.dims).keys():
+                    if key not in keystotranspose:
+                        keystotranspose.append(key)
+
+                datasetxr = datasetxr.transpose(*keystotranspose)
+                datasetxr = datasetxr.sortby('lat',ascending=False)
+
+                lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
+                lonleft = lonleft - 360.
+                lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
+                label = 'worldmap'
+                axes[label].clear()
+                axes[label].lon = xr.concat([lonleft,lonright],'lon').values
+                axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
 
         if (only is None) or ('worldmap' in only):
             #if 'axmap' not in self.__dict__ :
@@ -887,10 +889,10 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             from matplotlib import cm
             axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
             
-            
             title=frames['worldmap']['inputkey']
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+            if globaldata is not None: 
+                if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                    title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
             axes[label].set_title(title)
 
             label ='worldmap_colorbar'
diff --git a/class4gl/simulations/.simulations.py.swp b/class4gl/simulations/.simulations.py.swp
deleted file mode 100644
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From 8ad4f2484b8683fa0a8200923753a8b74d24d267 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 17:02:31 +0200
Subject: [PATCH 019/129] gitignore

---
 .gitignore | 113 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 113 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..d5a1137
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,113 @@
+*.o, 
+*.pyc
+*.class 
+class4gl/__pycache__/
+class4gl/__pycache__/*
+*.log
+.*
+build/
+dist/
+
+*/__pychache__/
+*.py[cod]
+*$py.class
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+
+ 
+!/.gitignore

From fa082ef05f7430587faf2eb14facdfaec1d95a97 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 17:04:44 +0200
Subject: [PATCH 020/129] cleanups

---
 class4gl/__pycache__/__init__.cpython-36.pyc  |  Bin 376 -> 0 bytes
 class4gl/__pycache__/class4gl.cpython-36.pyc  |  Bin 28782 -> 0 bytes
 .../__pycache__/data_global.cpython-36.pyc    |  Bin 17867 -> 0 bytes
 .../interface_functions.cpython-36.pyc        |  Bin 10781 -> 0 bytes
 .../interface_multi.cpython-36.pyc            |  Bin 31044 -> 0 bytes
 class4gl/__pycache__/model.cpython-36.pyc     |  Bin 36319 -> 0 bytes
 dist/class4gl-0.1dev.tar.gz                   |  Bin 74685 -> 0 bytes
 dist/class4gl-0.1dev/PKG-INFO                 |   14 -
 dist/class4gl-0.1dev/bin/__init__.py          |    7 -
 dist/class4gl-0.1dev/lib/__init__.py          |    7 -
 dist/class4gl-0.1dev/lib/class4gl.py          | 1611 ------------
 dist/class4gl-0.1dev/lib/data_air.py          |  473 ----
 dist/class4gl-0.1dev/lib/data_global.py       |  936 -------
 .../lib/interface_functions.py                |  506 ----
 dist/class4gl-0.1dev/lib/interface_multi.py   | 2061 ---------------
 dist/class4gl-0.1dev/lib/model.py             | 2214 -----------------
 dist/class4gl-0.1dev/setup.py                 |    9 -
 17 files changed, 7838 deletions(-)
 delete mode 100644 class4gl/__pycache__/__init__.cpython-36.pyc
 delete mode 100644 class4gl/__pycache__/class4gl.cpython-36.pyc
 delete mode 100644 class4gl/__pycache__/data_global.cpython-36.pyc
 delete mode 100644 class4gl/__pycache__/interface_functions.cpython-36.pyc
 delete mode 100644 class4gl/__pycache__/interface_multi.cpython-36.pyc
 delete mode 100644 class4gl/__pycache__/model.cpython-36.pyc
 delete mode 100644 dist/class4gl-0.1dev.tar.gz
 delete mode 100644 dist/class4gl-0.1dev/PKG-INFO
 delete mode 100644 dist/class4gl-0.1dev/bin/__init__.py
 delete mode 100644 dist/class4gl-0.1dev/lib/__init__.py
 delete mode 100644 dist/class4gl-0.1dev/lib/class4gl.py
 delete mode 100644 dist/class4gl-0.1dev/lib/data_air.py
 delete mode 100644 dist/class4gl-0.1dev/lib/data_global.py
 delete mode 100644 dist/class4gl-0.1dev/lib/interface_functions.py
 delete mode 100644 dist/class4gl-0.1dev/lib/interface_multi.py
 delete mode 100644 dist/class4gl-0.1dev/lib/model.py
 delete mode 100644 dist/class4gl-0.1dev/setup.py

diff --git a/class4gl/__pycache__/__init__.cpython-36.pyc b/class4gl/__pycache__/__init__.cpython-36.pyc
deleted file mode 100644
index 75cd64362b5997bc8f56bf6abf2c3fb3e5409324..0000000000000000000000000000000000000000
GIT binary patch
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diff --git a/class4gl/__pycache__/class4gl.cpython-36.pyc b/class4gl/__pycache__/class4gl.cpython-36.pyc
deleted file mode 100644
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diff --git a/dist/class4gl-0.1dev/PKG-INFO b/dist/class4gl-0.1dev/PKG-INFO
deleted file mode 100644
index 1cecd19..0000000
--- a/dist/class4gl-0.1dev/PKG-INFO
+++ /dev/null
@@ -1,14 +0,0 @@
-Metadata-Version: 1.0
-Name: class4gl
-Version: 0.1dev
-Summary: UNKNOWN
-Home-page: UNKNOWN
-Author: UNKNOWN
-Author-email: UNKNOWN
-License: GPLv3 licence
-Description: # class4gl
-        Chemistry Land-surface Atmosphere Soil Slab model (CLASS) | Python version
-        
-        This is the extension of class to be able to be used with global balloon soundings. 
-        
-Platform: UNKNOWN
diff --git a/dist/class4gl-0.1dev/bin/__init__.py b/dist/class4gl-0.1dev/bin/__init__.py
deleted file mode 100644
index a21583b..0000000
--- a/dist/class4gl-0.1dev/bin/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from . import model,class4gl,interface_multi,data_air,data_global
-
-__version__ = '0.1.0'
-
-__author__ = 'Hendrik Wouters '
-
-__all__ = []
diff --git a/dist/class4gl-0.1dev/lib/__init__.py b/dist/class4gl-0.1dev/lib/__init__.py
deleted file mode 100644
index a21583b..0000000
--- a/dist/class4gl-0.1dev/lib/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from . import model,class4gl,interface_multi,data_air,data_global
-
-__version__ = '0.1.0'
-
-__author__ = 'Hendrik Wouters '
-
-__all__ = []
diff --git a/dist/class4gl-0.1dev/lib/class4gl.py b/dist/class4gl-0.1dev/lib/class4gl.py
deleted file mode 100644
index 7baaa51..0000000
--- a/dist/class4gl-0.1dev/lib/class4gl.py
+++ /dev/null
@@ -1,1611 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-
-Created on Mon Jan 29 12:33:51 2018
-
-Module file for class4gl, which  extents the class-model to be able to take
-global air profiles as input. It exists of:
-
-CLASSES:
-    - an input object, namely class4gl_input. It includes:
-        - a function to read Wyoming sounding data from a yyoming stream object
-        - a function to read global data from a globaldata library object 
-    - the model object: class4gl
-    - ....    
-
-DEPENDENCIES:
-    - xarray
-    - numpy
-    - data_global
-    - Pysolar
-    - yaml
-
-@author: Hendrik Wouters
-
-"""
-
-
-
-""" Setup of envirnoment """
-
-# Standard modules of the stand class-boundary-layer model
-from model import model
-from model import model_output as class4gl_output
-from model import model_input
-from model import qsat
-#from data_soundings import wyoming 
-import Pysolar
-import yaml
-import logging
-import warnings
-import pytz
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-
-# Generic Python Packages
-import numpy as np
-import datetime as dt
-import pandas as pd
-import xarray as xr
-import io
-#from skewt.thermodynamics import TempK,DewPoint,MixR2VaporPress,GammaW,degCtoK, Rs_da, Cp_da,VaporPressure,MixRatio
-from data_global import data_global
-grav = 9.81
-
-# this is just a generic input object
-class generic_input(object):
-    def __init__(self):
-        self.init = True
-
-
-# all units from all variables in CLASS(4GL) should be defined here!
-units = {
-         'h':'m',
-         'theta':'K', 
-         'q':'kg/kg',
-         'cc': '-',
-         'cveg': '-',
-         'wg': 'm3 m-3',
-         'w2': 'm3 m-3',
-         #'wg': 'kg/kg',
-         'Tsoil': 'K',
-         'T2': 'K',
-         'z0m': 'm',
-         'alpha': '-',
-         'LAI': '-',
-         'dhdt':'m/h',
-         'dthetadt':'K/h',
-         'dqdt':'kg/kg/h',
-         'BR': '-',
-         'EF': '-',
-}
-
-class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
-
-    def __init__(self,set_pars_defaults=True,debug_level=None):
-
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        print('hello')
-        self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
-     
-        # # add ch to logger
-        # self.logger.addHandler(ch)
-
-        # """ end set up logger """
-
-
-
-        # these are the standard model input single-value parameters for class
-        self.pars = model_input()
-
-        # diagnostic parameters of the initial profile
-        self.diag = dict()
-
-        # In this variable, we keep track of the different parameters from where it originates from. 
-        self.sources = {}
-
-        if set_pars_defaults:
-            self.set_pars_defaults()
-
-    def set_pars_defaults(self):
-
-        """ 
-        Create empty model_input and set up case
-        """
-        defaults = dict( 
-        dt         = 60.    , # time step [s] 
-        runtime    = 6*3600 ,  # total run time [s]
-        
-        # mixed-layer input
-        sw_ml      = True   ,  # mixed-layer model switch
-        sw_shearwe = False  ,  # shear growth mixed-layer switch
-        sw_fixft   = False  ,  # Fix the free-troposphere switch
-        h          = 200.   ,  # initial ABL height [m]
-        Ps         = 101300.,  # surface pressure [Pa]
-        divU       = 0.     ,  # horizontal large-scale divergence of wind [s-1]
-        #fc         = 1.e-4  ,  # Coriolis parameter [m s-1]
-        
-        theta      = 288.   ,  # initial mixed-layer potential temperature [K]
-        dtheta     = 1.     ,  # initial temperature jump at h [K]
-        gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
-        advtheta   = 0.     ,  # advection of heat [K s-1]
-        beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
-        wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
-        
-        q          = 0.008  ,  # initial mixed-layer specific humidity [kg kg-1]
-        dq         = -0.001 ,  # initial specific humidity jump at h [kg kg-1]
-        gammaq     = 0.     ,  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        advq       = 0.     ,  # advection of moisture [kg kg-1 s-1]
-        wq         = 0.1e-3 ,  # surface kinematic moisture flux [kg kg-1 m s-1]
-        
-        CO2        = 422.   ,  # initial mixed-layer CO2 [ppm]
-        dCO2       = -44.   ,  # initial CO2 jump at h [ppm]
-        gammaCO2   = 0.     ,  # free atmosphere CO2 lapse rate [ppm m-1]
-        advCO2     = 0.     ,  # advection of CO2 [ppm s-1]
-        wCO2       = 0.     ,  # surface kinematic CO2 flux [ppm m s-1]
-        sw_wind    = True  ,  # prognostic wind switch
-        u          = 0.     ,  # initial mixed-layer u-wind speed [m s-1]
-        du         = 0.     ,  # initial u-wind jump at h [m s-1]
-        gammau     = 0.     ,  # free atmosphere u-wind speed lapse rate [s-1]
-        advu       = 0.     ,  # advection of u-wind [m s-2]
-        v          = 0.0    , # initial mixed-layer u-wind speed [m s-1]
-        dv         = 0.0    ,  # initial u-wind jump at h [m s-1]
-        gammav     = 0.     ,  # free atmosphere v-wind speed lapse rate [s-1]
-        advv       = 0.     ,  # advection of v-wind [m s-2]
-        sw_sl      = True   , # surface layer switch
-        ustar      = 0.3    ,  # surface friction velocity [m s-1]
-        z0m        = 0.02   ,  # roughness length for momentum [m]
-        z0h        = 0.02* 0.1 ,  # roughness length for scalars [m]
-        sw_rad     = True   , # radiation switch
-        lat        = 51.97  ,  # latitude [deg]
-        lon        = -4.93  ,  # longitude [deg]
-        doy        = 268.   ,  # day of the year [-]
-        tstart     = 6.8    ,  # time of the day [h UTC]
-        cc         = 0.0    ,  # cloud cover fraction [-]
-        Q          = 400.   ,  # net radiation [W m-2] 
-        dFz        = 0.     ,  # cloud top radiative divergence [W m-2] 
-        ls_type    = 'js'   ,  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        wg         = 0.21   ,  # volumetric water content top soil layer [m3 m-3]
-        w2         = 0.21   ,  # volumetric water content deeper soil layer [m3 m-3]
-        cveg       = 0.85   ,  # vegetation fraction [-]
-        Tsoil      = 295.   ,  # temperature top soil layer [K]
-        Ts         = 295.   ,    # initial surface temperature [K]
-        T2         = 296.   ,  # temperature deeper soil layer [K]
-        a          = 0.219  ,  # Clapp and Hornberger retention curve parameter a
-        b          = 4.90   ,  # Clapp and Hornberger retention curve parameter b
-        p          = 4.     ,  # Clapp and Hornberger retention curve parameter c
-        CGsat      = 3.56e-6,  # saturated soil conductivity for heat
-        wsat       = 0.472  ,  # saturated volumetric water content ECMWF config [-]
-        wfc        = 0.323  ,  # volumetric water content field capacity [-]
-        wwilt      = 0.171  ,  # volumetric water content wilting point [-]
-        C1sat      = 0.132  ,  
-        C2ref      = 1.8    ,
-        LAI        = 2.     ,  # leaf area index [-]
-        gD         = 0.0    ,  # correction factor transpiration for VPD [-]
-        rsmin      = 110.   ,  # minimum resistance transpiration [s m-1]
-        rssoilmin  = 50.    ,  # minimun resistance soil evaporation [s m-1]
-        alpha      = 0.25   ,  # surface albedo [-]
-        Wmax       = 0.0012 ,  # thickness of water layer on wet vegetation [m]
-        Wl         = 0.0000 ,  # equivalent water layer depth for wet vegetation [m]
-        Lambda     = 5.9    ,  # thermal diffusivity skin layer [-]
-        c3c4       = 'c3'   ,  # Plant type ('c3' or 'c4')
-        sw_cu      = False  ,  # Cumulus parameterization switch
-        dz_h       = 150.   ,  # Transition layer thickness [m]
-        cala       = None   ,  # soil heat conductivity [W/(K*m)]
-        crhoc      = None   ,  # soil heat capacity  [J/K*m**3]
-        sw_ls      = True   ,
-        sw_ap      = True  ,   # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-        sw_ac      = None  ,   # switch that tells to use large-scale gridded Air Circulation (advection and subsindence) fields  as input from eg., ERA-INTERIM
-        sw_lit     = False,
-        )
-        pars = model_input()
-        for key in defaults:
-            pars.__dict__[key] = defaults[key]
-        
-        self.update(source='defaults',pars=pars)
-        
-    def clear(self):
-        """ this procudure clears the class4gl_input """
-
-        for key in list(self.__dict__.keys()):
-            del(self.__dict__[key])
-        self.__init__()
-
-    def dump(self,file):
-        """ this procedure dumps the class4gl_input object into a yaml file
-            
-            Input: 
-                - self.__dict__ (internal): the dictionary from which we read 
-            Output:
-                - file: All the parameters in self.__init__() are written to
-                the yaml file, including pars, air_ap, sources etc.
-        """
-        file.write('---\n')
-        index = file.tell()
-        file.write('# CLASS4GL input; format version: 0.1\n')
-
-        # write out the position of the current record
-        yaml.dump({'index':index}, file, default_flow_style=False)
-
-        # we do not include the none values
-        for key,data in self.__dict__.items():
-            #if ((type(data) == model_input) or (type(class4gl_input):
-            if key == 'pars':
-
-                pars = {'pars' : self.__dict__['pars'].__dict__}
-                parsout = {}
-                for key in pars.keys():
-                    if pars[key] is not None:
-                        parsout[key] = pars[key]
-
-                yaml.dump(parsout, file, default_flow_style=False)
-            elif type(data) == dict:
-                if key == 'sources':
-                    # in case of sources, we want to have a
-                    # condensed list format as well, so we leave out
-                    # 'default_flow_style=False'
-                    yaml.dump({key : data}, file)
-                else: 
-                    yaml.dump({key : data}, file,
-                              default_flow_style=False)
-            elif type(data) == pd.DataFrame:
-                # in case of dataframes (for profiles), we want to have a
-                # condensed list format as well, so we leave out
-                # 'default_flow_style=False'
-                yaml.dump({key: data.to_dict(orient='list')},file)
-
-                # # these are trials to get it into a more human-readable
-                # fixed-width format, but it is too complex
-                #stream = yaml.dump({key : False},width=100, default_flow_style=False)
-                #file.write(stream)
-                
-                # workaround. I don't know how to put a table in a readable format by using yaml. So I do it manually here
-                #file.write(key+': !!str |\n')
-                #file.write(str(data)+'\n')
-       
-    def load_yaml_dict(self,yaml_dict,reset=True):
-        """ this procedure loads class4gl_input data from a dictionary obtained from yaml
-            
-            Input: 
-                - yaml_dict: the dictionary from which we read 
-                - reset: reset data before reading        
-            Output:
-                - All the parameters in self, eg., (pars, air_ap, sources etc.,).
-        """
-        
-        if reset:
-            for key in list(self.__dict__.keys()):
-                del(self.__dict__[key])
-            self.__init__()
-
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                self.__dict__[key] = model_input()
-                self.__dict__[key].__dict__ = data
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            elif key == 'sources':
-                self.__dict__[key] = data
-            elif key == 'diag':
-                self.__dict__[key] = data
-            else: 
-                warnings.warn("Key '"+key+"' may not be implemented.")
-                self.__dict__[key] = data
-
-    def update(self,source,**kwargs):
-        """ this procedure is to make updates of input parameters and tracking
-        of their source more convenient. It implements the assignment of
-        parameter source/sensitivity experiment IDs ('eg.,
-        'defaults', 'sounding balloon', any satellite information, climate
-        models, sensitivity tests etc.). These are all stored in a convenient
-        way with as class4gl_input.sources.  This way, the user can always consult with
-        from where parameters data originates from.  
-        
-        Input:
-            - source:    name of the underlying dataset
-            - **kwargs: a dictionary of data input, for which the key values
-            refer to the class4gl data type ('pars', 'air_ap', 'air_balloon', etc.) and
-            the values is a again a dictionary/dataframe of datakeys/columns
-            ('wg','PRES','datetime', ...) and datavalues (either single values,
-            profiles ...), eg., 
-
-                pars = {'wg': 0.007  , 'w2', 0.005}
-                pars = {pd.Dataframe('PRES': [1005.,9523,...]  , 'THTA': [295.,
-                                     300.,...]}
-            
-        Output:
-            - self.__dict__[datatype] : object to which the parameters are
-                                        assigned. They can be consulted with
-                                        self.pars, self.profiles, etc.
-                                        
-            - self.sources[source] : It supplements the overview overview of
-                                     data sources can be consulted with
-                                     self.sources. The structure is as follows:
-                                     as:
-                self.sources = { 
-                'wyoming': ['pars:datetime','air_balloon:PRES','air_ap:QABS', ...],
-                'GLEAM' :  ['pars:wg','pars:w2', ...],
-                 ...
-                }
-        
-        """
-
-        #print(source,kwargs)
-
-        for key,data in kwargs.items():
-
-            #print(key)
-            # if the key is not in class4gl_input object, then just add it. In
-            # that case, the update procedures below will just overwrite it 
-            if key not in self.__dict__:
-                self.__dict__[key] = data
-
-
-            
-
-            #... we do an additional check to see whether there is a type
-            # match. I not then raise a key error
-            if (type(data) != type(self.__dict__[key]) \
-                # we allow dict input for model_input pars
-                and not ((key == 'pars') and (type(data) == dict) and \
-                (type(self.__dict__[key]) == model_input))):
-
-                raise TypeError('input key '+key+' is not of the same type as the one in the class4gl_object')
-
-
-            # This variable keeps track of the added data that is supplemented
-            # by the current source. We add this to class4gl_input.sources
-            datakeys = []
-
-            #... and we update the class4gl_input data, and this depends on the
-            # data type
-
-            if type(self.__dict__[key]) == pd.DataFrame:
-                # If the data type is a dataframe, then we update the columns
-                for column in list(data.columns):
-                    #print(column)
-                    self.__dict__[key][column] = data[column]
-                    datakeys.append(column)
-                    
-
-            elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
-                # dictionary of parameters
-                if type(data) == model_input:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data.__dict__}
-                    datakeys = list(data.__dict__.keys())
-                elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
-                    datakeys = list(data.keys())
-                else:
-                    raise TypeError('input key '+key+' is not of the same type\
-                                    as the one in the class4gl_object')
-
-            elif type(self.__dict__[key]) == dict:
-                # if the data type is a dictionary, we update the
-                # dictionary 
-                self.__dict__[key] = {self.__dict__[key] , data}
-                datakeys = list(data.keys())
-
-
-            # if source entry is not existing yet, we add it
-            if source not in self.sources.keys():
-                self.sources[source] = []
-
-
-            # self.logger.debug('updating section "'+\
-            #                  key+' ('+' '.join(datakeys)+')'\
-            #                  '" from source \
-            #                  "'+source+'"')
-
-            # Update the source dictionary: add the provided data keys to the
-            # specified source list
-            for datakey in datakeys:
-                # At first, remove the occurences of the keys in the other
-                # source lists
-                for sourcekey,sourcelist in self.sources.items():
-                    if key+':'+datakey in sourcelist:
-                        self.sources[sourcekey].remove(key+':'+datakey)
-                # Afterwards, add it to the current source list
-                self.sources[source].append(key+':'+datakey)
-
-
-        # # in case the datatype is a class4gl_input_pars, we update its keys
-        # # according to **kwargs dictionary
-        # if type(self.__dict__[datatype]) == class4gl_input_pars:
-        #     # add the data parameters to the datatype object dictionary of the
-        #     # datatype
-        #     self.__dict__[datatype].__dict__ = {**self.__dict__[datatype].__dict__ ,
-        #                                        **kwargs}
-        # # in case, the datatype reflects a dataframe, we update the columns according
-        # # to the *args list
-        # elif type(self.__dict__[datatype]) == pd.DataFrame:
-        #     for dataframe in args:
-        #         for column in list(dataframe.columns):
-        #             self.__dict__[datatype][column] = dataframe[column]
-        
-
-    def get_profile(self,IOBJ, *args, **argv):
-        # if type(IOBJ) == wyoming:
-        self.get_profile_wyoming(IOBJ,*args,**argv)
-        # else:
-        #     raise TypeError('Type '+str(type(IOBJ))+' is not supported')
-        
-    def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
-        """ 
-            Purpose: 
-                This procedure assigns wyoming air profiles and parameters to the class4gl_input object.
-
-            Input:
-                1. wy_strm   = wyoming html (beautifulsoup) stream object. The
-                function will take the profile at the stream's current
-                position. 
-                2. air_ap_mode: which air profile do we take? 
-                    - b : best
-                    - l : according to lower limit for the mixed-layer height
-                            estimate
-                    - u : according to upper limit for the mixed-layer height
-                            estimate
-
-
-            Output:
-                1. all single-value parameters are stored in the
-                   class4gl_input.pars object
-                2. the souding profiles are stored in the in the
-                   class4gl_input.air_balloon dataframe
-                3. modified sounding profiles for which the mixed layer height
-                   is fitted
-                4. ...
-
-        """
-
-
-        # Raise an error in case the input stream is not the correct object
-        # if type(wy_strm) is not wyoming:
-        #    raise TypeError('Not a wyoming type input stream')
-
-        # Let's tell the class_input object that it is a Wyoming fit type
-        self.air_ap_type = 'wyoming'
-        # ... and which mode of fitting we apply
-        self.air_ap_mode = air_ap_mode
-
-        """ Temporary variables used for output """
-        # single value parameters derived from the sounding profile
-        dpars = dict()
-        # profile values
-        air_balloon = pd.DataFrame()
-        # fitted profile values
-        air_ap = pd.DataFrame()
-        
-        string = wy_strm.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = wy_strm.current.find_next('pre').find_next('pre').text
-        
-        # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
-        dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
-               }
-        
-        # we get weird output when it's a numpy Timestamp, so we convert it to
-        # pd.datetime type
-
-        dpars['datetime'] = pytz.utc.localize(dt.datetime.strptime(dpars['Observation time'], "%y%m%d/%H%M"))
-        dpars['STNID'] = dpars['Station number']
-
-        # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
-        # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
-                              / \
-                             (air_balloon.MIXR/1000.+1.)
-        # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-        
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
-
-        
-
-        cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-
-        air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
-
-
-        # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
-        #is_valid = (air_balloon.z >= 0)
-        # # this is an alternative pipe/numpy method
-        # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
-
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
-
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
-
-        if len(valid_indices) > 0:
-            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
-            
-            dpars['h_b'] = np.max((dpars['h'],10.))
-            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
-            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
-            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
-            
-            # the final mixed-layer height that will be used by class. We round it
-            # to 1 decimal so that we get a clean yaml output format
-            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
-        else:
-            dpars['h_u'] =np.nan
-            dpars['h_l'] =np.nan
-            dpars['h_e'] =np.nan
-            dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
-            dpars['Ps'] = np.nan
-
-
-
-
-        if ~np.isnan(dpars['h']):
-            # determine mixed-layer properties (moisture, potential temperature...) from profile
-            
-            # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
-            else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
-                       
-            dpars['theta']= ml_mean.theta
-            dpars['q']    = ml_mean.q
-            dpars['u']    = ml_mean.u 
-            dpars['v']    = ml_mean.v 
-        else:
-            dpars['theta'] = np.nan
-            dpars['q'] = np.nan
-            dpars['u'] = np.nan
-            dpars['v'] = np.nan
-            
-
-
-
-        # First 3 data points of the mixed-layer fit. We create a empty head
-        # first
-        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
-        # All other  data points above the mixed-layer fit
-        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
-        
-        #calculate mixed-layer jump ( this should be larger than 0.1)
-        
-        air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
-        
-        # make a row object for defining the jump
-        jump = air_ap_head.iloc[0] * np.nan
-            
-        if air_ap_tail.shape[0] > 1:
-
-            # we originally used THTA, but that has another definition than the
-            # variable theta that we need which should be the temperature that
-            # one would have if brought to surface (NOT reference) pressure.
-            for column in ['theta','q','u','v']:
-               
-               # initialize the profile head with the mixed-layer values
-               air_ap_head[column] = ml_mean[column]
-               # calculate jump values at mixed-layer height, which will be
-               # added to the third datapoint of the profile head
-               jump[column] = (air_ap_tail[column].iloc[1]\
-                               -\
-                               air_ap_tail[column].iloc[0])\
-                              /\
-                              (air_ap_tail.z.iloc[1]\
-                               - air_ap_tail.z.iloc[0])\
-                              *\
-                              (dpars['h']- air_ap_tail.z.iloc[0])\
-                              +\
-                              air_ap_tail[column].iloc[0]\
-                              -\
-                              ml_mean[column] 
-               if column == 'theta':
-                  # for potential temperature, we need to set a lower limit to
-                  # avoid the model to crash
-                  jump.theta = np.max((0.1,jump.theta))
-        
-               air_ap_head[column][2] += jump[column]
-        
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
-
-
-        # make theta increase strong enough to avoid numerical
-        # instability
-        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = dpars['theta']
-        z_low =     dpars['h']
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                (z_mean > (z_low+10.)) and \
-                (theta_mean > (theta_low+0.2) ) and \
-                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-
-
-
-
-
-        air_ap = \
-            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
-        
-        # we copy the pressure at ground level from balloon sounding. The
-        # pressure at mixed-layer height will be determined internally by class
-        #print(air_ap['PRES'].iloc[0])
-
-        rho        = 1.2                   # density of air [kg m-3]
-        g          = 9.81                  # gravity acceleration [m s-2]
-
-        air_ap['p'].iloc[0] =dpars['Ps'] 
-        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
-        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
-
-        
-        dpars['lat'] = dpars['Station latitude']
-        dpars['latitude'] = dpars['lat']
-        
-        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
-        dpars['lon'] = 0.
-        # this is the real longitude that will be used to extract ground data
-        dpars['longitude'] = dpars['Station longitude']
-        
-        dpars['ldatetime'] = dpars['datetime'] \
-                            + \
-                            dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-        dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
-                                    dpars['latitude'],\
-                                    dpars['longitude'],\
-                                    dpars['datetime']\
-                                )
-        dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
-                                         0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
-        # This is the nearest datetime when the sun is up (for class)
-        dpars['ldatetime_daylight'] = \
-                                np.min(\
-                                    (np.max(\
-                                        (dpars['ldatetime'],\
-                                         dpars['lSunrise'])\
-                                     ),\
-                                     dpars['lSunset']\
-                                    )\
-                                )
-        # apply the same time shift for UTC datetime
-        dpars['datetime_daylight'] = dpars['datetime'] \
-                                    +\
-                                    (dpars['ldatetime_daylight']\
-                                     -\
-                                     dpars['ldatetime'])
-        
-        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
-
-        # We set the starting time to the local sun time, since the model 
-        # thinks we are always at the meridian (lon=0). This way the solar
-        # radiation is calculated correctly.
-        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
-                         + \
-                         dpars['ldatetime_daylight'].minute/60.\
-                         + \
-                         dpars['ldatetime_daylight'].second/3600.
-        
-
-        # convert numpy types to native python data types. This provides
-        # cleaner data IO with yaml:
-        for key,value in dpars.items():
-            if type(value).__module__ == 'numpy':
-                dpars[key] = dpars[key].item()
-
-        # # we make a pars object that is similar to the destination object
-        # pars = model_input()
-        # for key,value in dpars.items():
-        #     pars.__dict__[key] = value
-
-
-        # we round the columns to a specified decimal, so that we get a clean
-        # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
-# 
-        for column,decimal in decimals.items():
-            air_balloon[column] = air_balloon[column].round(decimal)
-            air_ap[column] = air_ap[column].round(decimal)
-
-        self.update(source='wyoming',\
-                    # pars=pars,
-                    pars=dpars,\
-                    air_balloon=air_balloon,\
-                    air_ap=air_ap)
-
-        
-    def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
-    
-        """
-        Purpose: This sets copies the parameters from the global datasets into the self (or similar object) 
-                 according to the position (lat lon) and the class datetime and timespan
-                 globaldata should be a globaldata multifile object
-        
-        Input: 
-            - globaldata: this is the library object
-            - only_keys: only extract specified keys
-            - exclude_keys: do not inherit specified keys
-        """
-        classdatetime      = np.datetime64(self.pars.datetime_daylight)
-        classdatetime_stop = np.datetime64(self.pars.datetime_daylight \
-                                           + \
-                                           dt.timedelta(seconds=self.pars.runtime)\
-                                          )
-
-
-        # # list of variables that we get from global ground data
-        # self.ground_keys = ['fW', 'fB', 'fH', 'fTC', 'alpha', 'z0m', 'z0h', 
-        #                 'wsat', 'Tsoil', 'cc', 'T2', 'wg', 'w2', 'wfc', 
-        #                 'wwilt', 'DSMW', 'tex_coarse_values', 'tex_medium_values', 'tex_fine_values', 'code_values', 
-        #                 'texture', 'itex', 'isoil', 'BR',
-        #                 'b', 'cveg',
-        #                 'C1sat', 
-        #                 'C2ref', 'p', 'a',
-        #                 ] #globaldata.datasets.keys():
-
-        # # these are the required class4gl 3d atmospheric input which is not provided by the soundings
-        # self.atm_keys = ['advtheta_x','advtheta_y','advu_x','advu_y','advv_x','advv_y','advq_x','advq_y','w','p']
-
-
-        if type(globaldata) is not data_global:
-            raise TypeError("Wrong type of input library") 
-
-        # by default, we get all dataset keys
-        keys = list(globaldata.datasets.keys())
-
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
-
-        # # In case there is surface pressure, we also calculate the half-level
-        # # and full-level pressure fields
-        # if ('sp' in keys):
-        #     keys.append('pfull')
-        #     keys.append('phalf')
-
-        # If specified, we only take the keys that are in only_keys
-        if only_keys is not None:
-            for key in keys:
-                if key not in only_keys:
-                    keys.remove(key)
-                
-        # If specified, we take out keys that are in exclude keys
-        if exclude_keys is not None:
-            for key in keys:
-                if key in exclude_keys:
-                    keys.remove(key)
-
-        # we set everything to nan first in the pars section (non-profile parameters
-        # without lev argument), so that we can check afterwards whether the
-        # data is well-fetched or not.
-        for key in keys:
-            if not ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None) and \
-                ('lev' in globaldata.datasets[key].page[key].dims)):
-                self.update(source='globaldata',pars={key:np.nan})
-            # # we do not check profile input for now. We assume it is
-            # # available
-            #else:
-            #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
-
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
-
-        for key in keys:
-            # If we find it, then we obtain the variables
-            if ((key in globaldata.datasets) and \
-                (globaldata.datasets[key].page is not None)):
-
-                # check first whether the dataset has a height coordinate (3d space)
-                if 'lev' in globaldata.datasets[key].page[key].dims:
-
-                    # first, we browse to the correct file that has the current time
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-                        globaldata.datasets[key].browse_page(time=classdatetime)
-
-                    
-                    if (globaldata.datasets[key].page is not None):
-                        # find longitude and latitude coordinates
-                        ilats = (np.abs(globaldata.datasets[key].page.lat -
-                                        self.pars.latitude) < 0.5)
-                        ilons = (np.abs(globaldata.datasets[key].page.lon -
-                                        self.pars.longitude) < 0.5)
-                        
-                        # if we have a time dimension, then we look up the required timesteps during the class simulation
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
-
-                            # In case we didn't find any correct time, we take the
-                            # closest one.
-                            if np.sum(itimes) == 0.:
-
-
-                                classdatetimemean = \
-                                    np.datetime64(self.pars.datetime_daylight + \
-                                    dt.timedelta(seconds=int(self.pars.runtime/2.)
-                                                ))
-
-                                dstimes = globaldata.datasets[key].page.time
-                                time = dstimes.sel(time=classdatetimemean,method='nearest')
-                                itimes = (globaldata.datasets[key].page.time ==
-                                          time)
-                                
-                        else:
-                            # we don't have a time coordinate so it doesn't matter
-                            # what itimes is
-                            itimes = 0
-
-                        #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
-
-                        # over which dimensions we take a mean:
-                        dims = globaldata.datasets[key].page[key].dims
-                        namesmean = list(dims)
-                        namesmean.remove('lev')
-                        idxmean = [dims.index(namemean) for namemean in namesmean]
-                        
-                        value = \
-                        globaldata.datasets[key].page[key].isel(time=itimes,
-                                                                lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
-
-                        # Ideally, source should be equal to the datakey of globaldata.library 
-                        # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
-                        #  but therefore the globaldata class requires a revision to make this work
-                        self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
-
-                else:
-                    # this procedure is for reading the ground fields (2d space). 
-                    # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
-                    if 'time' in list(globaldata.datasets[key].page[key].dims):
-    
-                       # first, we browse to the correct file
-                       #print(key)
-                       globaldata.datasets[key].browse_page(time=classdatetime)
-    
-                    if globaldata.datasets[key].page is not None:
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - self.pars.latitude))
-                        ilat = np.where((DIST) == np.min(DIST))[0][0]
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - self.pars.longitude))
-                        ilon = np.where((DIST) == np.min(DIST))[0][0]
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lat'].values\
-                                - (self.pars.latitude + 0.5)))
-                        ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmax = ilat
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.variables['lon'].values\
-                                - (self.pars.longitude  + 0.5)))
-                        ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmax = ilon
-                        
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lat.values\
-                                - (self.pars.latitude - 0.5)))
-                        ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                            ilatmin = ilat
-                        DIST = \
-                        np.abs((globaldata.datasets[key].page.lon.values\
-                                - (self.pars.longitude  - 0.5)))
-                        ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                        if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                            ilonmin = ilon        
-                        
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
-                        else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
-                            
-                        if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                            
-                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
-                            
-                            classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
-                                                             dt.timedelta(seconds=self.pars.runtime)\
-                                                            ) 
-                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
-                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
-                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetimeend)):
-                                idatetimeend -= 1
-                            idatetime = np.min((idatetime,idatetimeend))
-                            #for gleam, we take the previous day values
-                            if key in ['wg', 'w2']:
-                                idatetime = idatetime - 1
-                                idatetimeend = idatetimeend - 1
-
-                            # in case of soil temperature, we take the exact
-                            # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
-                                idatetimeend = idatetime
-                            
-                            idts = range(idatetime,idatetimeend+1)
-                            
-                            count = 0
-                            self.__dict__[key] = 0.
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    for iidts in idts:
-                                        value += np.mean(globaldata.datasets[key].page[key].isel(time=iidts,lat=iilat,lon=iilon,drop=True).values)
-                                        count += 1
-                            value = value/count
-                            self.update(source='globaldata',pars={key:value.item()})
-                                
-                        else:
-                                
-                            count = 0
-                            value = 0.
-                            for iilat in ilatrange:
-                                for iilon in ilonrange:
-                                    value += np.mean(globaldata.datasets[key].page[key].isel(lat=iilat,lon=iilon,drop=True).values)
-                                    count += 1
-                            value = value/count                        
-
-                            self.update(source='globaldata',pars={key:value.item()})
-
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            self.logger.debug('also update LAI based on LAIpixel and cveg') 
-            # I suppose LAI pixel is already determined in the previous
-            # procedure. Anyway...
-            key = 'LAIpixel'
-
-            if globaldata.datasets[key].page is not None:
-                # first, we browse to the correct file that has the current time
-                if 'time' in list(globaldata.datasets[key].page[key].dims):
-                    globaldata.datasets[key].browse_page(time=classdatetime)
-            
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - self.pars.latitude))
-                ilat = np.where((DIST) == np.min(DIST))[0][0]
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - self.pars.longitude))
-                ilon = np.where((DIST) == np.min(DIST))[0][0]
-                 
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude + 0.5)))
-                ilatmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmax] < globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmax = ilat
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values \
-                        - (self.pars.longitude  + 0.5)))
-                ilonmax = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmax] < globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmax = ilon
-                
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lat.values\
-                        - (self.pars.latitude - 0.5)))
-                ilatmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lat'].values[ilatmin] > globaldata.datasets[key].page.variables['lat'].values[ilat]:
-                    ilatmin = ilat
-                DIST = \
-                np.abs((globaldata.datasets[key].page.lon.values\
-                        - (self.pars.longitude  - 0.5)))
-                ilonmin = np.where((DIST) == np.min(DIST))[0][0]
-                if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
-                    ilonmin = ilon        
-                DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
-                idatetime = np.where((DIST) == np.min(DIST))[0][0]
-                
-                
-                if ilatmin < ilatmax:
-                    ilatrange = range(ilatmin,ilatmax+1)
-                else:
-                    ilatrange = range(ilatmax,ilatmin+1)
-                    
-                if ilonmin < ilonmax:
-                    ilonrange = range(ilonmin,ilonmax+1)
-                else:
-                    ilonrange = range(ilonmax,ilonmin+1)           
-                
-                #tarray_res = np.zeros(shape=globaldata.datasets[key]['time'].shape)
-                LAIpixel = 0.
-                count = 0
-                for iilat in [ilat]: #ilatrange
-                    for iilon in [ilon]: #ilonrange
-                        LAIpixel += globaldata.datasets[key].page[key].isel(time = idatetime,lat=iilat,lon=iilon,drop=True).values
-                        
-                                        
-                        # if np.isnan(tarray[idatetime]):
-                        #     print("interpolating GIMMS LAIpixel nan value")
-                        #     
-                        #     mask = np.isnan(tarray)
-                        #     
-                        #     #replace each nan value with a interpolated value
-                        #     if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                        #         tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-                        #         
-                        #     else:
-                        #         print("Warning. Could not interpolate GIMMS LAIpixel nan value")
-                    
-                        #         tarray *= np.nan 
-                        
-                        count += 1
-                        #tarray_res += tarray
-                LAIpixel = LAIpixel/count
-                
-                count = 0
-                #tarray = globaldata.keys[dataset][key].isel({'lat':[ilat],'lon':[ilon]}).mean(dim=['lat','lon']).values
-  
-                self.update(source='globaldata',pars={'LAIpixel':np.float(LAIpixel)}) 
-                #print('LAIpixel:',self.__dict__['LAIpixel'])
-                #print('cveg:',self.__dict__['cveg'])
-                
-                # finally, we rescale the LAI according to the vegetation
-                # fraction
-                value = 0. 
-                if ((self.pars.cveg is not None) and (self.pars.cveg > 0.1)):
-                   value =self.pars.LAIpixel/self.pars.cveg
-                else:
-                    # in case of small vegetation fraction, we take just a standard 
-                    # LAI value. It doesn't have a big influence anyway for
-                    # small vegetation
-                    value = 2.
-                #print('LAI:',self.__dict__['LAI'])
-                self.update(source='globaldata',pars={'LAI':value}) 
-
-
-        # in case we have 'sp', we also calculate the 3d pressure fields at
-        # full level and half level
-        if ('sp' in keys) and ('sp' in self.pars.__dict__):
-            pdAB = pd.read_fwf('/user/data/gent/gvo000/gvo00090/EXT/scripts/ECMWF/ecmwf_coeffs_L60_wrf.txt',header=None,names=['A','B'],index_col=0)  
-
-            phalf,pfull =calc_air_ac_pres_L60(self.pars.sp,pdAB.A.values,pdAB.B.values)
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # hydrostatic thickness of each model layer
-            delpdgrav = -(phalf[:-1] - phalf[1:])/grav
-            # # dz = rhodz/(R * T / pfull)
-
-
-            # # subsidence multiplied by density. We calculate the subsidence of
-            # # the in class itself
-            # wrho = np.zeros_like(phalf)
-            # wrho[-1] = 0. 
-
-            # for ihlev in range(0,wrho.shape[0]-1):
-            #     # subsidence multiplied by density is the integral of
-            #     # divergences multiplied by the layer thicknessies
-            #     wrho[ihlev] = ((self.air_ac['divU_x'][ihlev:] + \
-            #                     self.air_ac['divU_y'][ihlev:]) * \
-            #                    delpdgrav[ihlev:]).sum()
-
-
-            
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'p':list(pfull)}))
-            self.update(source='globaldata',\
-                        air_ach=pd.DataFrame({'p':list(phalf)}))
-            self.update(source='globaldata',\
-                        air_ac=pd.DataFrame({'delpdgrav':list(delpdgrav)}))
-            # self.update(source='globaldata',\
-            #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
-
-    def check_source(self,source,check_only_sections=None):
-        """ this procedure checks whether data of a specified source is valid.
-
-        INPUT:
-            source: the data source we want to check
-            check_only_sections: a string or list with sections to be checked
-        OUTPUT:
-            returns True or False
-        """
-
-        # we set source ok to false as soon as we find a invalid input
-        source_ok = True
-
-        # convert to a single-item list in case of a string
-        check_only_sections_def = (([check_only_sections]) if \
-                                   type(check_only_sections) is str else \
-                                    check_only_sections)
-                                  
-        if source not in self.sources.keys():
-            self.logger.info('Source '+source+' does not exist')
-            source_ok = False
-
-        for sectiondatakey in self.sources[source]:                             
-            section,datakey = sectiondatakey.split(':')                         
-            if ((check_only_sections_def is None) or \
-                (section in check_only_sections_def)):                          
-                checkdatakeys = []
-                if type(self.__dict__[section]) is pd.DataFrame:
-                    checkdata = self.__dict__[section]
-                elif type(self.__dict__[section]) is model_input:
-                    checkdata = self.__dict__[section].__dict__
-
-                if (datakey not in checkdata):                              
-                    # self.logger.info('Expected key '+datakey+\
-                    #                  ' is not in parameter input')                        
-                    source_ok = False                                           
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
-        
-                    # self.logger.info('Key value of "'+datakey+\
-                    #                  '" is invalid: ('+ \
-                    # str(self.__dict__[section].__dict__[datakey])+')')         
-                    source_ok = False
-
-        return source_ok
-
-    def check_source_globaldata(self):
-        """ this procedure checks whether all global parameter data is
-        available, according to the keys in the self.sources"""
-
-        source_globaldata_ok = True
-
-        #self.get_values_air_input()
-
-        # and now we can get the surface values
-        #class_settings = class4gl_input()
-        #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
-        if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
-            source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
-        else:
-            # we only check the ground parameter data (pars section). The 
-            # profile data (air_ap section) are supposed to be valid in any 
-            # case.
-            source_ok = self.check_source(source='globaldata',\
-                                          check_only_sections=['air_ac',\
-                                                               'air_ap',\
-                                                               'pars'])
-            if not source_ok:
-                source_globaldata_ok = False
-        
-            # Additional check: we exclude desert-like
-            if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
-                source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
-            if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
-                source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
-            elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
-                source_globaldata_ok = False
-
-        return source_globaldata_ok
-
-
-class c4gli_iterator():
-    """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
-    
-        for information/documentation on creating such iterator classes, see: https://stackoverflow.com/questions/19151/build-a-basic-python-iterator
-    """
-    def __init__(self,file):
-        # take file as IO stream
-        self.file = file
-        self.yaml_generator = yaml.load_all(file)
-        self.current_dict = {}
-        self.current_class4gl_input = class4gl_input()
-        separator = self.file.readline() # this is just dummy
-        self.header = file.readline()
-        if self.header != '# CLASS4GL record; format version: 0.1\n':
-            raise NotImplementedError("Wrong format version: '"+self.header+"'")
-    def __iter__(self):
-        return self
-    def __next__(self):
-        self.current_dict = self.yaml_generator.__next__()
-        self.current_class4gl_input.load_yaml_dict(self.current_dict)
-        return self.current_class4gl_input
-
-
-
-#get_cape and lift_parcel are adapted from the SkewT package
-    
-class gl_dia(object):
-    def get_lifted_index(self,timestep=-1):
-        self.LI = get_lifted_index(self.input.Ps,self.out.T2m[timestep],self.out.q[timestep],self.p_pro,self.theta_pro,endp=50000.)
-    
-#from SkewT
-#def get_lcl(startp,startt,startdp,nsteps=101):
-#    from numpy import interp
-#    #--------------------------------------------------------------------
-#    # Lift a parcel dry adiabatically from startp to LCL.
-#    # Init temp is startt in K, Init dew point is stwrtdp,
-#    # pressure levels are in Pa    
-#    #--------------------------------------------------------------------
-#
-#    assert startdp<=startt
-#
-#    if startdp==startt:
-#        return np.array([startp]),np.array([startt]),np.array([startdp]),
-#
-#    # Pres=linspace(startp,60000.,nsteps)
-#    Pres=np.logspace(np.log10(startp),np.log10(60000.),nsteps)
-#
-#    # Lift the dry parcel
-#    T_dry=(startt)*(Pres/startp)**(Rs_da/Cp_da) 
-#    # Mixing ratio isopleth
-#    starte=VaporPressure(startdp)
-#    startw=MixRatio(starte,startp)
-#    e=Pres*startw/(.622+startw)
-#    T_iso=243.5/(17.67/np.log(e/6.112)-1.) + degCtoK
-#
-#    # Solve for the intersection of these lines (LCL).
-#    # interp requires the x argument (argument 2)
-#    # to be ascending in order!
-#    P_lcl=interp(0.,T_iso-T_dry,Pres)
-#    T_lcl=interp(P_lcl,Pres[::-1],T_dry[::-1])
-#
-#    # # presdry=linspace(startp,P_lcl)
-#    # presdry=logspace(log10(startp),log10(P_lcl),nsteps)
-#
-#    # tempdry=interp(presdry,Pres[::-1],T_dry[::-1])
-#    # tempiso=interp(presdry,Pres[::-1],T_iso[::-1])
-#
-#    return P_lcl,T_lcl
-
-
-
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
-
-
-
-#from class
-def get_lcl(startp,startt,startqv):
-        # Find lifting condensation level iteratively
-    lcl = 20.
-    RHlcl = 0.5
-    
-    itmax = 30
-    it = 0
-    while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       #self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_air_input()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        #print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_air_input()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_air_input()
-       
-
-
-    def get_values_air_input(self,latitude=None,longitude=None):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        BLHV = np.max((BLHV,10.))
-        BLHVu = np.max((BLHVu,10.))
-        BLHVd = np.max((BLHVd,10.))
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
-                
-                # get index of lowest valid observation. This seems to vary
-                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
-                if len(idxvalid) > 0:
-                    #print('idxvalid',idxvalid)
-                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
-                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
-                    else:
-                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
-                    #print(col,meanabl)
-               
-                
-                # if col == 'PRES':
-                #     meanabl =  
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV',]:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
-                        dtheta = np.max((0.1,dtheta_pre))
-                        #meanabl = meanabl - (dtheta - dtheta_pre)
-                        #print('dtheta_pre',dtheta_pre)
-                        #print('dtheta',dtheta)
-                        #print('meanabl',meanabl)
-                        #stop
-                        
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
-            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-            
-            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
-        
-            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
-            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
-
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-
-        try:
-            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
-        except:
-            print("could not convert latitude coordinate")
-            PARAMS.insert(0,'latitude', np.nan)
-            PARAMS.insert(0,'lat', np.nan)
-        try:
-            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
-            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
-            PARAMS.insert(0,'lon', 0.)
-        except:
-            print("could not convert longitude coordinate")
-            PARAMS.insert(0,'longitude', np.nan)
-            PARAMS.insert(0,'lon', 0.)
-
-        if latitude is not None:
-            print('overwriting latitude with specified value')
-            PARAMS['latitude'] = np.float(latitude)
-            PARAMS['lat'] = np.float(latitude)
-        if longitude is not None:
-            print('overwriting longitude with specified value')
-            PARAMS['longitude'] = np.float(longitude)
-        try:
-            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
-            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
-            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            # This is the nearest datetime when sun is up (for class)
-            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
-            # apply the same time shift for UTC datetime
-            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
-            
-        except:
-            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
-            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
-            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
-            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
-
-        
-
-        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
-        # as we are forcing lon equal to zero this is is expressed in local suntime
-        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
-
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
-        elif self.mode == 'b': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNb
-            BLCOLUMN = ONE_COLUMNb
-        elif self.mode == 'u': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNu
-            BLCOLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNd
-            BLCOLUMN = ONE_COLUMNd
-        else:
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb
-
-        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
-        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
-        # print(BLCOLUMN['HAGL'][lt6000])
-        # print(BLCOLUMN['HAGL'][lt2500])
-        # 
-        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
-
-        #print(BLCOLUMN['HAGL'][lt2500])
-        PARAMS.insert(0,'OK',
-                      ((BLHVe < 200.) and 
-                       ( len(np.where(lt6000)[0]) > 5) and
-                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
-                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
-                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
-                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
-                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
-                      )
-                     )
-
-        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
-        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
-        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
-        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
-        
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = USE_ONECOLUMN
-        # if self.mode == 'o': #original 
-        #     self.ONE_COLUMN = ONE_COLUMN
-        # elif self.mode == 'b': # best BLH
-        #     self.ONE_COLUMN = ONE_COLUMNb
-        # elif self.mode == 'u':# upper BLH
-        #     self.ONE_COLUMN = ONE_COLUMNu
-        # elif self.mode == 'd': # lower BLH
-        #     self.ONE_COLUMN=ONE_COLUMNd
-        # else:
-        #     self.ONE_COLUMN = ONE_COLUMN
-
diff --git a/dist/class4gl-0.1dev/lib/data_global.py b/dist/class4gl-0.1dev/lib/data_global.py
deleted file mode 100644
index 9c3d9b5..0000000
--- a/dist/class4gl-0.1dev/lib/data_global.py
+++ /dev/null
@@ -1,936 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: Hendrik Wouters
-
-Purpose: provides class routines for ground and atmosphere conditions used for
-the CLASS miced-layer model
-
-Usage:
-    from data_global import data_global
-    from class4gl import class4gl_input
-    from data_soundings import wyoming
-
-    # create a data_global object and load initial data pages
-    globaldata = data_global()
-    globaldata.load_datasets()
-    # create a class4gl_input object
-    c4gli = class4gl_input()
-    # Initialize it with profile data. We need to do this first. Actually this
-    # will set the coordinate parameters (datetime, latitude, longitude) in
-    # class4gl_input.pars.__dict__, which is required to read point data from
-    # the data_global object.
-
-    # open a Wyoming stream for a specific station
-    wy_strm = wyoming(STNM=91376)
-    # load the first profile
-    wy_strm.find_first()
-    # load the profile data into the class4gl_input object
-    c4gli.get_profile_wyoming(wy_strm)
-    
-    # and finally, read the global input data for this profile
-    c4gli.get_global_input(globaldata)
-
-
-"""
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-#import pynacolada as pcd
-import pandas as pd
-import xarray as xr
-import os
-import glob
-import sys
-import errno
-import warnings
-import logging
-
-
-#formatter = logging.Formatter()
-logging.basicConfig(format='%(asctime)s - \
-                               %(name)s - \
-                               %(levelname)s - \
-                               %(message)s')
-
-class book(object):
-    """ this is a class for a dataset spread over multiple files. It has a
-    similar purpose  open_mfdataset, but only 1 file (called current 'page')
-    one is loaded at a time. This saves precious memory.  """
-    def __init__(self,fn,concat_dim = None,debug_level=None):
-        self.logger = logging.getLogger('book')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        # filenames are expanded as a list and sorted by filename
-        self.pages = glob.glob(fn); self.pages.sort()
-        # In case length of the resulting list is zero, this means no file was found that matches fn. In that case we raise an error.
-        if len(self.pages) == 0:
-            raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), fn)
-        self.ipage = -1; self.page = None
-        self.renames = {} # each time when opening a file, a renaming should be done.
-        self.set_page(0)
-
-        # we consider that the outer dimension is the one we concatenate
-        self.concat_dim = concat_dim
-        if self.concat_dim is None:
-            self.concat_dim = self.concat_dim=list(self.page.dims.keys())[0]
-
-    # this wraps the xarray sel-commmand
-    def sel(*args, **kwargs):
-        for dim in kwargs.keys():
-            if dim == self.concat_dim:
-                self.browse_page(**{dim: kwargs[dim]})
-        return page.sel(*args,**kwargs)
-
-
-    ## this wraps the xarray class -> some issues with that, so I just copy the sel command (which I do not use yet)
-    #def __getattr__(self,attr):
-    #    orig_attr = self.page.__getattribute__(attr)
-    #    if callable(orig_attr):
-    #        def hooked(*args, **kwargs):
-    #            for dim in kwargs.keys():
-    #                if dim == self.concat_dim:
-    #                    self.browse_page(**{dim: kwargs[dim]})
-    #
-    #            result = orig_attr(*args, **kwargs)
-    #            # prevent wrapped_class from becoming unwrapped
-    #            if result == self.page:
-    #                return self
-    #            self.post()
-    #            return result
-    #        return hooked
-    #    else:
-    #        return orig_attr
-
-    def set_renames(self,renames):
-        #first, we convert back to original names, and afterwards, we apply the update of the renames.
-        reverse_renames = dict((v,k) for k,v in self.renames.items())
-        self.renames = renames
-        if self.page is not None:
-            self.page = self.page.rename(reverse_renames)
-            self.page = self.page.rename(self.renames)
-
-    def set_page(self,ipage,page=None):
-        """ this sets the right page according to ipage:
-                - We do not switch the page if we are already at the right one
-                - we set the correct renamings (level -> lev, latitude -> lat,
-                etc.)
-                - The dataset is also squeezed.
-        """
-
-        if ((ipage != self.ipage) or (page is not None)):
-
-            if self.page is not None:
-                self.page.close()
-
-            self.ipage = ipage
-            if page is not None:
-                self.page = page
-            else:
-                if self.ipage == -1:
-                   self.page = None
-                else:
-                    #try:
-
-                    self.logger.info("Switching to page "+str(self.ipage)+': '\
-                                     +self.pages[self.ipage])
-                    self.page = xr.open_dataset(self.pages[self.ipage])
-
-
-            # do some final corrections to the dataset to make them uniform
-            if self.page is not None:
-               if 'latitude' in self.page.dims:
-#    sel       f.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-                   self.page = self.page.rename({'latitude':'lat','longitude':'lon'})
-               if 'level' in self.page.dims:
-                   self.page = self.page.rename({'level':'lev'})
-
-               self.page = self.page.rename(self.renames)
-               self.page = self.page.squeeze(drop=True)
-
-    def browse_page(self,rewind=2,**args):
-
-        # at the moment, this is only tested with files that are stacked according to the time dimension.
-        dims = args.keys()
-
-
-        if self.ipage == -1:
-            self.set_page(0)
-
-        found = False
-        iipage = 0
-        startipage = self.ipage - rewind
-        while (iipage < len(self.pages)) and not found:
-            ipage = (iipage+startipage) % len(self.pages)
-            for dim in args.keys():
-                this_file = True
-
-                # here we store the datetimes in a directly-readable dictionary, so that we don't need to load it every time again
-                if 'dims' not in self.__dict__:
-                    self.dims = {}
-                if dim not in self.dims.keys():
-                    self.dims[dim] = [None]*len(self.pages)
-
-                if self.dims[dim][ipage] is None:
-                    self.logger.info('Loading coordinates of dimension "'+dim+\
-                                     '" of page "' +str(ipage)+'".')
-                    self.set_page(ipage)
-                    # print(ipage)
-                    # print(dim)
-                    # print(dim,self.page[dim].values)
-                    self.dims[dim][ipage] = self.page[dim].values
-
-                # determine current time range of the current page
-                mindim = self.dims[dim][ipage][0] -(self.dims[dim][ipage][1] - self.dims[dim][ipage][0])/2.
-                maxdim = self.dims[dim][ipage][-1] +(self.dims[dim][ipage][-1] - self.dims[dim][ipage][-2])/2.
-
-                if not ((args[dim] >= mindim) and (args[dim] < maxdim )):
-                    this_file = False
-
-            if this_file:
-                found = True
-                self.set_page(ipage)
-            else:
-
-                #if ((args[dim] >= self.page[dim].min().values) and (args[dim] < self.page[dim].max().values)):
-                #    iipage = len(self.pages) # we stop searching
-
-                iipage += 1
-
-        if not found:
-            self.logger.info("Page not found. Setting to page -1")
-            #iipage = len(self.pages) # we stop searching further
-            self.set_page(-1)
-
-        if self.ipage != -1:
-            self.logger.debug("I'm now at page "+ str(self.ipage)+': '+self.pages[self.ipage])
-        else:
-            self.logger.debug("I'm now at page "+ str(self.ipage))
-
-
-class data_global(object):
-    def __init__(self,sources= {
-        # # old gleam
-        # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
-        # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
-        # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
-        # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
-        #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
-        "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
-        "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
-        'IGBPDIS:wsat'  : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc',
-        "ERAINT:Ts"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:Tsoil"  : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc:stl1",
-        "ERAINT:T2"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc:stl2",
-        "ERAINT:cc"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc:tcc",
-        'IGBPDIS:wfc'   : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc',
-        'IGBPDIS:wwilt' : '/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc:wwp',
-        'MOD44B:cveg'   : '/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc:fv',
-        #'CERES:cc'      : '/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset*.nc:cldarea_total_1h',
-        "DSMW:b"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:b",
-        #"DSMW.C1sat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C1sat",
-        #"DSMW.C2ref"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:C2ref",
-        #"DSMW.p"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:p",
-        #"DSMW.a"        : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:a",
-        #"DSMW.CGsat"    : "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc:DSMW:CGsat",
-        "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
-        #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
-        #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
-        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
-        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
-        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
-        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
-        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
-        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
-        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
-        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
-        #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
-        #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
-        "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
-        "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
-        #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
-        },debug_level=None):
-        self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
-        self.sources = sources
-        self.datarefs = {}
-        self.datasets = {}
-        self.datetime = dt.datetime(1981,1,1)
-
-        self.logger = logging.getLogger('data_global')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-        self.debug_level = debug_level
-
-        warnings.warn('omitting pressure field p and advection')
-
-    def in_library(self,fn):
-        if fn not in self.library.keys():
-            return False
-        else:
-            print("Warning: "+fn+" is already in the library.")
-            return True
-
-    def add_to_library(self,fn):
-        if not self.in_library(fn):
-            print("opening: "+fn)
-            self.library[fn] = \
-                book(fn,concat_dim='time',debug_level=self.debug_level)
-
-            #self.library[fn] = xr.open_mfdataset(fn,concat_dim='time')
-            #if 'latitude' in self.library[fn].variables:
-            #    self.library[fn] = self.library[fn].rename({'latitude':'lat','longitude':'lon'})
-
-
-    # default procedure for loading datasets into the globaldata library
-    def load_dataset_default(self,input_fn,varssource=None,varsdest=None):
-        if type(varssource) is str:
-            varssource = [varssource]
-        if type(varsdest) is str:
-            varsdest = [varsdest]
-
-        self.add_to_library(input_fn)
-
-        if varssource is None:
-            varssource = []
-            for var in self.sources[input_fn].variables:
-                avoid = \
-                ['lat','lon','latitude','longitude','time','lev','level']
-                if ((len(list(var.shape)) >= 2) & (var not in avoid)): #two-dimensional array
-                    varssource.append(var)
-
-        if varsdest is None:
-            varsdest = varssource
-
-        #input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        for ivar,vardest in enumerate(varsdest):
-            varsource = varssource[ivar]
-            print('setting '+vardest+' as '+varsource+' from '+input_fn)
-
-            if vardest in self.datarefs.keys():
-                print("Warning! "+vardest+' is already provided by ',self.datarefs[vardest]+'. \n Overwriting....')
-            #self.add_to_library(fn,varsource,vardest)
-            if vardest != varsource:
-                libkey = input_fn+'.'+varsource+'.'+vardest
-                if libkey not in self.library.keys():
-                    #self.library[libkey] = self.library[input_fn].rename({varsource:vardest})
-                    self.library[libkey] = book(input_fn,\
-                                                debug_level=self.debug_level)
-                    self.library[libkey].set_renames({varsource: vardest})
-
-                self.datarefs[vardest] = libkey # this is to remember that it was originally varsource in input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-            else:
-                self.datarefs[vardest] = input_fn
-                self.datasets[vardest] =self.library[self.datarefs[vardest]]
-
-            # if ((vardest is not None) & (vardest not in self.datasets[vardest].variables)):
-            #     print('Warning: '+ vardest "not in " + input_fn)
-
-
-
-    def load_datasets(self,sources = None,recalc=0):
-
-        if sources is None:
-            sources = self.sources
-        for key in sources.keys():
-            #datakey,vardest,*args = key.split(':')
-            datakey,vardest = key.split(':')
-            #print(datakey)
-
-            fnvarsource = sources[key].split(':')
-            if len(fnvarsource) > 2:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource,fnargs = fnvarsource
-                fnargs = [fnargs]
-            elif len(fnvarsource) > 1:
-                #fn,varsource,*fnargs = fnvarsource
-                fn,varsource = fnvarsource
-                fnargs = []
-            else:
-                fn = sources[key]
-                varsource = vardest
-            self.load_dataset(fn,varsource,vardest,datakey,recalc=recalc)
-
-    def load_dataset(self,fn,varsource,vardest,datakey,recalc=0):
-            # the default way of loading a 2d dataset
-            if datakey in ['CERES','GLEAM','ERAINT','GIMMS']:
-                self.load_dataset_default(fn,varsource,vardest)
-            elif datakey == 'IGBPDIS':
-                if vardest == 'alpha':
-                    ltypes = ['W','B','H','TC']
-                    for ltype in ltypes:
-                        self.load_dataset_default(fn,'f'+ltype,'f'+ltype)
-                        ##self.datasets['f'+ltype]['f'+ltype]=  self.datasets['f'+ltype]['f'+ltype].squeeze(drop=True)
-
-
-                    # landfr = {}
-                    # for ltype in ['W','B','H','TC']:
-                    #     landfr[ltype] = datasets['f'+ltype]['f'+ltype].values
-
-
-
-                    keytemp = 'alpha'
-                    fnkeytemp = fn+':IGBPDIS:alpha'
-                    if (os.path.isfile(fnkeytemp)) and ( recalc < 6):
-                        self.library[fnkeytemp]  = book(fnkeytemp,
-                                                        debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-                    else:
-                        self.library[fn+':IGBPDIS:alpha'] = xr.Dataset()
-                        #self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.zeros_like(self.datasets['IGBPDIS']['IGBPDIS'],dtype=np.float)*np.nan
-                        self.library[fn+':IGBPDIS:alpha']['lat'] = self.datasets['fW'].page['lat']
-                        self.library[fn+':IGBPDIS:alpha']['lon'] = self.datasets['fW'].page['lon']
-                        self.library[fn+':IGBPDIS:alpha'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['fW'].page['lon'].shape[0],self.datasets['fW'].page['lat'].shape[0]),dtype=np.float),dims=('lon','lat'))
-                        self.datasets[keytemp] = self.library[fn+':IGBPDIS:alpha']
-                        self.datarefs[keytemp] =fn+':IGBPDIS:alpha'
-
-                        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-
-                        alpha=self.library[fn+':IGBPDIS:alpha'][keytemp].values
-                        for ltype in ltypes:
-                            alpha += self.datasets['f'+ltype].page['f'+ltype].values*aweights[ltype]
-
-                        self.library[fn+':IGBPDIS:alpha'][keytemp].values = alpha
-                        print('writing file to: '+fnkeytemp)
-                        os.system('rm '+fnkeytemp)
-                        self.library[fnkeytemp].to_netcdf(fnkeytemp)
-                        self.library[fnkeytemp].close()
-
-
-                        self.library[fnkeytemp]  = \
-                            book(fnkeytemp,debug_level=self.debug_level)
-                        self.datasets[keytemp] = self.library[fnkeytemp]
-                        self.datarefs[keytemp] = fnkeytemp
-
-
-                else:
-                    self.load_dataset_default(fn,varsource,vardest)
-
-
-            elif datakey == 'GLAS':
-                self.load_dataset_default(fn,varsource,vardest)
-                if vardest == 'z0m':
-                    self.datasets['z0m'].page['z0m'].values = (self.datasets['z0m'].page['z0m'].values/10.).clip(0.01,None)
-                elif vardest == 'z0h':
-                    self.datasets['z0h'].page['z0h'].values = (self.datasets['z0h'].page['z0h'].values/100.).clip(0.001,None)
-            elif datakey == 'DSMW':
-
-
-                # Procedure of the thermal properties:
-                # 1. determine soil texture from DSMW/10.
-                # 2. soil type with look-up table (according to DWD/EXTPAR)
-                # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987)
-                #    with parameter look-up table from Noilhan and Planton (1989).
-                #    Note: The look-up table is inspired on DWD/COSMO
-
-                # to do: implement inheretance, so that the the preliminary output of DSMW or any other dataset can be calculated first
-
-
-
-                fnout = fn.replace('*','') # for storing computationally heavy soil properties, instead of calculating everytime
-                self.load_dataset_default(fn,'DSMW')
-                print('calculating texture')
-                SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code','undefined']
-                TEMP  = {}
-                TEMP2 = self.datasets['DSMW'].page['DSMW'].values
-                TEMP3 = {}
-                for SPKEY in SPKEYS:
-
-
-                    keytemp = SPKEY+'_values'
-                    fnoutkeytemp = fnout+':DSMW:'+keytemp
-                    if (os.path.isfile(fnoutkeytemp)) and ( recalc < 5 ):
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                    else:
-                        #DSMW = self.datasets['DSMW']['DSMW']#   self.input_nc.variables['DSMW'][ilat,ilon]
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lat'] = self.datasets['DSMW'].page['lat']
-                        self.library[fn+':DSMW:'+SPKEY+'_values']['lon'] = self.datasets['DSMW'].page['lon']
-                        self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.int),dims=('lat','lon'))
-                        #self.library[fn+':DSMW:'+SPKEY+'_values'][SPKEY+'_values'] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=(np.int if SPKEY == 'code' else np.float))
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-                        # for faster computation, we need to get it to memory out of Dask.
-                        TEMP[SPKEY] = self.datasets[SPKEY+'_values'][SPKEY+'_values'].values
-                        TEMP3[SPKEY] = self.datasets['DSMW'].page[SPKEY].values
-
-                # yes, I know I only check the last file.
-                if not ((os.path.isfile(fnoutkeytemp)) and ( recalc < 5)):
-                    for idx in range(len(self.datasets['DSMW'].page['tex_coarse'].values))[:]:
-                        print('idx',idx,SPKEY)
-                        SEL = (TEMP2 == idx)
-                    #     print(idx,len(TEMP3))
-                        for SPKEY in SPKEYS:
-                            TEMP[SPKEY][SEL] = TEMP3[SPKEY][idx]
-
-                    for SPKEY in SPKEYS:
-                        keytemp = SPKEY+'_values'
-                        fnoutkeytemp = fnout+':DSMW:'+keytemp
-                        self.datasets[SPKEY+'_values'][SPKEY+'_values'].values = TEMP[SPKEY][:]
-                        os.system('rm '+fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].to_netcdf(fnoutkeytemp)
-                        self.datasets[SPKEY+'_values'].close()
-
-
-                        self.library[fn+':DSMW:'+SPKEY+'_values'] = \
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY+'_values'] = self.library[fn+':DSMW:'+SPKEY+'_values']
-                        self.datarefs[SPKEY+'_values'] =fn+':DSMW:'+SPKEY+'_values'
-
-
-                keytemp = 'texture'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 3 ):
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-                else:
-                    self.library[fn+':DSMW:texture'] = xr.Dataset()
-                    #self.library[fn+':DSMW:texture'][keytemp] = xr.zeros_like(self.datasets['DSMW']['DSMW'],dtype=np.float)*np.nan
-                    self.library[fn+':DSMW:texture']['lat'] = self.datasets['DSMW'].page['lat']
-                    self.library[fn+':DSMW:texture']['lon'] = self.datasets['DSMW'].page['lon']
-                    self.library[fn+':DSMW:texture'][keytemp] = xr.DataArray(np.zeros(shape=(self.datasets['DSMW'].page['lat'].shape[0],self.datasets['DSMW'].page['lon'].shape[0]),dtype=np.float),dims=('lat','lon'))
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-
-                    self.datasets[keytemp][keytemp].values = (0.5*self.datasets['tex_medium_values'].page['tex_medium_values'].values+1.0*self.datasets['tex_coarse_values'].page['tex_coarse_values'].values)/(self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+self.datasets['tex_fine_values'].page['tex_fine_values'].values)
-
-                    zundef = np.array(self.datasets['undefined_values'].page['undefined_values'].values,dtype=np.float)
-                    zundef[zundef < 0] = np.nan
-                    zsum_tex = self.datasets['tex_coarse_values'].page['tex_coarse_values'].values+self.datasets['tex_medium_values'].page['tex_medium_values'].values+ self.datasets['tex_fine_values'].page['tex_fine_values'].values
-                    VALID  = (zsum_tex >= zundef) *( ~np.isnan(zundef))
-
-                    self.datasets[keytemp][keytemp].values[~VALID] = 9012.
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-
-
-                    self.library[fnoutkeytemp]  = \
-                        book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:texture']
-                    self.datarefs[keytemp] =fn+':DSMW:texture'
-
-
-                print('calculating texture type')
-
-
-
-                keytemp = 'itex'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 2 ):
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-                else:
-                    self.library[fnoutkeytemp] = xr.Dataset()
-                    self.library[fnoutkeytemp][keytemp] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-                    X = self.datasets['texture'].page['texture'].values*100
-                    X[pd.isnull(X)] = -9
-
-
-                    self.datasets[keytemp][keytemp].values = X
-
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets['itex'].to_netcdf(fnoutkeytemp)
-                    self.datasets['itex'].close()
-
-
-                    self.library[fnoutkeytemp] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets[keytemp] = self.library[fn+':DSMW:itex']
-                    self.datarefs[keytemp] =fn+':DSMW:itex'
-
-
-                keytemp = 'isoil'
-                fnoutkeytemp=fnout+':DSMW:'+keytemp
-                isoil_reprocessed = False
-                if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-                else:
-                    isoil_reprocessed = True
-                    print('calculating soil type')
-                    self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                    self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    self.datasets['isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                    ITEX = self.datasets['itex'].page['itex'].values
-                    ISOIL = 9 + 0.*self.datasets['isoil']['isoil'].values
-                    LOOKUP = [
-                              [-10 ,9],# ocean
-                              [0 ,7],# fine textured, clay (soil type 7)
-                              [20,6],# medium to fine textured, loamy clay (soil type 6)
-                              [40,5],# medium textured, loam (soil type 5)
-                              [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                              [80,3],# coarse textured, sand (soil type 3)
-                              [100,9],# coarse textured, sand (soil type 3)
-                            ]
-                    for iitex,iisoil in LOOKUP:
-                        ISOIL[ITEX > iitex] = iisoil
-                        print('iitex,iisoil',iitex,iisoil)
-
-
-                    #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-                    LOOKUP = [
-                              [9001, 1 ], # ice, glacier (soil type 1)
-                              [9002, 2 ], # rock, lithosols (soil type 2)
-                              [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                              [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                              [9,    9 ], # undefined (ocean)
-                              [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                              [9000, 9 ], # undefined (inland lake)
-                              [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                              [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                            ]
-                    # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-                    CODE_VALUES = self.datasets['code_values'].page['code_values'].values
-
-                    CODE_VALUES[ITEX == 901200] = 9012
-                    for icode,iisoil in LOOKUP:
-                        ISOIL[CODE_VALUES == icode] = iisoil
-
-                    self.datasets['isoil']['isoil'].values = ISOIL
-                    os.system('rm '+fnoutkeytemp)
-                    self.datasets[keytemp].to_netcdf(fnoutkeytemp)
-                    self.datasets[keytemp].close()
-                    print('saved inbetween file to: '+fnoutkeytemp)
-
-                    self.library[fn+':DSMW:isoil'] = \
-                            book(fnoutkeytemp,debug_level=self.debug_level)
-                    self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                    self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-                #adopted from data_soil.f90 (COSMO5.0)
-                SP_LOOKUP = {
-                  # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea
-                  # (by index)                                           loam                    loam                                water      ice
-                  'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-                  'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-                  'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-                  'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-                  'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-                  'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-                  'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-                  'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-                  'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-                  'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-                  'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-                  'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-                  'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-                  'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-                  'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-                  'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-                  'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-                  'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-                  'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-                  # Important note: For peat, the unknown values below are set equal to that of loam
-                  #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-                  'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , 5.39    , np.nan   ,  np.nan  ],
-                  #error in table 2 of NP89: values need to be multiplied by e-6
-                  'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-                  'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , 6.    , np.nan   ,  np.nan  ],
-
-                  'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , 0.148    , np.nan   ,  np.nan  ],
-                  'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , 0.191    , np.nan   ,  np.nan  ],
-                  'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , 0.8    , np.nan   ,  np.nan  ],
-                }
-
-
-                # isoil_reprocessed = False
-                # if (os.path.isfile(fnoutkeytemp)) and ( recalc < 1):
-
-                #     self.library[fn+':DSMW:isoil'] = \
-                #             book(fnoutkeytemp,debug_level=self.debug_level)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-                # else:
-                #     isoil_reprocessed = True
-                #     print('calculating soil type')
-                #     self.library[fn+':DSMW:isoil'] = xr.Dataset()
-                #     self.library[fn+':DSMW:isoil']['isoil'] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.int)
-                #     self.datasets['isoil'] = self.library[fn+':DSMW:isoil']
-                #     self.datarefs['isoil'] =fn+':DSMW:isoil'
-
-
-
-
-                # this should become cleaner in future but let's hard code it for now.
-                DSMWVARS = ["b", "C1sat","C2ref","p","a" ]
-                print('calculating soil parameter')
-                DATATEMPSPKEY = {}
-                if (recalc < 1) and (isoil_reprocessed == False): 
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        keytemp = SPKEY
-                        fnoutkeytemp=fnout+':DSMW:'+keytemp
-                        self.library[fn+':DSMW:'+SPKEY] =\
-                                book(fnoutkeytemp,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fnoutkeytemp]
-                        self.datarefs[SPKEY] =fnoutkeytemp
-                else:
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-
-                        self.library[fn+':DSMW:'+SPKEY] = xr.Dataset()
-                        self.library[fn+':DSMW:'+SPKEY][SPKEY] = xr.zeros_like(self.datasets['DSMW'].page['DSMW'],dtype=np.float)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-                        DATATEMPSPKEY[SPKEY] = self.datasets[SPKEY][SPKEY].values
-                    ISOIL = self.datasets['isoil'].page['isoil'].values
-                    print(np.where(ISOIL>0.))
-                    for i in range(11):
-                        SELECT = (ISOIL == i)
-                        for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                            DATATEMPSPKEY[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-
-                    for SPKEY in DSMWVARS:#SP_LOOKUP.keys():
-                        self.datasets[SPKEY][SPKEY].values = DATATEMPSPKEY[SPKEY]
-
-                        os.system('rm '+fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].to_netcdf(fn+':DSMW:'+SPKEY)
-                        self.datasets[SPKEY].close()
-                        print('saved inbetween file to: '+fn+':DSMW:'+SPKEY)
-
-                        self.library[fn+':DSMW:'+SPKEY] = \
-                                book(fn+':DSMW:'+SPKEY,debug_level=self.debug_level)
-                        self.datasets[SPKEY] = self.library[fn+':DSMW:'+SPKEY]
-                        self.datarefs[SPKEY] =fn+':DSMW:'+SPKEY
-
-
-            else:
-                self.load_dataset_default(fn,varsource,vardest)
-
-
-
-
-
-
-#
-#                 # only print the last parameter value in the plot
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'cala'
-#                 #class_settings.__dict__[var] = np.float(SP['cala0'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#                 #inputs.append(cp.deepcopy(class_settings))
-#                 #var = 'crhoc'
-#                 #class_settings.__dict__[var] = np.float(SP['crhoc'])
-#                 #valnew = class_settings.__dict__[var]
-#                 #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#     key = "CERES"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         CERES_start_date = dt.datetime(2000,3,1)
-#         DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-#         DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-#         print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-#
-#         var = 'cc'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime_end = np.where(np.array(pcd.ncgetdatetime(input_nc))  < (class_settings.datetime+dt.timedelta(hours=int(class_settings.runtime/3600.))))[0][-1]
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#         print(class_settings.lat,class_settings.lon)
-#
-#         class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:idatetime_end,ilat,ilon])/100.
-#
-#         input_nc.close()
-#
-
-
-#     key = "GIMMS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-#         print("Reading Leag Area Index from "+input_fn)
-#         var = 'LAI'
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-#
-#         print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-#         tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-#
-#         if np.isnan(tarray[idatetime]):
-#             print("interpolating GIMMS cveg nan value")
-#
-#             mask = np.isnan(tarray)
-#             if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-#                 tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-#             else:
-#                 print("Warning. Could not interpolate GIMMS cveg nan value")
-#
-#         class_settings.__dict__[var] = tarray[idatetime]
-#
-#         input_nc.close()
-#
-#     key = "IGBPDIS_ALPHA"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         var = 'alpha'
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-#         print("Reading albedo from "+input_fn)
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#
-#         landfr = {}
-#         for ltype in ['W','B','H','TC']:
-#             landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-#
-#         aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-#
-#         alpha=0.
-#         for ltype in landfr.keys():
-#             alpha += landfr[ltype]*aweights[ltype]
-#
-#
-#         class_settings.__dict__[var] = alpha
-#         input_nc.close()
-#
-#
-#     key = "ERAINT_ST"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         print("Reading soil temperature from "+input_fn)
-#
-#         var = 'Tsoil'
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-#
-#         input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-#         var = 'T2'
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-#
-#         ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-#         ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-#
-#
-#         class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-#
-#
-#         input_nc.close()
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #var = 'T2'
-#     #valold = class_settings.__dict__[var]
-#     #
-#     #class_settings.__dict__[var] = 305.
-#     #class_settings.__dict__['Tsoil'] = 302.
-#     #valnew = class_settings.__dict__[var]
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     #inputs.append(cp.deepcopy(class_settings))
-#     #
-#     #var = 'Lambda'
-#     #valold = class_settings.__dict__[var]
-#
-#     ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book.
-#     ## I need to ask Chiel.
-#     ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-#     #
-#     #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg']
-#     #class_settings.__dict__[var] = valnew
-#     #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-#
-#
-#
-#     key = "GLAS"
-#     if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-#
-#         input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-#         print("Reading canopy height for determining roughness length from "+input_fn)
-#         var = 'z0m'
-#
-#
-#         #plt.plot
-#
-#         input_nc = nc4.Dataset(input_fn,'r')
-#
-#         ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-#         ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-#
-#         testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-#
-#         lowerlimit = 0.01
-#         if testval < lowerlimit:
-#             print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-#             class_settings.__dict__[var] = lowerlimit
-#         else:
-#             class_settings.__dict__[var] = testval
-#
-#         class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-#
-#
-#         input_nc.close()
-
-
-
-
-
diff --git a/dist/class4gl-0.1dev/lib/interface_functions.py b/dist/class4gl-0.1dev/lib/interface_functions.py
deleted file mode 100644
index 3e483f3..0000000
--- a/dist/class4gl-0.1dev/lib/interface_functions.py
+++ /dev/null
@@ -1,506 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-#from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-#'_afternoon.yaml'
-def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
-    filename = yaml_file.name
-    #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-    #yaml_file = open(filename)
-
-    #print('going to next observation',filename)
-    yaml_file.seek(index_start)
-
-    buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-
-    filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-    filebuffer.write(buf)
-    filebuffer.close()
-    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-    
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-
-    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-    print(command)
-    os.system(command)
-    jsonstream = open(filename+'.buffer.json.'+str(index_start))
-    record_dict = json.load(jsonstream)
-    jsonstream.close()
-    os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-
-
-    if mode =='mod':
-        modelout = class4gl()
-        modelout.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        return modelout
-    elif mode == 'ini':
-
- 
-        # datetimes are incorrectly converted to strings. We need to convert them
-        # again to datetimes
-        for key,value in record_dict['pars'].items():
-            # we don't want the key with columns that have none values
-            if value is not None: 
-                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
-               # elif (type(value) == str):
-                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-
-            if (value == 0.9e19) or (value == '.9e19'):
-                record_dict['pars'][key] = np.nan
-        for key in record_dict.keys():
-            #print(key)
-            if key in ['air_ap','air_balloon',]:
-                #NNprint('check')
-                for datakey,datavalue in record_dict[key].items():
-                    record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-
-        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-
-        c4gli = class4gl_input()
-        print(c4gli.logger,'hello')
-        c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+filename+'.buffer.json.'+str(index_start))
-        return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-
-class stations(object):
-    def __init__(self,path,suffix='ini',refetch_stations=False):
-
-        self.path = path
-
-        self.file = self.path+'/stations_list.csv'
-        if (os.path.isfile(self.file)) and (not refetch_stations):
-            self.table = pd.read_csv(self.file)
-        else:
-            self.table = self.get_stations(suffix=suffix)
-            self.table.to_csv(self.file)
-        
-        self.table = self.table.set_index('STNID')
-        #print(self.table)
-
-    def get_stations(self,suffix):
-        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
-        if len(stations_list_files) == 0:
-            stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
-        stations_list_files.sort()
-        print(stations_list_files)
-        if len(stations_list_files) == 0:
-            raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
-        stations_list = []
-        for stations_list_file in stations_list_files:
-            thisfile = open(stations_list_file,'r')
-            yamlgen = yaml.load_all(thisfile)
-            try:
-                first_record  = yamlgen.__next__()
-            except:
-                first_record = None
-            if first_record is not None:
-                stations_list.append({})
-                for column in ['STNID','latitude','longitude']:
-                    #print(first_record['pars'].keys())
-                    stations_list[-1][column] = first_record['pars'][column]
-                stations_list[-1]['filename'] = os.path.split(stations_list_file)[1]
-            yamlgen.close()
-            thisfile.close()
-    
-        print(stations_list)
-        return pd.DataFrame(stations_list)
-
-class stations_iterator(object):
-    def __init__(self,stations):
-        self.stations = stations
-        self.ix = -1 
-    def __iter__(self):
-        return self
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.stations.table)) 
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_row(self,row):
-        self.ix = row
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-    def set_STNID(self,STNID):
-        self.ix = np.where((self.stations.table.index == STNID))[0][0]
-        print(self.ix)
-        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
-        return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
-
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-    def close():
-        del(self.ix)
-
-class records_iterator(object):
-    def __init__(self,records):
-            
-        self.records = records
-        self.ix = -1 
-        
-    def __iter__(self):
-        return self
-
-    def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if self.ix >= len(self.records.index):
-            raise StopIteration
-        self.ix = np.mod(self.ix,len(self.records))
-        return self.records.index[self.ix], self.records.iloc[self.ix]
-    def __prev__(self):
-        return self.__next__(self,jump=-1)
-
-
-# #'_afternoon.yaml'
-# def get_record_yaml(yaml_file,index_start,index_end):
-#     filename = yaml_file.name
-#     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
-#     #yaml_file = open(filename)
-# 
-#     #print('going to next observation',filename)
-#     yaml_file.seek(index_start)
-# 
-#     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
-# 
-#     filebuffer = open(filename+'.buffer.yaml.'+str(index_start),'w')
-#     filebuffer.write(buf)
-#     filebuffer.close()
-#     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-#     
-#     command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+filename+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+filename+'.buffer.json.'+str(index_start)+' '
-# 
-#     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-#     print(command)
-#     os.system(command)
-#     jsonstream = open(filename+'.buffer.json.'+str(index_start))
-#     record_dict = json.load(jsonstream)
-#     jsonstream.close()
-#     os.system('rm '+filename+'.buffer.yaml.'+str(index_start))
-#  
-#     # datetimes are incorrectly converted to strings. We need to convert them
-#     # again to datetimes
-#     for key,value in record_dict['pars'].items():
-#         # we don't want the key with columns that have none values
-#         if value is not None: 
-#             if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','ldatetime_daylight','datetime_daylight','datetime_daylight']:#(type(value) == str):
-#            # elif (type(value) == str):
-#                 record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-#                 
-#                 # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-#                 record_dict['pars'][key] = record_dict['pars'][key].astimezone(pytz.UTC)
-# 
-#         if (value == 0.9e19) or (value == '.9e19'):
-#             record_dict['pars'][key] = np.nan
-#     for key in record_dict.keys():
-#         print(key)
-#         if key in ['air_ap','air_balloon',]:
-#             print('check')
-#             for datakey,datavalue in record_dict[key].items():
-#                 record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
-# 
-#     #os.system('rm '+filename+'.buffer.json.'+str(index_start))
-# 
-#     c4gli = class4gl_input()
-#     c4gli.load_yaml_dict(record_dict)
-#     return c4gli
-
-
-
-
-
-
-        # self.frames['stats']['records_current_station_index'] = \
-        #     (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-        #      == \
-        #      self.frames['stats']['current_station'].name)
-
-        # # create the value table of the records of the current station
-        # tab_suffixes = \
-        #         ['_mod','_obs','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        # for tab_suffix in tab_suffixes:
-        #     self.frames['stats']['records_current_station'+tab_suffix] = \
-        #         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-# class records_selection(object):
-#     def __init__
-
-# class records(object):
-#     def __init__(self,stations,path_obs,path_mod):
-#         self.stations = stations
-#         self.path_obs = path_obs
-#         self.path_mod = path_mod
-# 
-#         self.ini =       self.get_records(self.path_mod,'ini')
-#         self.mod =       self.get_records(self.path_mod,'mod')
-#         #self.morning =   self.get_records(self.path_obs,'morning')
-#         self.afternoon = self.get_records(self.path_obs,'afternoon')
-# 
-#         
-#         self.afternoon.index = self.afternoon.ldatetime.dt.date
-#         self.afternoon = self.afternoon.loc[records_ini.ldatetime.dt.date]
-# 
-#         self.index = self.ini.index
-#         self.mod.index = self.index
-#         self.afternoon.index = self.index
-# 
-# 
-#         #self.records_iterator = records_current_station_mod.iterrows()
-
-
-
-def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
-
-    records = pd.DataFrame()
-    for STNID,station in stations.iterrows():
-        dictfnchunks = []
-        if getchunk is 'all':
-
-            # we try the old single-chunk filename format first (usually for
-            # original profile pairs)
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.yaml'
-            if os.path.isfile(fn):
-                chunk = 0
-                dictfnchunks.append(dict(fn=fn,chunk=chunk))
-
-            # otherwise, we use the new multi-chunk filename format
-            else:
-                chunk = 0
-                end_of_chunks = False
-                while not end_of_chunks:
-                    fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(fn):
-                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
-                    else:
-                        end_of_chunks = True
-                    chunk += 1
-
-            # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
-            # yamlfilenames = glob.glob(globyamlfilenames)
-            # yamlfilenames.sort()
-        else:
-            fn = path_yaml+'/'+format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
-            dictfnchunks.append(dict(fn=fn,chunk=getchunk))
-            
-        if len(dictfnchunks) > 0:
-            for dictfnchunk in dictfnchunks:
-                yamlfilename = dictfnchunk['fn']
-                chunk = dictfnchunk['chunk']
-                print(chunk)
-
-                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
-                pklfilename = yamlfilename.replace('.yaml','.pkl')
-
-                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
-                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
-                generate_pkl = False
-                if not os.path.isfile(pklfilename): 
-                    print('pkl file does not exist. I generate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                elif not (os.path.getmtime(yamlfilename) <  \
-                    os.path.getmtime(pklfilename)):
-                    print('pkl file older than yaml file, so I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-
-                if refetch_records:
-                    print('refetch_records flag is True. I regenerate "'+\
-                          pklfilename+'" from "'+yamlfilename+'"...')
-                    generate_pkl = True
-                if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(pklfilename)])
-                   # irecord = 0
-                else:
-                    with open(yamlfilename) as yaml_file:
-
-                        dictout = {}
-
-                        next_record_found = False
-                        end_of_file = False
-                        while (not next_record_found) and (not end_of_file):
-                            linebuffer = yaml_file.readline()
-                            next_record_found = (linebuffer == '---\n')
-                            end_of_file = (linebuffer == '')
-                        next_tell = yaml_file.tell()
-                        
-                        while not end_of_file:
-
-                            print(' next record:',next_tell)
-                            current_tell = next_tell
-                            next_record_found = False
-                            yaml_file.seek(current_tell)
-                            filebuffer = open(yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
-                            linebuffer = ''
-                            while ( (not next_record_found) and (not end_of_file)):
-                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
-                                linebuffer = yaml_file.readline()
-                                next_record_found = (linebuffer == '---\n')
-                                end_of_file = (linebuffer == '')
-                            filebuffer.close()
-                            
-                            next_tell = yaml_file.tell()
-                            index_start = current_tell
-                            index_end = next_tell
-
-                            
-                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                            print(command)
-                            
-                            os.system(command)
-                            #jsonoutput = subprocess.check_output(command,shell=True) 
-                            #print(jsonoutput)
-                            #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(yamlfilename+'.buffer.json.'+str(current_tell))
-                            record = json.load(jsonstream)
-                            dictouttemp = {}
-                            for key,value in record['pars'].items():
-                                # we don't want the key with columns that have none values
-                                if value is not None: 
-                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
-                                   if (type(value) in regular_numeric_types):
-                                        dictouttemp[key] = value
-                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
-                            recordindex = record['index']
-                            dictouttemp['chunk'] = chunk
-                            dictouttemp['index_start'] = index_start
-                            dictouttemp['index_end'] = index_end
-                            os.system('rm '+yamlfilename+'.buffer.json.'+str(current_tell))
-                            for key,value in dictouttemp.items():
-                                if key not in dictout.keys():
-                                    dictout[key] = {}
-                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
-                            print(' obs record registered')
-                            jsonstream.close()
-                            os.system('rm '+yamlfilename+'.buffer.yaml.'+str(current_tell))
-                    records_station = pd.DataFrame.from_dict(dictout)
-                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+pklfilename+') for station '\
-                          +str(STNID))
-                    records_station.to_pickle(pklfilename)
-                    # else:
-                    #     os.system('rm '+pklfilename)
-                    records = pd.concat([records,records_station])
-    return records
-
-def stdrel(mod,obs,columns):
-    stdrel = pd.DataFrame(columns = columns)
-    for column in columns:
-        stdrel[column] = \
-                (mod.groupby('STNID')[column].transform('mean') -
-                 obs.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') + \
-                (mod[column] -
-                 mod.groupby('STNID')[column].transform('mean')) /\
-                obs.groupby('STNID')[column].transform('std') 
-    return stdrel
-
-def pct(obs,columns):
-    pct = pd.DataFrame(columns=columns)
-    for column in columns:
-        #print(column)
-        pct[column] = ""
-        pct[column] = obs[column].rank(pct=True)
-    return pct
-
-def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
-    stats = pd.DataFrame()
-    for key in keys: 
-        stats['d'+key+'dt'] = ""
-        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
-                              (obs_afternoon.ldatetime - \
-                               obs_morning.ldatetime).dt.seconds*3600.
-    return stats
-
diff --git a/dist/class4gl-0.1dev/lib/interface_multi.py b/dist/class4gl-0.1dev/lib/interface_multi.py
deleted file mode 100644
index 83148e5..0000000
--- a/dist/class4gl-0.1dev/lib/interface_multi.py
+++ /dev/null
@@ -1,2061 +0,0 @@
-import pandas as pd
-import numpy as np
-import datetime as dt
-import os
-import xarray as xr
-import sys
-from contextlib import suppress
-from time import sleep
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl,units
-from interface_functions import *
-# from data_soundings import wyoming
-import yaml
-import glob
-import pandas as pd
-import json
-import io
-import subprocess
-import pytz
-from scipy.stats import mstats
-
-from matplotlib.colors import LinearSegmentedColormap
-cdictpres = {'blue': (\
-                   (0.,    0.,  0.),
-                   (0.25,  0.25, 0.25),
-                   (0.5,  .70, 0.70),
-                   (0.75, 1.0, 1.0),
-                   (1,     1.,  1.),
-                   ),
-       'green': (\
-                   (0. ,   0., 0.0),
-                   (0.25,  0.50, 0.50),
-                   (0.5,  .70, 0.70),
-                   (0.75,  0.50, 0.50),
-                   (1  ,    0,  0.),
-                   ),
-       'red':  (\
-                  (0 ,  1.0, 1.0),
-                  (0.25 ,  1.0, 1.0),
-                   (0.5,  .70, 0.70),
-                  (0.75 , 0.25, 0.25),
-                  (1,    0., 0.),
-                  )}
-
-statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-os.system('module load Ruby')
-
-class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
-        """ creates an interactive interface for analysing class4gl experiments
-
-        INPUT:
-            path_exp : path of the experiment output
-            path_obs : path of the observations 
-            globaldata: global data that is being shown on the map
-            refetch_stations: do we need to build the list of the stations again?
-        OUTPUT:
-            the procedure returns an interface object with interactive plots
-
-        """
-        
-        # set the ground
-        self.globaldata = globaldata
-
- 
-        self.path_exp = path_exp
-        self.path_obs = path_obs
-        self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
-
-        # # get the list of stations
-        # stationsfile = self.path_exp+'/stations_list.csv'
-        # if (os.path.isfile(stationsfile)) and (not refetch_stations):
-        #     stations = pd.read_csv(stationsfile)
-        # else:
-        #     stations = get_stations(self.path_exp)
-        #     stations.to_csv(stationsfile)
-
-        # stations = stations.set_index('STNID')
-
-        self.frames = {}
-
-        self.frames['stats'] = {}
-        self.frames['worldmap'] = {}
-                
-        self.frames['profiles'] = {}
-        self.frames['profiles'] = {}
-        self.frames['profiles']['DT'] = None
-        self.frames['profiles']['STNID'] = None
-
-        #self.frames['worldmap']['stationsfile'] = stationsfile
-        self.frames['worldmap']['stations'] = stations(self.path_exp, \
-                                                       suffix='ini',\
-                                                       refetch_stations=refetch_stations)
-
-        # Initially, the stats frame inherets the values/iterators of
-        # worldmap
-        for key in self.frames['worldmap'].keys():
-            self.frames['stats'][key] = self.frames['worldmap'][key]
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_ini'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='ini',\
-                                           refetch_records=refetch_records
-                                           )
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_mod'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_exp,\
-                                           subset='mod',\
-                                           refetch_records=refetch_records
-                                           )
-
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_obs_afternoon'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_obs,\
-                                           subset='afternoon',\
-                                           refetch_records=refetch_records
-                                           )
-
-        self.frames['stats']['records_all_stations_mod'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['records_all_stations_ini']['dates'] = \
-            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
-
-
-        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
-
-        self.frames['stats']['records_all_stations_obs_afternoon'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
-
-        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
-
-        self.frames['stats']['viewkeys'] = ['h','theta','q']
-        print('Calculating table statistics')
-        self.frames['stats']['records_all_stations_mod_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_mod'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-
-        self.frames['stats']['inputkeys'] = inputkeys
-        
-        # self.frames['stats']['inputkeys'] = \
-        #     [ key for key in \
-        #       self.globaldata.datasets.keys() \
-        #       if key in \
-        #       list(self.frames['stats']['records_all_stations_obs'].columns)]
-
-
-        # get units from the class4gl units database
-        self.units = dict(units)
-        # for those that don't have a definition yet, we just ask a question
-        # mark
-        for var in self.frames['stats']['inputkeys']:
-            self.units[var] = '?'
-
-        self.frames['worldmap']['inputkeys'] = self.frames['stats']['inputkeys'] 
-        self.frames['stats']['records_all_stations_ini_pct'] = \
-                  pct(self.frames['stats']['records_all_stations_ini'], \
-                      columns = self.frames['stats']['inputkeys'])
-
-        #     pd.DataFrame(columns = self.frames['stats']['viewkeys'])
-        # for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-        #     mod['
-
-        # 
-        # 
-        # \
-        #        self.frames['stats']['records_all_stations_mod'], \
-
-
-
-        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
-        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
-        #               columns = [ 'd'+key+'dt' for key in \
-        #                           self.frames['stats']['viewkeys']], \
-        #              )
-
-        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
-        #               obs = self.frames['stats']['records_all_stations_ini'], \
-        #               columns = self.frames['stats']['viewkeys'], \
-        #              )
-        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
-        
-        print('filtering pathological data')
-        # some observational sounding still seem problematic, which needs to be
-        # investigated. In the meantime, we filter them
-        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
-
-        # we filter ALL data frames!!!
-        for key in self.frames['stats'].keys():
-            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
-               (self.frames['stats'][key].index.names == indextype):
-                self.frames['stats'][key] = self.frames['stats'][key][valid]
-        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
-
-        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
-
-
-        print("filtering stations from interface that have no records")
-        for STNID,station in self.frames['worldmap']['stations'].table.iterrows():
-            if ((self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                    == STNID).sum() == 0):
-                print("dropping", STNID)
-                self.frames['worldmap']['stations'].table = \
-                        self.frames['worldmap']['stations'].table.drop(STNID)
-                    
-        self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-        
-        # TO TEST: should be removed, since it's is also done just below
-        self.frames['stats']['stations_iterator'] = \
-            self.frames['worldmap']['stations_iterator'] 
-
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkeys'][0]
-        self.frames['worldmap']['inputkey'] = self.frames['worldmap']['inputkey']
-        self.next_station()
-
-        # self.goto_datetime_worldmap(
-        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-        #     'after')
-    def sel_station(self,STNID=None,rownumber=None):
-
-        if (STNID is not None) and (rownumber is not None):
-            raise ValueError('Please provide either STNID or rownumber, not both.')
-
-        if (STNID is None) and (rownumber is None):
-            raise ValueError('Please provide either STNID or rownumber.')
-            
-        if STNID is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = self.frames['worldmap']['stations_iterator'].set_STNID(STNID)
-            print(
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-            )
-            self.update_station()
-        elif rownumber is not None:
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-             = STNID,station = self.frames['worldmap']['stations_iterator'].set_row(rownumber)
-            self.update_station()
-
-
-
-    def next_station(self,event=None,jump=1):
-        with suppress(StopIteration):
-            self.frames['worldmap']['STNID'],\
-            self.frames['worldmap']['current_station'] \
-                = self.frames['worldmap']['stations_iterator'].__next__(jump)
-            # self.frames['worldmap']['stations_iterator'].close()
-            # del(self.frames['worldmap']['stations_iterator'])
-            # self.frames['worldmap']['stations_iterator'] = \
-            #                 selfself.frames['worldmap']['stations'].iterrows()
-            # self.frames['worldmap']['STNID'],\
-            # self.frames['worldmap']['current_station'] \
-            #     = self.frames['worldmap']['stations_iterator'].__next__()
-
-        self.update_station()
-
-    def prev_station(self,event=None):
-        self.next_station(jump = -1,event=event)
-    def update_station(self):
-        for key in ['STNID','current_station','stations_iterator']: 
-            self.frames['stats'][key] = self.frames['worldmap'][key] 
-
-
-
-        # generate index of the current station
-        self.frames['stats']['records_current_station_index'] = \
-            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-             == \
-             self.frames['stats']['current_station'].name)
-
-        # create the value table of the records of the current station
-        tab_suffixes = \
-                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-        for tab_suffix in tab_suffixes:
-            self.frames['stats']['records_current_station'+tab_suffix] = \
-                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-        # go to first record of current station
-        self.frames['stats']['records_iterator'] = \
-                        records_iterator(self.frames['stats']['records_current_station_mod'])
-        (self.frames['stats']['STNID'] , \
-        self.frames['stats']['current_record_chunk'] , \
-        self.frames['stats']['current_record_index']) , \
-        self.frames['stats']['current_record_mod'] = \
-                        self.frames['stats']['records_iterator'].__next__()
-
-        for key in self.frames['stats'].keys():
-            self.frames['profiles'][key] = self.frames['stats'][key]
-
-        STNID = self.frames['profiles']['STNID']
-        chunk = self.frames['profiles']['current_record_chunk']
-        if 'current_station_file_ini' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_ini'].close()
-        self.frames['profiles']['current_station_file_ini'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-        if 'current_station_file_mod' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_mod'].close()
-        self.frames['profiles']['current_station_file_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_afternoon'].close()
-        self.frames['profiles']['current_station_file_afternoon'] = \
-            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-        self.frames['profiles']['records_iterator'] = \
-                        records_iterator(self.frames['profiles']['records_current_station_mod'])
-        (self.frames['profiles']['STNID'] , \
-        self.frames['profiles']['current_record_chunk'] , \
-        self.frames['profiles']['current_record_index']) , \
-        self.frames['profiles']['current_record_mod'] = \
-                        self.frames['profiles']['records_iterator'].__next__()
-
-
-        # for the profiles we make a distinct record iterator, so that the
-        # stats iterator can move independently
-
-        self.update_record()
-
-    def next_record(self,event=None,jump=1):
-        with suppress(StopIteration):
-            (self.frames['profiles']['STNID'] , \
-            self.frames['profiles']['current_record_chunk'] , \
-            self.frames['profiles']['current_record_index']) , \
-            self.frames['profiles']['current_record_mod'] = \
-                      self.frames['profiles']['records_iterator'].__next__(jump)
-        # except (StopIteration):
-        #     self.frames['profiles']['records_iterator'].close()
-        #     del( self.frames['profiles']['records_iterator'])
-        #     self.frames['profiles']['records_iterator'] = \
-        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
-        #     (self.frames['profiles']['STNID'] , \
-        #     self.frames['profiles']['current_record_index']) , \
-        #     self.frames['profiles']['current_record_mod'] = \
-        #                     self.frames['profiles']['records_iterator'].__next__()
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        self.update_record()
-
-    def prev_record(self,event=None):
-        self.next_record(jump=-1,event=event)
-
-    def update_record(self):
-        self.frames['profiles']['current_record_ini'] =  \
-            self.frames['profiles']['records_current_station_ini'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'],\
-                  self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon'] =  \
-            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'] , \
-                  self.frames['profiles']['current_record_index'])]
-
-        self.frames['profiles']['current_record_mod_stats'] = \
-                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
-                    self.frames['profiles']['STNID'], \
-                    self.frames['profiles']['current_record_chunk'], \
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_ini_pct'] = \
-                self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
-
-        for key in self.frames['profiles'].keys():
-            self.frames['stats'][key] = self.frames['profiles'][key]
-        # frame
-        # note that the current station, record is the same as the stats frame for initialization
-
-        # select first 
-        #self.frames['profiles']['current_record_index'], \
-        #self.frames['profiles']['record_yaml_mod'] = \
-        #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
-        #                   self.frames['stats']['current_record_index'])
-        self.frames['profiles']['record_yaml_mod'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_mod'], \
-               self.frames['profiles']['current_record_mod'].index_start,
-               self.frames['profiles']['current_record_mod'].index_end,
-               mode='mod')
-                                
-        record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_ini'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_ini'], \
-               record_ini.index_start,
-               record_ini.index_end,
-                mode='ini')
-
-        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
-
-        self.frames['profiles']['record_yaml_obs_afternoon'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_afternoon'], \
-               record_afternoon.index_start,
-               record_afternoon.index_end,
-                mode='ini')
-
-
-        key = self.frames['worldmap']['inputkey']
-        # only redraw the map if the current world map has a time
-        # dimension
-        if 'time' in self.globaldata.datasets[key].page[key].dims:
-            self.goto_datetime_worldmap(
-                self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                'after')
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap',
-                                                  'profiles'])
-        else:
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap_stations',
-                                                  'profiles'])
-
-    def abline(self,slope, intercept,axis):
-        """Plot a line from slope and intercept"""
-        #axis = plt.gca()
-        x_vals = np.array(axis.get_xlim())
-        y_vals = intercept + slope * x_vals
-        axis.plot(x_vals, y_vals, 'k--')
-
-    def plot(self):
-        import pylab as pl
-        from matplotlib.widgets import Button
-        import matplotlib.pyplot as plt
-        import matplotlib as mpl
-        '''
-        Definition of the axes for the sounding table stats
-        '''
-        
-        fig = pl.figure(figsize=(14,9))
-        axes = {} #axes
-        btns = {} #buttons
-
-        # frames, which sets attributes for a group of axes, buttens, 
-        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
-            label = 'stats_'+str(key)
-            axes[label] = fig.add_subplot(\
-                            len(self.frames['stats']['viewkeys']),\
-                            5,\
-                            5*ikey+1,label=label)
-            # Actually, the axes should be a part of the frame!
-            #self.frames['stats']['axes'] = axes[
-
-            # pointer to the axes' point data
-            axes[label].data = {}
-
-            # pointer to the axes' color fields
-            axes[label].fields = {}
-
-
-        fig.tight_layout()
-        fig.subplots_adjust(top=0.95,bottom=0.15,left=0.05,right=0.99,hspace=0.26,wspace=0.08)
-
-        label ='stats_colorbar'
-        axes[label] = fig.add_axes([0.025,0.06,0.18,0.025])
-        axes[label].fields = {}
-
-        from matplotlib.colors import LinearSegmentedColormap
-        cdictpres = {'blue': (\
-                           (0.,    0.,  0.),
-                           (0.25,  0.25, 0.25),
-                           (0.5,  .70, 0.70),
-                           (0.75, 1.0, 1.0),
-                           (1,     1.,  1.),
-                           ),
-               'green': (\
-                           (0. ,   0., 0.0),
-                           (0.25,  0.50, 0.50),
-                           (0.5,  .70, 0.70),
-                           (0.75,  0.50, 0.50),
-                           (1  ,    0,  0.),
-                           ),
-               'red':  (\
-                          (0 ,  1.0, 1.0),
-                          (0.25 ,  1.0, 1.0),
-                           (0.5,  .70, 0.70),
-                          (0.75 , 0.25, 0.25),
-                          (1,    0., 0.),
-                          )}
-        
-        self.statsviewcmap = LinearSegmentedColormap('statsviewcmap', cdictpres)
-
-
-        label = 'times'
-               
-        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-
-
-        label = 'worldmap'
-               
-        axes[label] = fig.add_axes([0.25,0.48,0.40,0.35]) #[*left*, *bottom*, *width*,    *height*]
-        # add pointers to the data of the axes
-        axes[label].data = {}
-        # add pointers to color fields (for maps and colorbars) in the axes
-        axes[label].fields = {}
-        axes[label].lat = None
-        axes[label].lon = None
-
-        label = 'worldmap_colorbar'
-        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
-        axes[label].fields = {}
-
-        # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
-        label = 'worldmap_stations'
-        axes[label] = fig.add_axes([0.25,0.48,0.40001,0.350001]) #[*left*, *bottom*, *width*,    *height*]
-        axes[label].data = {}
-
-        fig.canvas.mpl_connect('pick_event', self.on_pick)
-        fig.canvas.callbacks.connect('motion_notify_event', self.on_plot_hover)
-
-
-        """ buttons definitions """
-        
-        label = 'bprev_dataset'
-        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous dataset')
-        btns[label].on_clicked(self.prev_dataset)
-
-        label = 'bnext_dataset'
-        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next dataset')
-        btns[label].on_clicked(self.next_dataset)
-
-        label = 'bprev_datetime'
-        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous datetime')
-        btns[label].on_clicked(self.prev_datetime)
-
-        label = 'bnext_datetime'
-        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next datetime')
-        btns[label].on_clicked(self.next_datetime)
-
-
-        label = 'bprev_station'
-        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous station')
-        btns[label].on_clicked(self.prev_station)
-
-        label = 'bnext_station'
-        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next station')
-        btns[label].on_clicked(self.next_station)
-
-        label = 'bprev_record'
-        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous record')
-        btns[label].on_clicked(self.prev_record)
-
-        label = 'bnext_record'
-        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next record')
-        btns[label].on_clicked(self.next_record)
-
-
-        # self.nstatsview = nstatsview
-        # self.statsviewcmap = statsviewcmap
-        self.fig = fig
-        self.axes = axes
-        self.btns = btns
-        self.tbox = {}
-        # self.hover_active = False
-
-        #self.tbox['loading'] = fig.text(0.30,0.01, " ",fontsize=10, 
-        #                                transform=plt.gcf().transFigure)
-
-        self.tbox['datetime'] =  fig.text(0.70, 0.96, " ", fontsize=10,
-                                          transform=plt.gcf().transFigure)
-
-        label = 'air_ap:theta'
-        self.axes[label] = fig.add_axes([0.70,0.44,0.12,0.50], label=label)
-
-        label = 'air_ap:q'
-        self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
-
-        label = 'out:h'
-        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
-
-        label = 'out:theta'
-        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
-
-        label = 'out:q'
-        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
-
-        label = 'SEB'
-        self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
-
-
-        self.hover_active = False
-        self.fig = fig
-        self.fig.show()
-        self.fig.canvas.draw()
-        self.refresh_plot_interface()
-
-
-    # def scan_stations(self):
-    #     blabla
-        
-
-
-    # def get_records(current_file):
-    #     records = pd.DataFrame()
-
-    #     # initial position
-    #     next_record_found = False
-    #     while(not next_record_found):
-    #         next_record_found = (current_file.readline() == '---\n')
-    #     next_tell = current_file.tell() 
-    #     end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #     while not end_of_file:
-    #         current_tell = next_tell
-    #         next_record_found = False
-    #         current_file.seek(current_tell)
-    #         while ( (not next_record_found) and (not end_of_file)):
-    #             current_line = current_file.readline()
-    #             next_record_found = (currentline == '---\n')
-    #             end_of_file = (currentline == '') # an empty line means we are at the end
-
-    #         # we store the position of the next record
-    #         next_tell = current_file.tell() 
-    #         
-    #         # we get the current record. Unfortunately we need to reset the
-    #         # yaml record generator first.
-    #         current_yamlgen.close()
-    #         current_yamlgen = yaml.load_all(current_file)
-    #         current_file.seek(current_tell)
-    #         current_record_mod = current_yamlgen.__next__()
-    #     current_yamlgen.close()
-
-    #     return records
-
-       #      next_record_found = False
-       #      while(not record):
-       #          next_record_found = (self.current_file.readline() == '---\n')
-       #      self.current_tell0 = self.current_file.tell() 
-
-       #  
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell0 = self.current_file.tell() 
-
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell1 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell0)
-       #  self.r0 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell1)
-       #  next_record_found = False
-       #  while ( (not next_record_found) and (not end_of_file):
-       #      current_line = self.current_file.readline()
-       #      next_record_found = (currentline == '---\n')
-       #      end_of_file = (currentline == '') # an empty line means we are at the end
-
-       #  self.current_tell2 = self.current_file.tell() 
-
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell1)
-       #  self.r1 = self.current_yamlgen.__next__()
-
-       #  self.current_file.seek(self.current_tell2)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell3 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell2)
-       #  self.r2 = self.current_yamlgen.__next__()
-
-       #  # go to position of next record in file
-       #  self.current_file.seek(self.current_tell3)
-       #  next_record_found = False
-       #  while(not next_record_found):
-       #      next_record_found = (self.current_file.readline() == '---\n')
-       #  self.current_tell4 = self.current_file.tell() 
-
-       #  self.current_yamlgen.close()
-       #  self.current_yamlgen = yaml.load_all(self.current_file)
-       #  self.current_file.seek(self.current_tell3)
-       #  self.r3 = self.current_yamlgen.__next__()
- 
-       #  #self.update_tablestats(SOUNDINGS_TABLESTATS)
-
-    def goto_datetime_worldmap(self,DT,shift=None):
-        DT = np.datetime64(DT) #self.globaldata.datasets[self.axes['worldmap'].focus['key']].variables['time'].values[self.axes['worldmap'].focus['iDT']]
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            self.globaldata.datasets[self.frames['worldmap']['inputkey']].browse_page(time=DT)
-            DIST = np.abs((self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values - DT))
-            self.frames['worldmap']['iDT'] = np.where((DIST) == np.min(DIST))[0][0]
-            if ((shift == 'after') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] < DT)):
-                self.frames['worldmap']['iDT'] += 1
-            elif ((shift == 'before') and (self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']] > DT)):
-                self.frames['worldmap']['iDT'] -= 1 
-            # for gleam, we take the values of the previous day
-            if self.frames['worldmap']['inputkey'] in ['wg','w2']:
-                self.frames['worldmap']['iDT'] -= 2 
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-        #else:
-        #    self.frames['worldmap'].pop('DT')
-
-    def next_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] + 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def prev_datetime(self,event=None):
-        if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
-            # for now we don't go to different files, so we cannot go to
-            # another file 
-            self.frames['worldmap']['iDT'] = (self.frames['worldmap']['iDT'] - 1) % len(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values)
-            self.frames['worldmap']['DT'] = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables['time'].values[self.frames['worldmap']['iDT']]
-            if "fig" in self.__dict__.keys():
-                self.refresh_plot_interface(only='worldmap') 
-
-    def next_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-    def prev_dataset(self,event=None):
-        ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
-        ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
-        self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
-
-
-    def sel_dataset(self,inputkey):
-        self.frames['worldmap']['inputkey'] = inputkey
-        self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
-        self.goto_datetime_worldmap(
-            self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-            'after')# get nearest datetime of the current dataset to the profile
-        if "fig" in self.__dict__.keys():
-            self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
-       
-    # def prev_station(self,event=None):
-    #     self.istation = (self.istation - 1) % self.stations.shape[0]
-    #     self.update_station()
-
-
-
-
-    #def update_datetime(self):
-    #    if 'time' in self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims:
-    #    #if 'time' in list(dict(self.globaldata.datasets[self.worldmapfocus['key']].variables[self.worldmapfocus['key']].dims).keys()):
-    #        #self.worldmapfocus['DT'] = self.globaldata.datasets[self.worldmapfocus['key']].variables['time'].values[self.worldmapfocus['iDT']]
-    #        print(self.worldmapfocus['DT'])
-    #        self.refresh_plot_interface(only='worldmap')
-
-    def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
-
-        #print('r1')
-        for argkey in args.keys():
-            self.__dict__[arg] = args[argkey]
-
-        axes = self.axes
-        tbox = self.tbox
-        frames = self.frames
-        fig = self.fig
- 
-        if (only is None) or ('worldmap' in only):
-            globaldata = self.globaldata
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
-            else:
-                datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
-            keystotranspose = ['lat','lon']
-            for key in dict(datasetxr.dims).keys():
-                if key not in keystotranspose:
-                    keystotranspose.append(key)
-
-            datasetxr = datasetxr.transpose(*keystotranspose)
-            datasetxr = datasetxr.sortby('lat',ascending=False)
-
-            lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
-            lonleft = lonleft - 360.
-            lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
-            label = 'worldmap'
-            axes[label].clear()
-            axes[label].lon = xr.concat([lonleft,lonright],'lon').values
-            axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
-
-        if (only is None) or ('worldmap' in only):
-            #if 'axmap' not in self.__dict__ :
-            #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
-            #else:
-
-            #stations = self.stations
-
-
-            # self.gmap = Basemap(projection='kav7', lat_0 = 0, lon_0 =0,
-            #     resolution = 'l', 
-            # area_thresh = 0.1,
-            #     llcrnrlon=-180., llcrnrlat=-90.0,
-            #     urcrnrlon=180., urcrnrlat=90.0,ax=self.axmap)
-            # 
-            # self.gmap.drawcoastlines(color='white',linewidth=0.3)
-            # self.gmap.drawcountries(color='white',linewidth=0.3)
-            # #self.gmap.fillcontinents(color = 'gray')
-            # self.gmap.drawmapboundary(color='white',linewidth=0.3)
-            # # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),labels=[1,1,0,1])
-            # # self.gmap.drawparallels(np.arange(-90, 90, 30.),labels=[1,0,0,0])
-            # self.gmap.drawmeridians(np.arange(-180, 180+45, 60.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # self.gmap.drawparallels(np.arange(-90, 90, 30.),color='white',linewidth=0.3,labels=[0,0,0,0])
-            # #self.ax5.shadedrelief()
-
-           #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
-
-
-            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
-            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
-
-            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
-            if 'lev' in field.dims:
-                field = field.isel(lev=-1)
-
-            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
-            axes[label].axis('off')
-
-            from matplotlib import cm
-            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
-            
-            
-            title=frames['worldmap']['inputkey']
-            if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
-            axes[label].set_title(title)
-
-            label ='worldmap_colorbar'
-            axes[label].clear()
-            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
-
-
-            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
-            # x,y = self.gmap(lons,lats)
-            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
-            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
-
-        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
-
-            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
-            store_xlim = {}
-            store_ylim = {}
-            for ikey, key in enumerate(statskeys_out):
-                if (only is not None) and ('stats_lightupdate' in only):
-                    store_xlim[key] = axes['stats_'+key].get_xlim()
-                    store_ylim[key] = axes['stats_'+key].get_ylim()
-                self.axes['stats_'+key].clear()    
-
-            label = 'times'
-            self.axes[label].clear()
-
-            key = 'dthetadt'
-            x = self.frames['stats']['records_all_stations_ini']['datetime']
-            #print(x)
-            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-            #print(y)
-            z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            #print(z)
-
-            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
-            self.axes[label].data[label] = self.axes[label].scatter(x.values,
-                                                                    y.values,
-                                                                    c=z.values,
-                                                                    cmap=self.statsviewcmap,
-                                                                    s=2,
-                                                                    vmin=0.,
-                                                                    vmax=1.,
-                                                                    alpha=alpha_cloud_pixels)
-
-            
-            x = self.frames['stats']['records_current_station_ini']['datetime']
-            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-            self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-
-            x = self.frames['profiles']['records_current_station_ini']['datetime']
-            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-            z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-            self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-            self.axes[label].set_xlim((dt.datetime(1981,1,1),dt.datetime(2018,1,1)))
-            self.axes[label].set_ylabel(key+ ' ['+self.units[key]+']')
-
-            for ikey, key in enumerate(statskeys_out):
-
-                # show data of all stations
-                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_all_stations_mod_stats'][key]
-                z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                qvalmax = x.quantile(0.999)
-                qvalmin = x.quantile(0.001)
-                print('applying extra filter over extreme values for plotting stats')
-                selx = (x >= qvalmin) & (x < qvalmax)
-                sely = (x >= qvalmin) & (x < qvalmax)
-                x = x[selx & sely]
-                y = y[selx & sely]
-                z = z[selx & sely]
-                self.axes['stats_'+key].data['stats_'+key] = \
-                       self.axes['stats_'+key].scatter(x,y, c=z,\
-                                cmap=self.statsviewcmap,\
-                                s=3,picker=3,label=key,vmin=0.,vmax=1.,alpha=alpha_cloud_pixels)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
-
-                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_current_station_mod_stats'][key]
-                z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
-
-                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['profiles']['records_current_station_mod_stats'][key]
-                z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
-                self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
-                       self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
-                                cmap=self.statsviewcmap,\
-                                s=20,picker=20,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
-
-                if len(x) > 1:
-                    fit = np.polyfit(x, y, deg=1)
-                    self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
-                         self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
-
-                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
-                y = self.frames['stats']['current_record_mod_stats'][key]
-                z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
-
-                text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
-                self.axes['stats_'+key].data['stats_'+key+'_current_record'] = \
-                    axes['stats_'+key].annotate(text, \
-                                               xy=(x,y),\
-                                               xytext=(0.05,0.05),\
-                                               textcoords='axes fraction',\
-                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
-                                               color='white',\
-                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
-                # self.axes['stats_'+key].data[key+'_current_record'] = \
-                #        self.axes['stats_'+key].scatter(x,y, c=z,\
-                #                 cmap=self.statsviewcmap,\
-                #                 s=30,picker=15,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=1.1)
-
-                # axes['stats_'+key].set_title('relative deviation per station of '+ key)
-                self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
-                # # highlight data for curent station
-                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
-
-                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
-
-                if ikey == len(statskeys_out)-1:
-                    self.axes['stats_'+key].set_xlabel('external')
-                    #axes[label].set_xlabel('ext: '+ key+' ['+statsunits[ikey]+']')
-                axes['stats_'+key].set_ylabel('model')
-
-
-                if (only is not None) and ('stats_lightupdate' in only):
-                    self.axes['stats_'+key].set_xlim(*store_xlim[key])
-                    self.axes['stats_'+key].set_ylim(*store_ylim[key])
-                else:
-                    limlow = np.min((axes['stats_'+key].get_xlim()[0],axes['stats_'+key].get_ylim()[0]))
-                    limhigh = np.max((axes['stats_'+key].get_xlim()[1],axes['stats_'+key].get_ylim()[1]))
-                    self.axes['stats_'+key].set_xlim(limlow,limhigh)
-                    self.axes['stats_'+key].set_ylim(limlow,limhigh)
-                self.abline(1,0,axis=self.axes['stats_'+key])
-
-        if (only is None) or ('stats_colorbar' in only):
-            label ='stats_colorbar'
-            axes[label].clear()
-            import matplotlib as mpl
-            norm = mpl.colors.Normalize(vmin=0.,vmax=1.)
-            self.axes[label].fields[label] = \
-             mpl.colorbar.ColorbarBase(self.axes[label],\
-                        orientation='horizontal',\
-                        label="percentile of "+self.frames['worldmap']['inputkey'],
-                        alpha=1.,
-                                cmap=self.statsviewcmap,\
-                                       norm=norm
-                         )
-
-        #print('r1')
-        if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
-            #print('r2')
-            label = 'worldmap_stations'
-            axes[label].clear()
-            
-            stations = self.frames['worldmap']['stations'].table
-            globaldata = self.globaldata
-            
-            key = label
-
-            #print('r3')
-            if (stations is not None):
-                xlist = []
-                ylist = []
-                #print('r4')
-                for iSTN,STN in frames['worldmap']['stations'].table.iterrows():
-            #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-                    x,y = len(axes['worldmap'].lon)*(STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]) ,len(axes['worldmap'].lat)*(STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    xlist.append(x)
-                    ylist.append(y)
-                #picker is needed to make it clickable (pick_event)
-                axes[label].data[label] = axes[label].scatter(xlist,ylist,
-                                                              c='r', s=15,
-                                                              picker = 15,
-                                                              label=key,
-                                                              edgecolor='k',
-                                                              linewidth=0.8)
-
-            # cb.set_label('Wilting point [kg kg-3]')
-                #print('r5')
-
-                
-            #     xseries = []
-            #     yseries = []
-            #     for iSTN,STN in stations.iterrows():
-            # #        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            # #        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #         x,y = len(axes[label].lon)*(STN['longitude_ext']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(axes[label].lat)*(STN['latitude_ext']- axes[label].axes[label].lat[0])/(axes[label].lat[-1] - axes[label].axes[label].lat[0])
-            #         xseries.append(x)                    
-            #         yseries.append(y)
-            #         
-            #         
-            #     axes[label].data[label] = axes[label].scatter(xseries,yseries, c='r' , s=15, edgecolor='none',label=key)
-                    
-                if ('current_station' in frames['worldmap']):
-                    #print('r5')
-                    STN = frames['stats']['current_station']
-                    STNID = frames['stats']['STNID']
-                    #print('r5')
-
-                    x,y = len(axes['worldmap'].lon)* \
-                            (STN['longitude']- axes['worldmap'].lon[0])/(axes['worldmap'].lon[-1] - axes['worldmap'].lon[0]),\
-                          len(axes['worldmap'].lat)* \
-                            (STN['latitude']- axes['worldmap'].lat[0])/(axes['worldmap'].lat[-1] - axes['worldmap'].lat[0])
-                    #print('r6')
-                    #VAL = self.seltablestats[(self.seltablestats['STNID'] \
-                    #                          == \
-                    #                          self.frames['worldmap']['STNID'])\
-                    #                         & \
-                    #                         (self.seltablestats['DT'] \
-                    #                          == self.axes['statsview0].focus['DT']) \
-                    #                        ][self.axes['worldmap'].focus['key']+'_ext'].iloc[0]
-                    #print('r7')
-                    text = 'STNID: '+ format(STNID,'10.0f') + \
-                            ', LAT: '+format(STN['latitude'],'3.3f')+ \
-                            ', LON: '+format(STN['longitude'],'3.3f')+ \
-                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
-
-                            #+', VAL: '+format(VAL,'.3e')
-
-                    axes[label].scatter(x,y, c='r', s=30,\
-                                        edgecolor='k',picker=30,label=key,linewidth=1.1)
-                    #print('r8')
-            
-                    #colorrange = list(axes[label].fields['worldmap'].get_clim())
-                    #colorstation = (VAL-colorrange[0])/(colorrange[1]-colorrange[0])
-                    #colorstation = max((min((1.,colorstation)),0.))
-                    colorstation =0.2
-                    from matplotlib import cm
-                    axes[label].annotate(text,
-                                         xy=(x,y),
-                                         xytext=(0.05,0.05),
-                                         textcoords='axes fraction', 
-                                         bbox=dict(boxstyle="round",
-                                         fc = cm.viridis(colorstation)),
-                                         arrowprops=dict(arrowstyle="->",
-                                                         linewidth=1.1),
-                                         color='white' if colorstation < 0.5 else 'black')
-                    #print('r9')
-
-                    # #pos = sc.get_offsets()[ind["ind"][0]]
-                    # 
-                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
-                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
-                    # axes[label].data[label+'statannotate'].set_text(text)
-                    #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
-                    # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
-            #print('r9')
-            axes[label].axis('off')
-            axes[label].set_xlim(0,(len(axes['worldmap'].lon)))
-            axes[label].set_ylim((len(axes['worldmap'].lat),0))
-            #print('r10')
-
-        if (only is None) or ('profiles' in only): 
-            #print('r11')
-
-            # # self.istation = np.where(self.stations['ID'] == STNID)[0][0]
-            # # self.update_station(goto_first_sounding=False)
-            # isounding = np.where(pd.DatetimeIndex(self.df_soundings_eval_pairs.datetime) == self.profilefocus['DT'])[0][0]
-            # #self.isounding = (self.isounding - 1) % self.df_soundings_eval_pairs.shape[0]
-            # self.morning_sounding = self.df_soundings_eval_pairs.loc[isounding]
-            # self.evening_sounding = self.df_soundings.loc[self.morning_sounding['eval0']]
-
-            label = 'air_ap:theta'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-                # +\
-                # ' -> '+ \
-                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
-            
-            
-            
-            
-            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-            #print('r12')
-
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            #print(self.frames['profiles']['record_yaml_ini'].pars.h)
-            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
-            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
-            hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            #print('r13')
-            # 
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r14')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-
-            #print('r15')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-                          
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r16')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r17')
-            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
-            print(hmax)
-            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
-            if valid_mod:
-
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="mod "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                                 +'LT')
-
-            #print('r18')
-            axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('theta [K]')
-
-            label = 'air_ap:q'
-            axes[label].clear()
-
-            tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
-            # 
-
-            #print('r19')
-            # #axes[label].set_title(self.morning_sounding.ldatetime.strftime("local time:  %H:%M")+' -> '+self.evening_sounding.ldatetime.strftime("%H:%M"))
-            # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # 
-            if valid_mod:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            else:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            # 
-            #print('r20')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r21')
-
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_ap.z.values[zco],"b:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
-
-
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
-
-            #print('r23')
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-
-            #print('r24')
-            if valid_mod:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
-                zco = range(zidxmax)
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
-                                 label="fit ")#+\
-                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
-                             #+'LT')
-            #print('r25')
-            #axes[label].legend()
-
-            #axes[label].legend(prop={'family':'monospace'},loc='upper left')
-            #axes[label].set_ylabel('height [m]')
-            axes[label].set_xlabel('q [kg/kg]')
-
-            # #axes[label].set_title(self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.theta_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.theta_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.theta_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod "+self.evening_sounding.ldatetime.strftime("%H:%M")+'LT')
-
-            # #pl.subplots_adjust(right=0.6)
-
-            # label = 'q_pro'
-            # axes[label].clear()
-
-            # hmax = np.max([self.morning_sounding.c4gl.input.h,self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.h])
-            # 
-            # zco =  self.morning_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.obs.q_pro[zco], self.morning_sounding.obs.z_pro[zco],"b*",label="obs")
-            # 
-            # zco =  self.morning_sounding.c4gl.input.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.input.q_pro[zco], self.morning_sounding.c4gl.input.z_pro[zco ],"b:",label="fit")
-
-            # #self.ax5.set_title(self.evening_sounding.ldatetime.strftime("local time: %H:%M"))
-            # zco =  self.evening_sounding.obs.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.obs.q_pro[zco], self.evening_sounding.obs.z_pro[zco],"r*",label="obs")
-            # 
-            # zco =  self.evening_sounding.fit.z_pro < 2.*hmax
-            # axes[label].plot(self.evening_sounding.fit.q_pro[zco], self.evening_sounding.fit.z_pro[zco],"r:",label="fit")
-            # 
-            # zco = self.morning_sounding.c4gl.z_pro < 2.*hmax
-            # axes[label].plot(self.morning_sounding.c4gl.q_pro[zco], self.morning_sounding.c4gl.z_pro[zco],"r-",label="mod")
-            # #pl.subplots_adjust(right=0.6)
-            # axes[label].set_xlabel('specific humidity [kg/kg]')
- 
-
-            #print('r26')
-            time = self.frames['profiles']['record_yaml_mod'].out.time
-            for ilabel,label in enumerate(['h','theta','q']):
-                axes["out:"+label].clear()
-                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
-                axes["out:"+label].set_ylabel(label)
-                if ilabel == 2:
-                    axes["out:"+label].set_xlabel('local sun time [h]')
-                
-            #print('r27')
-            label = 'SEB'
-            axes[label].clear()
-            
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
-            axes[label].hlines(0.,*axes[label].get_xlim(),'k')
-            axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
-            axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
-                
-            #print('r28')
-            
-            axes[label].legend()
-            
-            #         for ax in self.fig_timeseries_axes:
-#             ax.clear()
-#         
-#         self.fig_timeseries_axes[0].plot(self.morning_sounding.c4gl.out.h,label='h')
-#         self.fig_timeseries_axes[1].plot(self.morning_sounding.c4gl.out.theta,label='theta')
-#         self.fig_timeseries_axes[2].plot(self.morning_sounding.c4gl.out.q,label='q')
-#         #print(self.morning_sounding.c4gl.out.Swin)
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Swin - self.morning_sounding.c4gl.out.Swout,label='Sw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.H,label='H')
-#         self.fig_timeseries_axes[3].plot(self.morning_sounding.c4gl.out.Lwin - self.morning_sounding.c4gl.out.Lwout,label='Lw')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.G,label='G')
-#         self.fig_timeseries_axes[3].plot(-self.morning_sounding.c4gl.out.LE,label='LE')
-#         self.fig_timeseries_axes[3].hlines(0.,*self.fig_timeseries_axes[3].get_xlim(),'k')
-#         self.fig_timeseries_axes[3].legend()
-#         self.fig.canvas.draw()
-            
-
-
-
-
-
-
-        #self.ready()
-        #print('r29')
-        fig.canvas.draw()
-        #fig.show()
-
-        self.axes = axes
-        self.tbox = tbox
-        self.fig = fig
-
-    def on_pick(self,event):
-        #print("HELLO")
-        # this makes clear that the dataset is loading (set_profile_focus takes a long time to load!)
-        #self.axes['theta_pro'].clear()
-        #self.axes['q_pro'].clear()
-        
-
-        # workaround because I cannot track the axes label here. I need it because the behaviour of this function should depend on which axes we are.
-        # I can only track the label of the data points. So we make a definition that clarifies to which axes the select data points (having a 'key') belongs to
-        keys_to_axes = {}
-        for ikey,key in enumerate(self.frames['stats']['viewkeys']):
-            keys_to_axes['d'+self.frames['stats']['viewkeys'][ikey]+'dt'] = 'stats_d'+key+'dt'
-
-        keys_to_axes['worldmap_stations'] = 'worldmap_stations'
-        keys_to_axes['worldmap'] = 'worldmap'
-        
-        axes = self.axes
-        #nstatsview = self.nstatsview
-        #statsviewcmap = self.statsviewcmap
-        stations = self.frames['worldmap']['stations'].table
-
-
-        #print("p1")
-        current = event
-        artist = event.artist
-        
-        selkey = artist.get_label()
-        
-        #print(keys_to_axes)
-        
-        label = keys_to_axes[selkey]
-        #print("HELLO",selkey,label)
-
-        # # Get to know in which axes we are
-        # label = None
-        # for axeskey in axes.keys():
-        #     if event.inaxes == axes[axeskey]:
-        #         label = axeskey
-        #         
-
-        # cont, pos = None, None
-        
-        xmouse, ymouse = event.mouseevent.xdata, event.mouseevent.ydata
-        ind = event.ind
-        # x, y = artist.get_xdata(), artist.get_ydata() # for some reason this doesnt work yet :/
-        d = axes[label].collections[0]
-        #d.set_offset_position('data')
-        xy = d.get_offsets()
-        x, y =  xy[:,0],xy[:,1]
-        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
-
-        #print("p2")
-        if len(ind) > 0:
-            #print("p3")
-            pos = x[ind[0]], y[ind[0]]
-
-            #if label[:-1] == 'statsview':
-            #    #seltablestatsstdrel = self.seltablestatsstdrel
-            #    #seltablestatspct = self.seltablestatspct
-
-            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-            #    
-            #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-            #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
-            #    self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-            #    
-            #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap','profiles'],statsnewdata=False)
-            #el
-            if (label == 'worldmap') or (label == 'worldmap_stations'):
-                self.hover_active = False
-                if (self.frames['worldmap']['STNID'] !=
-                    self.frames['profiles']['STNID']):
-                # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
-                # so we just need to perform update_station
-                    self.update_station()
-            elif (label[:5] == 'stats'):
-
-                self.hover_active = False
-                if (self.frames['stats']['STNID'] !=
-                self.frames['profiles']['STNID']) or \
-                   (self.frames['stats']['current_record_chunk'] != 
-                    self.frames['profiles']['current_record_chunk']) or \
-                   (self.frames['stats']['current_record_index'] != 
-                    self.frames['profiles']['current_record_index']):
-
-
-
-                    for key in ['STNID','current_station','stations_iterator']: 
-                        self.frames['worldmap'][key] = self.frames['stats'][key] 
-
-                    for key in self.frames['stats'].keys():
-                        self.frames['profiles'][key] = self.frames['stats'][key]
-
-                    STNID = self.frames['profiles']['STNID']
-                    chunk = self.frames['profiles']['current_record_chunk']
-                    if 'current_station_file_ini' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_ini'].close()
-                    self.frames['profiles']['current_station_file_ini'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
-
-                    if 'current_station_file_mod' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_mod'].close()
-                    self.frames['profiles']['current_station_file_mod'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_afternoon'].close()
-                    self.frames['profiles']['current_station_file_afternoon'] = \
-                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
-
-                    # go to hovered record of current station
-                    self.frames['profiles']['records_iterator'] = \
-                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # ... and go to the record of the profile window (last one that
-                    # was picked by the user)
-                    found = False
-                    EOF = False
-                    while (not found) and (not EOF):
-                        try:
-                            (STNID,chunk,index),record = self.frames['profiles']['records_iterator'].__next__()
-                            #print("hello*")
-                            #print(self.frames['profiles']['current_record_index'])
-                            if (chunk == self.frames['profiles']['current_record_chunk']) and \
-                               (index == self.frames['profiles']['current_record_index']) and \
-                               (STNID == self.frames['profiles']['STNID']):
-                                #print('found!')
-                                found = True
-                        except StopIteration:
-                            EOF = True
-                    if found:
-                        self.frames['stats']['current_record_mod'] = record
-                        self.frames['stats']['current_record_chunk'] = chunk
-                        self.frames['stats']['current_record_index'] = index
-                    # # for the profiles we make a distinct record iterator, so that the
-                    # # stats iterator can move independently
-                    # self.frames['profiles']['records_iterator'] = \
-                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
-                    # (self.frames['profiles']['STNID'] , \
-                    # self.frames['profiles']['current_record_index']) , \
-                    # self.frames['profiles']['current_record_mod'] = \
-                    #                 self.frames['profiles']['records_iterator'].__next__()
-
-
-                    # for the profiles we make a distinct record iterator, so that the
-                    # stats iterator can move independently
-
-                    self.update_record()
-
-
-
-    def on_plot_hover(self,event):
-        axes = self.axes
-        #print('h1')
-
-        # Get to know in which axes we are
-        label = None
-        for axeskey in axes.keys():
-            if event.inaxes == axes[axeskey]:
-                label = axeskey
-                
-        #print('h2')
-
-        cont, pos = None, None
-        #print (label)
-        
-        if label is not None:
-            if  ('data' in axes[label].__dict__.keys()) and \
-                (label in axes[label].data.keys()) and \
-                (axes[label].data[label] is not None):
-                
-                #print('h3')
-                cont, ind =  axes[label].data[label].contains(event)
-                selkey = axes[label].data[label].get_label()
-                if len(ind["ind"]) > 0:
-                    #print('h4')
-                    pos = axes[label].data[label].get_offsets()[ind["ind"][0]]
-                    #print('pos',pos,selkey)
-
-
-                    #if label[:-1] == 'statsview':
-                    #    seltablestatsstdrel = self.seltablestatsstdrel
-                    #    seltablestatspct = self.seltablestatspct
-
-                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
-                    #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
-                    #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
-                    #    self.hover_active = True
-                    #    
-                    #    self.refresh_plot_interface(only=['statsviews_lightupdate','worldmap_stations'])
-                    #    
-                    #el
-                    #print(label[:5])
-                    if (label[:5] == 'stats') or (label == 'times'):
-                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
-                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
-                        
-
-                        if label[:5] == 'stats':
-                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                            (self.frames['stats']['STNID'] ,
-                             self.frames['stats']['current_record_chunk'], 
-                             self.frames['stats']['current_record_index']) = \
-                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-                        # elif label[:5] == 'stats':
-                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
-                        #     (self.frames['stats']['STNID'] ,
-                        #      self.frames['stats']['current_record_chunk'], 
-                        #      self.frames['stats']['current_record_index']) = \
-                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-
-
-                        self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-                        
-                        # # TO TEST: should be removed, since it's is also done just below
-                        # self.frames['stats']['stations_iterator'] = \
-                        #     self.frames['worldmap']['stations_iterator'] 
-                
-                
-                        # self.goto_datetime_worldmap(
-                        #     self.frames['profiles']['current_record_obs'].datetime.to_pydatetime(),
-                        #     'after')
-
-
-                        # scrolling to the right station
-                        STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                        EOF = False
-                        found = False
-                        while (not found and not EOF):
-                            if (STNID == self.frames['stats']['STNID']):
-                                   found = True 
-                            if not found:
-                                try:
-                                    STNID,station = self.frames['stats']['stations_iterator'].__next__()
-                                except (StopIteration):
-                                    EOF = True
-                        if found:
-                        #    self.frames['stats']['STNID'] = STNID
-                            self.frames['stats']['current_station'] =  station
-
-                        #STNID = self.frames['profiles']['current_record_index'].iloc[0].name[0]
-                        #index = self.frames['profiles']['current_record_index'].iloc[0].name[1]
-
-
-                        # generate index of the current station
-                        self.frames['stats']['records_current_station_index'] = \
-                            (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                             == self.frames['stats']['STNID'])
-
-
-                        tab_suffixes = \
-                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            self.frames['stats']['records_current_station'+tab_suffix] = \
-                                self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-
-                        # go to hovered record of current station
-                        self.frames['stats']['records_iterator'] = \
-                                        records_iterator(self.frames['stats']['records_current_station_mod'])
-
-
-                        # ... and go to the record of the profile window (last one that
-                        # was picked by the user)
-                        found = False
-                        EOF = False
-                        while (not found) and (not EOF):
-                            try:
-                                (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                                #print("hello*")
-                                #print(self.frames['profiles']['current_record_index'])
-                                if (index == self.frames['stats']['current_record_index']) and \
-                                   (chunk == self.frames['stats']['current_record_chunk']) and \
-                                   (STNID == self.frames['stats']['STNID']):
-                                    #print('found!')
-                                    found = True
-                            except StopIteration:
-                                EOF = True
-                        if found:
-                            #print('h5')
-                            self.frames['stats']['current_record_mod'] = record
-                            self.frames['stats']['current_record_chunk'] = chunk
-                            self.frames['stats']['current_record_index'] = index
-
-                        #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
-                        tab_suffixes = \
-                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                        for tab_suffix in tab_suffixes:
-                            #print(tab_suffix)
-                            #print(self.frames['stats']['records_current_station'+tab_suffix])
-                            self.frames['stats']['current_record'+tab_suffix] =  \
-                                self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                      (self.frames['stats']['STNID'] , \
-                                       self.frames['stats']['current_record_chunk'] , \
-                                       self.frames['stats']['current_record_index'])]
-
-
-                        self.hover_active = True
-                        self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                        # print('h13')
-                        # if 'time' in self.globaldata.datasets[key].page[key].dims:
-                        #     self.goto_datetime_worldmap(
-                        #         self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
-                        #         'after')
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap',
-                        #                                           'profiles'])
-                        # else:
-                        #     if "fig" in self.__dict__.keys():
-                        #         self.refresh_plot_interface(only=['stats_lightupdate',
-                        #                                           'worldmap_stations',
-                        #                                           'profiles'])
-
-
-
-                    elif label in ['worldmap_stations','worldmap']:
-                        #print('h5')
-
-                        if (self.axes['worldmap'].lat is not None) and \
-                           (self.axes['worldmap'].lon is not None):
-
-
-                            #self.loading()
-                            self.fig.canvas.draw()
-                            self.fig.show()
-
-
-                            # get position of 
-                            latmap = round(pos[1]/len(self.axes['worldmap'].lat)*(self.axes['worldmap'].lat[-1] - \
-                                                                 self.axes['worldmap'].lat[0]) + \
-                                           self.axes['worldmap'].lat[0],4)
-                            lonmap = round(pos[0]/len(self.axes['worldmap'].lon)*(self.axes['worldmap'].lon[-1] - \
-                                                                 self.axes['worldmap'].lon[0]) + \
-                                           self.axes['worldmap'].lon[0],4)
-                        
-                            stations = self.frames['worldmap']['stations'].table
-                            #print('h7')
-                        
-                            #reset stations iterator:
-                            # if 'stations_iterator' in self.frames['worldmap'].keys():
-                            #     self.frames['worldmap']['stations_iterator'].close()
-                            #     del(self.frames['worldmap']['stations_iterator'])
-                            # if 'stations_iterator' in self.frames['stats'].keys():
-                            #     self.frames['stats']['stations_iterator'].close()
-                            #     del(self.frames['stats']['stations_iterator'])
-                            self.frames['worldmap']['stations_iterator'] =\
-                               stations_iterator(self.frames['worldmap']['stations'])
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                            EOF = False
-                            found = False
-                            while (not found and not EOF):
-                                #print('h8',station.latitude,latmap)
-                                #print('h8',station.longitude,lonmap)
-                                if (round(station.latitude,3) == round(latmap,3)) and \
-                                    (round(station.longitude,3) == round(lonmap,3)):
-                                       found = True 
-                                if not found:
-                                    try:
-                                        STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                                    except (StopIteration):
-                                        EOF = True
-                            if found:
-                                self.frames['worldmap']['STNID'] = STNID
-                                self.frames['worldmap']['current_station'] = \
-                                        station
-                        
-                            self.frames['stats']['stations_iterator'] = \
-                                self.frames['worldmap']['stations_iterator'] 
-                            #print('h8')
-                            # inherit station position for the stats frame...
-                            for key in self.frames['worldmap'].keys():
-                                self.frames['stats'][key] = self.frames['worldmap'][key]
-                                
-                            ## fetch records of current station...
-                            #self.frames['stats']['records_current_station_mod'] =\
-                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                            # ... and their indices
-                            self.frames['stats']['records_current_station_index'] = \
-                                    (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                                     == \
-                                     self.frames['stats']['current_station'].name)
-
-
-                            tab_suffixes = \
-                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['records_current_station'+tab_suffix] = \
-                                    self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-
-                            # ... create a record iterator ...
-                            #self.frames['stats']['records_iterator'].close()
-                            del(self.frames['stats']['records_iterator'])
-                            self.frames['stats']['records_iterator'] = \
-                                self.frames['stats']['records_current_station_mod'].iterrows()
-
-
-
-                        
-                            #print('h9')
-                            # ... and go to to the first record of the current station
-                            (self.frames['stats']['STNID'] , \
-                             self.frames['stats']['current_record_chunk'] , \
-                             self.frames['stats']['current_record_index']) , \
-                            self.frames['stats']['current_record_mod'] = \
-                                self.frames['stats']['records_iterator'].__next__()
-                        
-
-
-
-                            #print('h10')
-                            # cash the current record
-                            tab_suffixes = \
-                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                            for tab_suffix in tab_suffixes:
-                                self.frames['stats']['current_record'+tab_suffix] =  \
-                                    self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                                          (self.frames['stats']['STNID'] , \
-                                           self.frames['stats']['current_record_chunk'] , \
-                                           self.frames['stats']['current_record_index'])]
-
-                            #print('h11')
-                            
-                            self.hover_active = True
-                            self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations','profiles'])
-                            #print('h13')
-
-                        
-
-            #if (stations is not None):
-            #    for iSTN,STN in stations.iterrows():
-            ##        x,y =self.gmap(STN['longitude'],STN['latitude'])
-            ##        self.gmap.plot(x,y, 'mo' if (self.STNID == STN['ID']) else 'ro' , markersize=1)
-            #        x,y = len(axes[label].lon)*(STN['longitude']- axes[label].lon[0])/(axes[label].lon[-1] - axes[label].lon[0])  ,len(lat)*(STN['latitude']- axes[label].lat[0])/(lat[-1] - axes[label].lat[0])
-            #        axes['worldmap'].plot(x,y, 'mo' if (axes['worldmap'].focus['STNID'] == STN['ID']) else 'ro' , markersize=2)
-
-        # self.fig.show()
- 
-        # we are hovering on nothing, so we are going back to the position of
-        # the profile sounding
-        if pos is None:
-            if self.hover_active == True:
-                #print('h1*')
-                
-                #self.loading()
-                # to do: reset stations iterators
-
-                # get station and record index from the current profile
-                for key in ['STNID', 'current_station']:
-                    self.frames['stats'][key] = self.frames['profiles'][key]
-
-                self.frames['stats']['STNID'] = self.frames['profiles']['STNID']
-                self.frames['stats']['current_station'] = \
-                        self.frames['profiles']['current_station']
-                #print('h3a*')
-                self.frames['stats']['records_current_station_mod'] = \
-                        self.frames['profiles']['records_current_station_mod']
-                #print('h3b*')
-
-                # the next lines recreate the records iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-
-                # reset stations iterator...
-                #self.frames['stats']['records_iterator'].close()
-                del(self.frames['stats']['records_iterator'])
-                self.frames['stats']['records_iterator'] = \
-                    self.frames['stats']['records_current_station_mod'].iterrows()
-                #print('h4*')
-
-                # ... and go to the record of the profile window (last one that
-                # was picked by the user)
-                found = False
-                EOF = False
-                while (not found) and (not EOF):
-                    try:
-                        (STNID,chunk,index),record = self.frames['stats']['records_iterator'].__next__()
-                        #print("hello*")
-                        #print(self.frames['profiles']['current_record_index'])
-                        #print(self.frames['profiles']['STNID'])
-                        #print(STNID,index)
-                        if (index == self.frames['profiles']['current_record_index']) and \
-                            (chunk == self.frames['profiles']['current_record_chunk']) and \
-                            (STNID == self.frames['profiles']['STNID']):
-                            #print('found!')
-                            found = True
-                    except StopIteration:
-                        EOF = True
-                if found:
-                    #print('h5*')
-                    self.frames['stats']['current_record_mod'] = record
-                    self.frames['stats']['current_record_chunk'] = chunk
-                    self.frames['stats']['current_record_index'] = index
-
-                #print('h6*')
-
-
-
-                # # fetch records of current station...
-                # self.frames['stats']['records_current_station_mod'] =\
-                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
-
-                # ... and their indices
-                self.frames['stats']['records_current_station_index'] = \
-                        (self.frames['stats']['records_all_stations_index'].get_level_values('STNID')\
-                         == \
-                         self.frames['stats']['current_station'].name)
-
-
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['records_current_station'+tab_suffix] = \
-                        self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
-
-                
-
-                # cash the records of the current stations
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
-                for tab_suffix in tab_suffixes:
-                    self.frames['stats']['current_record'+tab_suffix] =  \
-                        self.frames['stats']['records_current_station'+tab_suffix].loc[\
-                              (self.frames['stats']['STNID'] , \
-                               self.frames['stats']['current_record_chunk'] , \
-                               self.frames['stats']['current_record_index'])]
-
-
-                # the next lines recreate the stations iterator. Probably it's
-                # better to just copy the profile iterator and its position to
-                # the worldmap/stats 
-                #print('h7*')
-
-                # reset the stations iterators
-                for framekey in ['stats','worldmap']:
-                    ##print(framekey)
-                    if 'stations_iterator' in self.frames[framekey]:
-                        #self.frames[framekey]['stations_iterator'].close()
-                        del(self.frames[framekey]['stations_iterator'])
-
-                self.frames['worldmap']['current_station'] = \
-                        self.frames['profiles']['current_station']
-
-                #recreate the stations iterator for the worldmap...
-                self.frames['worldmap']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
-
-                # ... and go the position of the profile
-                #print('h8*')
-                STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                EOF = False
-                found = False
-                while (not found and not EOF):
-                    if STNID == self.frames['profiles']['STNID'] :
-                        found = True 
-                    if not found:
-                        try:
-                            STNID,station = self.frames['worldmap']['stations_iterator'].__next__()
-                        except (StopIteration):
-                            EOF = True
-                if found:
-                    self.frames['worldmap']['current_station'] = station
-                    self.frames['worldmap']['STNID'] = STNID
-                #print('h9*')
-                self.frames['stats']['stations_iterator'] = \
-                    self.frames['worldmap']['stations_iterator'] 
-
-                # the stats window now inherits the current station from the
-                # worldmap
-                for key in ['STNID','current_station','stations_iterator']: 
-                    self.frames['stats'][key] = self.frames['worldmap'][key] 
-                #print('h10*')
-
-                # # we now only need inherit station position and go to first record
-                # for key in self.frames['worldmap'].keys():
-                #     self.frames['stats'][key] = self.frames['worldmap'][key]
-
-                # self.frames['stats']['records_current_station'] =\
-                #     get_records(pd.DataFrame().append(self.frames['stats']['current_station']))
-
-                # #print(self.frames['stats']['records_current_station'])
-                # self.frames['stats']['records_iterator'] = \
-                #                 self.frames['stats']['records_current_station'].iterrows()
-                # (self.frames['stats']['STNID'] , \
-                # self.frames['stats']['current_record_index']) , \
-                # self.frames['stats']['current_record_mod'] = \
-                #                 self.frames['stats']['records_iterator'].__next__()
-                
-
-
-
-
-
-
-                #self.set_statsviewfocus('STNID', self.profilefocus['STNID'])
-                ##self.set_statsviewfocus('DT'], self.profilefocus['DT'])
-                #self.axes['worldmap'].focus['STNID'] = self.profilefocus['STNID']
-                ##self.goto_datetime_worldmap(self.profilefocus['DT'],'after')
-                self.hover_active = False
-                self.refresh_plot_interface(only=['stats_lightupdate','worldmap_stations'],statsnewdata=False)
-    # def loading(self):
-    #     self.tbox['loading'].set_text('Loading...')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-    #     sleep(0.1)
-    # def ready(self):
-    #     self.tbox['loading'].set_text('Ready')
-    #     self.fig.canvas.draw()
-    #     self.fig.show()
-
-
-
diff --git a/dist/class4gl-0.1dev/lib/model.py b/dist/class4gl-0.1dev/lib/model.py
deleted file mode 100644
index 8760411..0000000
--- a/dist/class4gl-0.1dev/lib/model.py
+++ /dev/null
@@ -1,2214 +0,0 @@
-# 
-# CLASS
-# Copyright (c) 2010-2015 Meteorology and Air Quality section, Wageningen University and Research centre
-# Copyright (c) 2011-2015 Jordi Vila-Guerau de Arellano
-# Copyright (c) 2011-2015 Chiel van Heerwaarden
-# Copyright (c) 2011-2015 Bart van Stratum
-# Copyright (c) 2011-2015 Kees van den Dries
-# 
-# This file is part of CLASS
-# 
-# CLASS is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published bygamma
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-# 
-# CLASS is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-# 
-# You should have received a copy of the GNU General Public License
-# along with CLASS.  If not, see .
-#
-
-import copy as cp
-import numpy as np
-import sys
-import warnings
-import pandas as pd
-from ribtol_hw import zeta_hs2 , funcsche
-import logging
-#from SkewT.thermodynamics import Density
-#import ribtol
-
-grav = 9.81
-def esat(T):
-    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
-
-def qsat(T,p):
-    return 0.622 * esat(T) / p
-
-
-def ribtol(Rib, zsl, z0m, z0h): 
-    Rib = np.float64(Rib)
-    zsl = np.float64(zsl)
-    z0m = np.float64(z0m)
-    z0h = np.float64(z0h)
-    #print(Rib,zsl,z0m,z0h)
-    if(Rib > 0.):
-        L    = 1.
-        L0   = 2.
-    else:
-        L  = -1.
-        L0 = -2.
-    #print(Rib,zsl,z0m,z0h)
-    while (abs(L - L0) > 0.001):
-        L0      = L
-        fx      = Rib - zsl / L * (np.log(zsl / z0h) - psih(zsl / L) + psih(z0h / L)) / (np.log(zsl / z0m) - psim(zsl / L) + psim(z0m / L))**2.
-        Lstart  = L - 0.001*L
-        Lend    = L + 0.001*L
-        fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - psih(zsl / Lstart) + psih(z0h / Lstart)) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lstart) + psim(z0m / Lstart))**2.) \
-                  - (-zsl /  Lend   * (np.log(zsl / z0h) - psih(zsl / Lend  ) + psih(z0h / Lend  )) / \
-                                      (np.log(zsl / z0m) - psim(zsl / Lend  ) + psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-        L       = L - fx / fxdif
-        #print(L,fx/fxdif)
-        if(abs(L) > 1e12):
-            break
-
-    return L
-  
-def psim(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-        #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psim = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-    return psim
-  
-def psih(zeta):
-    if(zeta <= 0):
-        x     = (1. - 16. * zeta)**(0.25)
-        psih  = 2. * np.log( (1. + x*x) / 2.)
-        #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-        #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-    else:
-        psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-    return psih
- 
-class model:
-    def __init__(self, model_input = None,debug_level=None):
-
-        """ set up logger (see: https://docs.python.org/2/howto/logging.html)
-        """
-
-        self.logger = logging.getLogger('model')
-        if debug_level is not None:
-            self.logger.setLevel(debug_level)
-
-        """ initialize the different components of the model """ 
-
-        if model_input is not None:
-            # class4gl style input
-            if 'pars' in model_input.__dict__.keys():
-
-                # we make a reference to the full input first, so we can dump it
-                # afterwards
-                self.input_c4gl = model_input
-
-                # we copy the regular parameters first. We keep the classical input
-                # format as self.input so that we don't have to change the entire
-                # model code.
-                self.input = cp.deepcopy(model_input.pars)
-
-                # we copy other sections we are interested in, such as profile
-                # data, and store it also under input
-
-                # I know we mess up a bit the structure of the class4gl_input, but
-                # we will make it clean again at the time of dumping data
-
-                # So here, we copy the profile data into self.input
-                # 1. Air circulation data 
-                if 'sw_ac' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ac']:
-                    self.input.__dict__['air_ac'] = model_input.__dict__['air_ac']
-                    #self.input.__dict__['air_ach'] = model_input.__dict__['air_ach']
-
-                    # correct pressure of levels according to surface pressure
-                    # error (so that interpolation is done in a consistent way)
-
-                    p_e = self.input.Ps - self.input.sp
-                    for irow in self.input.air_ac.index[::-1]:
-                       self.input.air_ac.p.iloc[irow] =\
-                        self.input.air_ac.p.iloc[irow] + p_e
-                       p_e = p_e -\
-                       (self.input.air_ac.p.iloc[irow]+p_e)/\
-                        self.input.air_ac.p.iloc[irow] *\
-                        self.input.air_ac.delpdgrav.iloc[irow]*grav
-
-
-
-                # 2. Air circulation data 
-                if 'sw_ap' in self.input.__dict__.keys() \
-                   and self.input.__dict__['sw_ap']:
-                    self.input.__dict__['air_ap'] = model_input.__dict__['air_ap']
-
-            # standard class input
-            else:
-                self.input = cp.deepcopy(model_input)
-
-    def load_yaml_dict(self,yaml_dict):
-        for key,data in yaml_dict.items():
-            if key == 'pars':
-                for keydata,value in data.items():
-                    self.__dict__[keydata] = value
-            elif key in ['air_ap','air_balloon','air_ac','air_ach']:
-                self.__dict__[key] = pd.DataFrame(data)
-            #elif key == 'sources':
-            #    self.__dict__[key] = data
-            elif key == 'out':
-                # lets convert it to a list of dictionaries
-                dictouttemp = pd.DataFrame(data).to_dict('list')
-            else: 
-                 warnings.warn("Key '"+key+"' is be implemented.")
-            #     self.__dict__[key] = data
-
-
-        self.tsteps = len(dictouttemp['h'])
-        self.out = model_output(self.tsteps)
-        for keydictouttemp in dictouttemp.keys():
-            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
-
-
-  
-    def run(self):
-        # initialize model variables
-        self.init()
-  
-        # time integrate model 
-        #for self.t in range(self.tsteps):
-        while self.t < self.tsteps:
-          
-            # time integrate components
-            self.timestep()
-  
-        # delete unnecessary variables from memory
-        self.exitmodel()
-    
-    def init(self):
-        # assign variables from input data
-        # initialize constants
-        self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
-        self.cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-        self.rho        = 1.2                   # density of air [kg m-3]
-        self.k          = 0.4                   # Von Karman constant [-]
-        self.g          = 9.81                  # gravity acceleration [m s-2]
-        self.Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-        self.Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-        self.bolz       = 5.67e-8               # Bolzman constant [-]
-        self.rhow       = 1000.                 # density of water [kg m-3]
-        self.S0         = 1368.                 # solar constant [W m-2]
-
-        # A-Gs constants and settings
-        # Plant type:       -C3-     -C4-
-        self.CO2comp298 =  [68.5,    4.3    ]   # CO2 compensation concentration [mg m-3]
-        self.Q10CO2     =  [1.5,     1.5    ]   # function parameter to calculate CO2 compensation concentration [-]
-        self.gm298      =  [7.0,     17.5   ]   # mesophyill conductance at 298 K [mm s-1]
-        self.Ammax298   =  [2.2,     1.7    ]   # CO2 maximal primary productivity [mg m-2 s-1]
-        self.Q10gm      =  [2.0,     2.0    ]   # function parameter to calculate mesophyll conductance [-]
-        self.T1gm       =  [278.,    286.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.T2gm       =  [301.,    309.   ]   # reference temperature to calculate mesophyll conductance gm [K]
-        self.Q10Am      =  [2.0,     2.0    ]   # function parameter to calculate maximal primary profuctivity Ammax
-        self.T1Am       =  [281.,    286.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.T2Am       =  [311.,    311.   ]   # reference temperature to calculate maximal primary profuctivity Ammax [K]
-        self.f0         =  [0.89,    0.85   ]   # maximum value Cfrac [-]
-        self.ad         =  [0.07,    0.15   ]   # regression coefficient to calculate Cfrac [kPa-1]
-        self.alpha0     =  [0.017,   0.014  ]   # initial low light conditions [mg J-1]
-        self.Kx         =  [0.7,     0.7    ]   # extinction coefficient PAR [-]
-        self.gmin       =  [0.25e-3, 0.25e-3]   # cuticular (minimum) conductance [mm s-1]
-
-        self.mco2       =  44.;                 # molecular weight CO2 [g mol -1]
-        self.mair       =  28.9;                # molecular weight air [g mol -1]
-        self.nuco2q     =  1.6;                 # ratio molecular viscosity water to carbon dioxide
-
-        self.Cw         =  0.0016;              # constant water stress correction (eq. 13 Jacobs et al. 2007) [-]
-        self.wmax       =  0.55;                # upper reference value soil water [-]
-        self.wmin       =  0.005;               # lower reference value soil water [-]
-        self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
-        self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
-
-        # Read switches
-        self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
-        self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
-        self.sw_fixft   = self.input.sw_fixft   # Fix the free-troposphere switch
-        self.sw_wind    = self.input.sw_wind    # prognostic wind switch
-        self.sw_sl      = self.input.sw_sl      # surface layer switch
-        self.sw_rad     = self.input.sw_rad     # radiation switch
-        self.sw_ls      = self.input.sw_ls      # land surface switch
-        self.ls_type    = self.input.ls_type    # land surface paramaterization (js or ags)
-        self.sw_cu      = self.input.sw_cu      # cumulus parameterization switch
-
-        self.sw_lit   = self.input.sw_lit       # switch for iterative L calculation
-        self.sw_ac    = self.input.sw_ac        # switch to take account of large-scale gridded Air Circulation (advection and subsidence) fields as input., eg., from ERA-INTERIM 
-        self.sw_ap    = self.input.sw_ap        # switch that tells to initialize with fitted Air Profiles (eg., from balloon soundings) as input
-  
-        # initialize mixed-layer
-        self.h          = self.input.h          # initial ABL height [m]
-        self.Ps         = self.input.Ps         # surface pressure [Pa]
-        self.sp         = self.input.sp         # This is also surface pressure
-                                                #but derived from the global data [Pa]
-        self.divU       = self.input.divU       # horizontal large-scale divergence of wind [s-1]
-        self.ws         = None                  # large-scale vertical velocity [m s-1]
-        self.wf         = None                  # mixed-layer growth due to radiative divergence [m s-1]
-        self.we         = -1.                   # entrainment velocity [m s-1]
-       
-         # Temperature 
-        self.theta      = self.input.theta      # initial mixed-layer potential temperature [K]
-        
-        
-        self.substep    = False
-        self.substeps   = 0
-
-
-
-        self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
-        self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
-        self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
-        self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
-        self.wthetae    = None                  # entrainment kinematic heat flux [K m s-1]
- 
-        self.wstar      = 0.                    # convective velocity scale [m s-1]
- 
-        # 2m diagnostic variables 
-        self.T2m        = None                  # 2m temperature [K]
-        self.q2m        = None                  # 2m specific humidity [kg kg-1]
-        self.e2m        = None                  # 2m vapor pressure [Pa]
-        self.esat2m     = None                  # 2m saturated vapor pressure [Pa]
-        self.u2m        = None                  # 2m u-wind [m s-1]
-        self.v2m        = None                  # 2m v-wind [m s-1]
- 
-        # Surface variables 
-        self.thetasurf  = self.input.theta      # surface potential temperature [K]
-        self.thetavsurf = None                  # surface virtual potential temperature [K]
-        self.qsurf      = None                  # surface specific humidity [g kg-1]
-
-        # Mixed-layer top variables
-        self.P_h        = None                  # Mixed-layer top pressure [pa]
-        self.T_h        = None                  # Mixed-layer top absolute temperature [K]
-        self.q2_h       = None                  # Mixed-layer top specific humidity variance [kg2 kg-2]
-        self.CO22_h     = None                  # Mixed-layer top CO2 variance [ppm2]
-        self.RH_h       = None                  # Mixed-layer top relavtive humidity [-]
-        self.dz_h       = None                  # Transition layer thickness [-]
-        self.lcl        = None                  # Lifting condensation level [m]
-
-        # Virtual temperatures and fluxes
-        self.thetav     = None                  # initial mixed-layer potential temperature [K]
-        self.dthetav    = None                  # initial virtual temperature jump at h [K]
-        self.wthetav    = None                  # surface kinematic virtual heat flux [K m s-1]
-        self.wthetave   = None                  # entrainment kinematic virtual heat flux [K m s-1]
-       
-        
-        
-        
-        
-        
-        # Moisture 
-        self.q          = self.input.q          # initial mixed-layer specific humidity [kg kg-1]
-
-        self.dq         = self.input.dq         # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = self.input.gammaq     # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = self.input.advq       # advection of moisture [kg kg-1 s-1]
-        self.wq         = self.input.wq         # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = None                  # entrainment moisture flux [kg kg-1 m s-1]
-        self.wqM        = None                  # moisture cumulus mass flux [kg kg-1 m s-1]
-  
-        self.qsat       = None                  # mixed-layer saturated specific humidity [kg kg-1]
-        self.esat       = None                  # mixed-layer saturated vapor pressure [Pa]
-        self.e          = None                  # mixed-layer vapor pressure [Pa]
-        self.qsatsurf   = None                  # surface saturated specific humidity [g kg-1]
-        self.dqsatdT    = None                  # slope saturated specific humidity curve [g kg-1 K-1]
-      
-        
-        
-        # CO2
-        fac = self.mair / (self.rho*self.mco2)  # Conversion factor mgC m-2 s-1 to ppm m s-1
-        self.CO2        = self.input.CO2        # initial mixed-layer CO2 [ppm]
-        self.dCO2       = self.input.dCO2       # initial CO2 jump at h [ppm]
-        self.gammaCO2   = self.input.gammaCO2   # free atmosphere CO2 lapse rate [ppm m-1]
-        self.advCO2     = self.input.advCO2     # advection of CO2 [ppm s-1]
-        self.wCO2       = self.input.wCO2 * fac # surface kinematic CO2 flux [ppm m s-1]
-        self.wCO2A      = 0                     # surface assimulation CO2 flux [ppm m s-1]
-        self.wCO2R      = 0                     # surface respiration CO2 flux [ppm m s-1]
-        self.wCO2e      = None                  # entrainment CO2 flux [ppm m s-1]
-        self.wCO2M      = 0                     # CO2 mass flux [ppm m s-1]
-       
-        # Wind 
-        self.u          = self.input.u          # initial mixed-layer u-wind speed [m s-1]
-        self.du         = self.input.du         # initial u-wind jump at h [m s-1]
-        self.gammau     = self.input.gammau     # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = self.input.advu       # advection of u-wind [m s-2]
-        
-        self.v          = self.input.v          # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = self.input.dv         # initial u-wind jump at h [m s-1]
-        self.gammav     = self.input.gammav     # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = self.input.advv       # advection of v-wind [m s-2]
-         
-  # BEGIN -- HW 20170606
-        # z-coordinate for vertical profiles of stratification above the mixed-layer height
-
-        if self.sw_ac:
-        # this is the data frame with the grided profile on the L60 grid
-        # (subsidence, and advection) 
-            self.air_ac      = self.input.air_ac  # full level air circulation
-                                                  # forcing
-            # self.air_ach     = self.input.air_ach # half level air circulation
-            #                                       # forcing
-            
-
-        if self.sw_ap:
-        # this is the data frame with the fitted profile (including HAGL,
-        # THTA,WSPD, SNDU,WNDV PRES ...)
-            self.air_ap      = self.input.air_ap  # initial profile of potential temperature [K]
-
-            # just for legacy reasons...
-            if 'z' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(z= lambda x: x.HAGL)
-            if 'p' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(p= lambda x: x.PRES*100.)
-
-            indexh = np.where(self.air_ap.z.values == self.h)
-            if (len(indexh) == 0) or (indexh[0][0] !=1) or (indexh[0][1] !=2):
-                raise ValueError("Error input profile consistency: mixed- \
-                                 layer height needs to be equal to the second \
-                                 and third \
-                                 level of the vertical profile input!")
-            # initialize q from its profile when available
-            p_old = self.Ps
-            p_new = self.air_ap.p[indexh[0][0]]
-            
-            if ((p_old is not None) & (p_old != p_new)):
-                print("Warning: Ps input was provided ("+str(p_old)+\
-                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
-                    +str(p_new)+"Pa).")
-                                    
-            self.Ps = p_new
-            # these variables/namings are more convenient to work with in the code
-            # we will update the original variables afterwards
-            #self.air_ap['q'] = self.air_ap.QABS/1000.
-
-            self.air_ap = \
-                    self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
-            # we require the temperature fields, since we need to consider
-            # advection
-            # if self.sw_ac:
-            #     #self.air_ap['theta'] = self.air_ap['t'] *
-
-            #     # we consider self.sp in case of air-circulation input (for
-            #     # consistence)
-            #     self.air_ap['t'] = \
-            #                 self.air_ap.theta *  \
-            #                 (self.air_ap.p/self.sp)**(self.air_ap['R']/self.cp)
-            # else:
-            # we consider self.Ps in case of balloon input only 
-            self.air_ap = self.air_ap.assign(t = lambda x: \
-                               x.theta * (x.p/self.Ps)**(x.R/self.cp))
-
-            #self.air_ap['theta'] = self.air_ap.THTA
-            if 'u' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(u = lambda x: x.WNDU)
-            if 'v' not in list(self.air_ap.columns):
-                self.air_ap = self.air_ap.assign(v = lambda x: x.WNDV)
-
-            for var in ['theta','q','u','v']:
-
-                
-                if self.air_ap[var][1] != self.air_ap[var][0]:
-                    raise ValueError("Error input profile consistency: two \
-                                     lowest profile levels for "+var+" should \
-                                     be equal.")
-                
-                # initialize the value from its profile when available
-                value_old = self.__dict__[var]
-                value_new = self.air_ap[var][indexh[0][0]]
-                
-                if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided \
-                                     ("+str(value_old)+ "kg kg-1), \
-                                     but it is now overwritten by the first \
-                                     level (index 0) of air_ap]var\ which is \
-                                     different (" +str(value_new)+"K).")
-                                        
-                self.__dict__[var] = value_new
-
-                # make a profile of the stratification 
-                # please note that the stratification between z_pro[i] and
-                # z_pro[i+1] is given by air_ap.GTHT[i]
-
-                # self.air_ap.GTHT = np.gradient(self.air_ap.THTA) /
-                # np.gradient(self.z_pro)
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
-
-
-                self.__dict__['gamma'+var] = \
-                    self.air_ap['gamma'+var][np.where(self.h >= \
-                                                     self.air_ap.z)[0][-1]]
-
-
-
-        # the variable p_pro is just for diagnosis of lifted index
-            
-            
-
-            # input Ph is wrong, so we correct it according to hydrostatic equation
-            #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-
-            #if self.sw_ac:
-                # note that we use sp as surface pressure, which is determined
-                # from era-interim instead of the observations. This is to
-                # avoid possible failure of the interpolation routine
-                # self.air_ap.p = np.array([self.Ps, self.P_h, self.P_h-0.1]\
-                #                          + \
-                #                          list(self.air_ap.p[3:]))
-
-            # else:
-                # in the other case, it is updated at the time of calculting
-                # the statistics 
-
-# END -- HW 20170606      
-        #print(self.air_ap)
-
-        if self.sw_ac and not self.sw_ap:
-            raise ValueError("air circulation switch only possible when air \
-                             profiles are given")
-        
-        if self.sw_ac:
-
-            # # # we comment this out, because subsidence is calculated
-            # according to advection
-            # #interpolate subsidence towards the air_ap height coordinate
-            # self.air_ap['w'] = np.interp(self.air_ap.p,\
-            #                               self.air_ac.p,\
-            #                               self.air_ac.w) 
-            # #subsidence at the mixed-layer top
-            # self.w = self.air_ap.w[1]
-        
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-                # in case we didn't find any points, we just take the lowest one.
-                # actually, this can happen if ERA-INTERIM pressure levels are
-                # inconsistent with 
-                if in_ml.sum() == 0:
-                    warnings.warn(" no circulation points in the mixed layer \
-                                  found. We just take the bottom one.")
-                    in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-
-                for var in ['t','q','u','v']:
-    
-                   # calculation of the advection variables for the mixed layer
-                   # we weight by the hydrostatic thickness of each layer and
-                   # divide by the total thickness
-                   self.__dict__['adv'+var] = \
-                            ((self.air_ac['adv'+var+'_x'][in_ml] \
-                             + \
-                             self.air_ac['adv'+var+'_y'][in_ml])* \
-                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                            self.air_ac['delpdgrav'][in_ml].sum()
-
-                   # calculation of the advection variables for the profile above
-                   # (lowest 3 values are not used by class)
-                   self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
-                   self.air_ap['adv'+var] = \
-                           np.interp(self.air_ap.p,\
-                                     self.air_ac.p,\
-                                     self.air_ac['adv'+var+'_x']) \
-                           + \
-                           np.interp(self.air_ap.p, \
-                                       self.air_ac.p, \
-                                       self.air_ac['adv'+var+'_y'])
-
-                # as an approximation, we consider that advection of theta in the
-                # mixed layer is equal to advection of t. This is a sufficient
-                # approximation since theta and t are very similar at the surface
-                # pressure.
-                self.__dict__['advtheta'] = self.__dict__['advt']
-
-
-            # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
-            # # # CHECKED AGAIN SINCE THERE IS SIMILAR STRATEGY USED FOR 
-            # # # CALCULATING THE ADVECTION PROFILES
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # self.wrho = np.interp(self.P_h,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) 
-            # self.ws   = self.air_ap.w.iloc[1]
-
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                self.air_ap = self.air_ap.assign(wp = 0.)
-                self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                              self.air_ac.p, \
-                                              self.air_ac['wp'])
-                self.air_ap = self.air_ap.assign(R = 0.)
-                self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                     self.Rv*self.air_ap.q)
-                self.air_ap = self.air_ap.assign(rho = 0.)
-                self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-                
-                self.air_ap = self.air_ap.assign(w = 0.)
-                self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-                #print('hello w ini')
-
-                # Note: in case of sw_ac is False, we update it from prescribed
-                # divergence
-                self.ws   = self.air_ap.w[1]
-
-                # self.ws   = self.wrho/self.rho
-                # self.ws   = self.wrho/(self.P_h/ \
-                #                        (self.Rd*(1.-self.q) + self.Rv*self.q) * \
-                #                         self.theta) # this should be T!!!
-
-                # self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-                #                         + \
-                #                         self.air_ac['divU_y'][in_ml])* \
-                #             self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                #             self.air_ac['delpdgrav'][in_ml].sum() \
-        
-
-        # Tendencies 
-        self.htend      = None                  # tendency of CBL [m s-1]
-        self.thetatend  = None                  # tendency of mixed-layer potential temperature [K s-1]
-        self.dthetatend = None                  # tendency of potential temperature jump at h [K s-1]
-        self.qtend      = None                  # tendency of mixed-layer specific humidity [kg kg-1 s-1]
-        self.dqtend     = None                  # tendency of specific humidity jump at h [kg kg-1 s-1]
-        self.CO2tend    = None                  # tendency of CO2 humidity [ppm]
-        self.dCO2tend   = None                  # tendency of CO2 jump at h [ppm s-1]
-        self.utend      = None                  # tendency of u-wind [m s-1 s-1]
-        self.dutend     = None                  # tendency of u-wind jump at h [m s-1 s-1]
-        self.vtend      = None                  # tendency of v-wind [m s-1 s-1]
-        self.dvtend     = None                  # tendency of v-wind jump at h [m s-1 s-1]
-        self.dztend     = None                  # tendency of transition layer thickness [m s-1]
-  
-        # initialize surface layer
-        self.ustar      = self.input.ustar      # surface friction velocity [m s-1]
-        self.uw         = None                  # surface momentum flux in u-direction [m2 s-2]
-        self.vw         = None                  # surface momentum flux in v-direction [m2 s-2]
-        self.z0m        = self.input.z0m        # roughness length for momentum [m]
-        self.z0h        = self.input.z0h        # roughness length for scalars [m]
-        self.Cm         = 1e12                  # drag coefficient for momentum [-]
-        self.Cs         = 1e12                  # drag coefficient for scalars [-]
-        self.L          = None                  # Obukhov length [m]
-        self.Rib        = None                  # bulk Richardson number [-]
-        self.ra         = None                  # aerodynamic resistance [s m-1]
-  
-        # initialize radiation
-        self.lat        = self.input.lat        # latitude [deg]
-        #self.fc         = self.input.fc         # coriolis parameter [s-1]
-        self.fc         = 4. * np.pi/(24.*3600.) * np.sin(self.lat/180.*np.pi)
-        self.lon        = self.input.lon        # longitude [deg]
-        self.doy        = self.input.doy        # day of the year [-]
-        self.tstart     = self.input.tstart     # time of the day [-]
-        self.cc         = self.input.cc         # cloud cover fraction [-]
-        self.Swin       = None                  # incoming short wave radiation [W m-2]
-        self.Swout      = None                  # outgoing short wave radiation [W m-2]
-        self.Lwin       = None                  # incoming long wave radiation [W m-2]
-        self.Lwout      = None                  # outgoing long wave radiation [W m-2]
-        self.Q          = self.input.Q          # net radiation [W m-2]
-        self.dFz        = self.input.dFz        # cloud top radiative divergence [W m-2] 
-  
-        # initialize land surface
-        self.wg         = self.input.wg         # volumetric water content top soil layer [m3 m-3]
-        self.w2         = self.input.w2         # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = self.input.Tsoil      # temperature top soil layer [K]
-        self.T2         = self.input.T2         # temperature deeper soil layer [K]
-                           
-        self.a          = self.input.a          # Clapp and Hornberger retention curve parameter a [-]
-        self.b          = self.input.b          # Clapp and Hornberger retention curve parameter b [-]
-        self.p          = self.input.p          # Clapp and Hornberger retention curve parameter p [-]
-        self.CGsat      = self.input.CGsat      # saturated soil conductivity for heat
-                           
-        self.wsat       = self.input.wsat       # saturated volumetric water content ECMWF config [-]
-        self.wfc        = self.input.wfc        # volumetric water content field capacity [-]
-        self.wwilt      = self.input.wwilt      # volumetric water content wilting point [-]
-                           
-        self.C1sat      = self.input.C1sat      
-        self.C2ref      = self.input.C2ref      
-
-        self.c_beta     = self.input.c_beta     # Curvature plant water-stress factor (0..1) [-]
-        
-        self.LAI        = self.input.LAI        # leaf area index [-]
-        self.gD         = self.input.gD         # correction factor transpiration for VPD [-]
-        self.rsmin      = self.input.rsmin      # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = self.input.rssoilmin  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = self.input.alpha      # surface albedo [-]
-  
-        self.rs         = 1.e6                  # resistance transpiration [s m-1]
-        self.rssoil     = 1.e6                  # resistance soil [s m-1]
-                           
-        self.Ts         = self.input.Ts         # surface temperature [K]
-                           
-        self.cveg       = self.input.cveg       # vegetation fraction [-]
-        self.Wmax       = self.input.Wmax       # thickness of water layer on wet vegetation [m]
-        self.Wl         = self.input.Wl         # equivalent water layer depth for wet vegetation [m]
-        self.cliq       = None                  # wet fraction [-]
-                          
-        self.Lambda     = self.input.Lambda     # thermal diffusivity skin layer [-]
-  
-        self.Tsoiltend  = None                  # soil temperature tendency [K s-1]
-        self.wgtend     = None                  # soil moisture tendency [m3 m-3 s-1]
-        self.Wltend     = None                  # equivalent liquid water tendency [m s-1]
-  
-        self.H          = None                  # sensible heat flux [W m-2]
-        self.LE         = None                  # evapotranspiration [W m-2]
-        self.LEliq      = None                  # open water evaporation [W m-2]
-        self.LEveg      = None                  # transpiration [W m-2]
-        self.LEsoil     = None                  # soil evaporation [W m-2]
-        self.LEpot      = None                  # potential evaporation [W m-2]
-        self.LEref      = None                  # reference evaporation using rs = rsmin / LAI [W m-2]
-        self.G          = None                  # ground heat flux [W m-2]
-
-        # initialize A-Gs surface scheme
-        self.c3c4       = self.input.c3c4       # plant type ('c3' or 'c4')
-
-        # initialize cumulus parameterization
-        self.sw_cu      = self.input.sw_cu      # Cumulus parameterization switch
-        self.dz_h       = self.input.dz_h       # Transition layer thickness [m]
-        self.ac         = 0.                    # Cloud core fraction [-]
-        self.M          = 0.                    # Cloud core mass flux [m s-1] 
-        self.wqM        = 0.                    # Cloud core moisture flux [kg kg-1 m s-1] 
-  
-        # initialize time variables
-        self.tsteps = int(np.floor(self.input.runtime / self.input.dt))
-        self.dt     = self.input.dt
-        self.dtcur      = self.dt
-        self.firsttime = True
-        self.t      = 0
- 
-        # Some sanity checks for valid input
-        if (self.c_beta is None): 
-            self.c_beta = 0                     # Zero curvature; linear response
-        assert(self.c_beta >= 0 or self.c_beta <= 1)
-
-        # initialize output
-        self.out = model_output(self.tsteps)
- 
-        self.statistics()
-  
-        # calculate initial diagnostic variables
-        if(self.sw_rad):
-            self.run_radiation()
- 
-        if(self.sw_sl):
-            for i in range(10): 
-                self.run_surface_layer()
-  
-        if(self.sw_ls):
-            self.run_land_surface()
-
-        if(self.sw_cu):
-            self.run_mixed_layer()
-            self.run_cumulus()
-        
-        if(self.sw_ml):
-            self.run_mixed_layer()
-
-    def timestep(self):
-
-        self.dtmax = +np.inf
-        self.logger.debug('before stats') 
-        self.statistics()
-
-        # run radiation model
-        self.logger.debug('before rad') 
-        if(self.sw_rad):
-            self.run_radiation()
-  
-        # run surface layer model
-        if(self.sw_sl):
-            self.logger.debug('before surface layer') 
-            self.run_surface_layer()
-        
-        # run land surface model
-        if(self.sw_ls):
-            self.logger.debug('before land surface') 
-            self.run_land_surface()
- 
-        # run cumulus parameterization
-        if(self.sw_cu):
-            self.logger.debug('before cumulus') 
-            self.run_cumulus()
-   
-        self.logger.debug('before mixed layer') 
-        # run mixed-layer model
-        if(self.sw_ml):
-            self.run_mixed_layer()
-        self.logger.debug('after mixed layer') 
- 
-        #get first profile data point above mixed layer
-        if self.sw_ap:
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                # here we correct for the fact that the upper profile also
-                # shifts in the vertical.
-
-                diffhtend = self.htend - self.air_ap.w[zidx_first]
-                if diffhtend > 0:
-                    dtmax_new = (self.air_ap.z[zidx_first] - self.h)/ diffhtend
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            else:
-                if self.htend > 0:
-                    dtmax_new = ( self.air_ap.z[zidx_first] - self.h)/self.htend 
-                    self.dtmax= min(dtmax_new,self.dtmax)
-            #print(self.h,zidx_first,self.ws,self.air_ap.z)
-
-        
-        #print(self.t,self.dtcur,self.dt,dtmax,self.air_ap.z[zidx_first],self.h)
-        self.logger.debug('before store') 
-        self.substep =  (self.dtcur > self.dtmax)
-        if self.substep:
-            dtnext = self.dtcur - self.dtmax
-            self.dtcur = self.dtmax
-
-        #print(self.t,self.dtcur,self.dt,dtmax,self.tstart + self.t*self.dt/3600.)
-
-        # HW: this will be done multiple times in case of a substep is needed
-        # store output before time integration
-        if self.firsttime:
-            self.store()
-  
-        self.logger.debug('before integrate land surface ('+str(self.t)+', '+str(self.dtcur)+')')
-        # time integrate land surface model
-        if(self.sw_ls):
-            self.integrate_land_surface()
-        self.logger.debug('before integrate mixed layer') 
-        # time integrate mixed-layer model
-        if(self.sw_ml):
-            self.integrate_mixed_layer() 
-        self.logger.debug('after integrate mixed layer') 
-        if self.substep:
-            self.dtcur = dtnext
-            self.firsttime = False
-            self.substeps += 1
-        else:
-            self.dtcur = self.dt
-            self.t += 1 
-            self.firsttime = True
-            self.substeps = 0
-        self.logger.debug('going to next step')
-        
-        
-  
-    def statistics(self):
-        # Calculate virtual temperatures 
-        self.thetav   = self.theta  + 0.61 * self.theta * self.q
-        self.wthetav  = self.wtheta + 0.61 * self.theta * self.wq
-        self.dthetav  = (self.theta + self.dtheta) * (1. + 0.61 * (self.q + self.dq)) - self.theta * (1. + 0.61 * self.q)
-        # Mixed-layer top properties
-        self.P_h    = self.Ps - self.rho * self.g * self.h
-        # else:
-            # in the other case, it is updated at the time that the profile is
-            # updated (and at the initialization
-
-        self.T_h    = self.theta - self.g/self.cp * self.h
-
-        #self.P_h    = self.Ps / np.exp((self.g * self.h)/(self.Rd * self.theta))
-        #self.T_h    = self.theta / (self.Ps / self.P_h)**(self.Rd/self.cp)
-
-        self.RH_h   = self.q / qsat(self.T_h, self.P_h)
-
-        # Find lifting condensation level iteratively
-        if(self.t == 0):
-            self.lcl = self.h
-            RHlcl = 0.5
-        else:
-            RHlcl = 0.9998 
-
-        itmax = 30
-        it = 0
-        while(((RHlcl <= 0.9999) or (RHlcl >= 1.0001)) and it 0):
-            self.q2_h   = -(self.wqe  + self.wqM  ) * self.dq   * self.h / (self.dz_h * self.wstar)
-            self.CO22_h = -(self.wCO2e+ self.wCO2M) * self.dCO2 * self.h / (self.dz_h * self.wstar)
-        else:
-            self.q2_h   = 0.
-            self.CO22_h = 0.
-
-        # calculate cloud core fraction (ac), mass flux (M) and moisture flux (wqM)
-        self.ac     = max(0., 0.5 + (0.36 * np.arctan(1.55 * ((self.q - qsat(self.T_h, self.P_h)) / self.q2_h**0.5))))
-        self.M      = self.ac * self.wstar
-        self.wqM    = self.M * self.q2_h**0.5
-
-        # Only calculate CO2 mass-flux if mixed-layer top jump is negative
-        if(self.dCO2 < 0):
-            self.wCO2M  = self.M * self.CO22_h**0.5
-        else:
-            self.wCO2M  = 0.
-
-    def run_mixed_layer(self):
-        if(not self.sw_sl):
-            # decompose ustar along the wind components
-            self.uw = - np.sign(self.u) * (self.ustar ** 4. / (self.v ** 2. / self.u ** 2. + 1.)) ** (0.5)
-            self.vw = - np.sign(self.v) * (self.ustar ** 4. / (self.u ** 2. / self.v ** 2. + 1.)) ** (0.5)
-
-
-
-        # calculate large-scale vertical velocity (subsidence)
-        if not ((self.sw_ac is not None) and ('w' in self.sw_ac)):
-            self.ws = -self.divU * self.h
-        # else:
-        #     in case the air circulation switch is turned on, subsidence is
-        #     calculated from the circulate profile at the initialization and
-        #     in the integrate_mixed_layer routine
-              
-        # calculate compensation to fix the free troposphere in case of subsidence 
-        if(self.sw_fixft):
-            w_th_ft  = self.gammatheta * self.ws
-            w_q_ft   = self.gammaq     * self.ws
-            w_CO2_ft = self.gammaCO2   * self.ws 
-        else:
-            w_th_ft  = 0.
-            w_q_ft   = 0.
-            w_CO2_ft = 0. 
-      
-        # calculate mixed-layer growth due to cloud top radiative divergence
-        self.wf = self.dFz / (self.rho * self.cp * self.dtheta)
-       
-        # calculate convective velocity scale w* 
-        if(self.wthetav > 0.):
-            self.wstar = ((self.g * self.h * self.wthetav) / self.thetav)**(1./3.)
-        else:
-            self.wstar  = 1e-6;
-      
-        # Virtual heat entrainment flux 
-        self.wthetave    = -self.beta * self.wthetav 
-        
-        # compute mixed-layer tendencies
-        if(self.sw_shearwe):
-            self.we    = (-self.wthetave + 5. * self.ustar ** 3. * self.thetav / (self.g * self.h)) / self.dthetav
-        else:
-            self.we    = -self.wthetave / self.dthetav
-        # Don't allow boundary layer shrinking if wtheta < 0 
-        if(self.we < 0):
-            self.we = 0.
-
-        # Calculate entrainment fluxes
-        self.wthetae     = -self.we * self.dtheta
-        self.wqe         = -self.we * self.dq
-        self.wCO2e       = -self.we * self.dCO2
-        
-        htend_pre       = self.we + self.ws + self.wf - self.M
-        
-        #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-        
- 
-        #print('thetatend_pre',thetatend_pre)
-        
-        #preliminary boundary-layer top chenage
-        #htend_pre = self.we + self.ws + self.wf - self.M
-        #preliminary change in temperature jump
-        dthetatend_pre  = self.gammatheta * (self.we + self.wf - self.M) - \
-                          thetatend_pre + w_th_ft
-        
-        dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
-        l_entrainment = True
-
-        if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
-            l_entrainment = False
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! temperature jump is at the lower limit \
-                          and is not growing: entrainment is disabled for this (sub)timestep.") 
-        elif dtheta_pre < 0.1:
-            dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
-            l_entrainment = True
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          " Warning! Potential temperature jump at mixed- \
-                          layer height would become too low limiting timestep \
-                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
-            self.dtmax = min(self.dtmax,dtmax_new)
-            warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "next subtimestep, entrainment will be disabled")
-            #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
-
-
-
-        # when entrainment is disabled, we just use the simplified formulation
-        # as in Wouters et al., 2013 (section 2.2.1)
-
-        self.dthetatend = l_entrainment*dthetatend_pre + \
-                        (1.-l_entrainment)*0.
-        self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
-        self.htend = l_entrainment*htend_pre + \
-                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
-        #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
-        #stop
-
-
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
-
-
-        # self.qtend = l_entrainment*qtend_pre + \
-        #              (1.-l_entrainment)*( (self.wq  - self.wqM)/self.h + self.advq)
-        # self.CO2tend = l_entrainment*CO2tend_pre + \
-        #              (1.-l_entrainment)*( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-
-
-        #     # part of the timestep for which the temperature mixed-layer jump
-        #     # was changing, and for which entrainment took place. For the other
-        #     # part, we don't assume entrainment anymore, and we use the
-        #     # simplified formulation  of Wouters et al., 2013
-
-        #     #self.htend =(self.dthetatend + self.thetatend - w_th_ft)/self.gammatheta +self.ws
-        #   
-        #     self.thetatend = l_entrainment*(self.gammatheta * (self.we + self.wf - self.M) - \
-        #                      self.dthetatend + w_th_ft) + \
-        #                      l_entrainment*((self.wtheta  ) / self.h + self.advtheta)
-        #     self.htend = fac*self.htend + \
-        #                  (1.-fac)* (( self.ws  - self.M)+((self.wtheta) / self.h + self.advtheta)/self.gammatheta)
-        #     self.qtend = fac*self.qtend + (1.-fac)* ( (self.wq  - self.wqM)/self.h + self.advq)
-        #     self.CO2tend = fac*self.qtend + (1.-fac)* ( (self.wCO2  - self.wCO2M)/self.h + self.advCO2)
-
-        #     #self.thetatend += (self.wtheta - self.wthetae             ) / self.h + self.advtheta
-
-        # else:
-        #     #self.htend = htend_pre
-        #     self.dthetatend = dthetatend_pre
-        #     self.thetatend = thetatend_pre
-        
-        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
-     
-        # assume u + du = ug, so ug - u = du
-        if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
-  
-            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
-        
-        # tendency of the transition layer thickness
-        if(self.ac > 0 or self.lcl - self.h < 300):
-            self.dztend = ((self.lcl - self.h)-self.dz_h) / 7200.
-        else:
-            self.dztend = 0.
-
-   
-    def integrate_mixed_layer(self):
-        # set values previous time step
-        h0      = self.h
-        
-        theta0  = self.theta
-        dtheta0 = self.dtheta
-        q0      = self.q
-        dq0     = self.dq
-        CO20    = self.CO2
-        dCO20   = self.dCO2
-        
-        u0      = self.u
-        du0     = self.du
-        v0      = self.v
-        dv0     = self.dv
-
-        dz0     = self.dz_h
-  
-        # integrate mixed-layer equations
-        
-            
-
-# END -- HW 20170606        
-        self.h        = h0      + self.dtcur * self.htend
-        # print(self.h,self.htend)
-        # stop
-        self.theta    = theta0  + self.dtcur * self.thetatend
-        #print(dtheta0,self.dtcur,self.dthetatend)
-        self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
-        self.q        = q0      + self.dtcur * self.qtend
-        self.dq       = dq0     + self.dtcur * self.dqtend
-        self.CO2      = CO20    + self.dtcur * self.CO2tend
-        self.dCO2     = dCO20   + self.dtcur * self.dCO2tend
-        self.dz_h     = dz0     + self.dtcur * self.dztend
-            
-        # Limit dz to minimal value
-        dz0 = 50
-        if(self.dz_h < dz0):
-            self.dz_h = dz0 
-  
-        if(self.sw_wind):
-            self.u        = u0      + self.dtcur * self.utend
-            self.du       = du0     + self.dtcur * self.dutend
-            self.v        = v0      + self.dtcur * self.vtend
-            self.dv       = dv0     + self.dtcur * self.dvtend
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-            for var in ['t','q','u','v']:
-                #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
-
-            # take into account advection for the whole profile
-                
-                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
-
-            var = 'z'
-            #print(self.air_ap[var])
-                #     print(self.air_ap['adv'+var])
-
-
-
-
-            #moving the profile vertically according to the vertical wind
-                #if ((self.air_ap.z is not None) and (self.air_ap.w is not None)):
-
-
-            # air_apvarold = pd.Series(np.array(self.air_ap.z))
-            # print(self.h,self.ws,self.htend,self.dtcur,air_apvarold )
-            # stop
-
-
-                # # recalculate subsidence at the mixed-layer top from the profile. Yet, this would be overwritten from the external forcing.
-                # self.ws = np.interp(self.h , self.z_pro,self.w_pro)
-
-            #As t is updated, we also need to recalculate theta (and R)
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-
-            # air_aptheta_old = pd.Series(self.air_ap['theta'])
-            self.air_ap['theta'] = \
-                        self.air_ap.t * \
-                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            zidx_first = np.where(self.air_ap.z > self.h)[0][0]
-            self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
-                                         self.dtcur * self.air_ap.w[zidx_first:]
-
-#            print(self.t, self.dtcur,self.dt,self.air_ap.w[zidx_first])
-#            print(self.t, self.dtcur,self.dt,self.htend)
-
-            # # the pressure levels of the profiles are recalculated according to
-            # # there new height (after subsidence)
-            # self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] - \
-            #         self.air_ap.p[zidx_first:]/self.air_ap['R'][zidx_first:]/self.air_ap['t'][zidx_first:] \
-            #         * self.dtcur *  self.air_ap.w[zidx_first:]
-
-            self.air_ap.p[zidx_first:] = self.air_ap.p[zidx_first:] + \
-                    self.dtcur * self.air_ap.wp[zidx_first:]
-
-            #print(pd.DataFrame([self.air_ap.z,air_apvarold]))
-        # note that theta and q itself are updatet by class itself
-
-    
-        if self.sw_ap:
-            # Just for model consistency preservation purposes, we set the
-            # theta variables of the mixed-layer to nan values, since the
-            # mixed-layer values should overwritte by the mixed-layer
-            # calculations of class.
-            self.air_ap['theta'][0:3] = np.nan 
-            self.air_ap['p'][0:3] = np.nan 
-            self.air_ap['q'][0:3] = np.nan 
-            self.air_ap['u'][0:3] = np.nan 
-            self.air_ap['v'][0:3] = np.nan 
-            self.air_ap['t'][0:3] = np.nan 
-            self.air_ap['z'][0:3] = np.nan 
-
-            # Update the vertical profiles: 
-            #   - new mixed layer properties( h, theta, q ...)
-            #   - any data points below the new ixed-layer height are removed
-
-            # Three data points at the bottom that describe the mixed-layer
-            # properties
-            air_ap_head = self.air_ap.iloc[0:3] # make an empty table with similar
-                                           # columns as air_ap
-            # air_ap_head['z'].iloc[0] = 2.
-            # air_ap_head['z'].iloc[1] = self.__dict__['h']
-            # air_ap_head['z'].iloc[2] = self.__dict__['h']
-            air_ap_head.values[:,list(air_ap_head.columns).index('z')] = \
-                        [2.,self.__dict__['h'],self.__dict__['h']]
-            for var in ['theta','q','u','v']:
-
-                air_ap_head.values[:,list(air_ap_head.columns).index(var)] = \
-                        [self.__dict__[var], \
-                         self.__dict__[var], \
-                         self.__dict__[var] + self.__dict__['d'+var]]
-                
-            #print(self.air_ap)
-
-            # This is the remaining profile considering the remaining
-            # datapoints above the mixed layer height
-            air_ap_tail = self.air_ap.iloc[3:]
-            air_ap_tail = air_ap_tail[air_ap_tail.z > self.h]
-
-            # print('h',self.h)
-            # # only select samples monotonically increasing with height
-            # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            # air_ap_tail = pd.DataFrame()
-            # theta_low = self.theta
-            # z_low =     self.h
-            # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            # for ibottom in range(1,len(air_ap_tail_orig)):
-            #     if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +2.:
-            #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
-
-
-
-
-            # make theta increase strong enough to avoid numerical
-            # instability
-            air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-            air_ap_tail = pd.DataFrame()
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-            theta_low = self.theta
-            z_low =     self.h
-            ibottom = 0
-            itop = 0
-            # print(air_ap_tail_orig)
-            # stop
-
-            # HW: this is the lower limit that we use for gammatheta, which is
-            # there to avoid model crashes. Besides on this limit, the upper
-            # air profile is modified in a way that is still conserves total
-            # quantities of moisture and temperature. The limit is set by trial
-            # and error. The numerics behind the crash should be investigated
-            # so that a cleaner solution can be provided.
-            gammatheta_lower_limit = 0.002
-            while ((itop in range(0,1)) or (itop != ibottom)):
-                theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-                z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-                if (
-                    #(z_mean > (z_low+0.2)) and \
-                    #(theta_mean > (theta_low+0.02) ) and \
-                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
-                  (itop >= (len(air_ap_tail_orig)-1)) \
-                   :
-
-                    air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                    ibottom = itop+1
-                    theta_low = air_ap_tail.theta.iloc[-1]
-                    z_low =     air_ap_tail.z.iloc[-1]
-    
-
-                itop +=1
-                # elif  (itop > len(air_ap_tail_orig)-10):
-                #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-                #print(itop,ibottom)
-
-            if itop > 1:
-                    warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! Temperature profile was too steep. \
-                                  Modifying profile: "+ \
-                                  str(itop - 1)+ " measurements were dropped \
-                                  and replaced with its average \
-                                  Modifying profile. \
-                                  mean with next profile point(s).") 
-
-
-            self.air_ap = pd.concat((air_ap_head,\
-                                     air_ap_tail,\
-                                     air_ap_tail_orig[itop:])).reset_index().drop('index',\
-                                                                      axis=1)
-
-            if  self.sw_ac:
-                qvalues = \
-                        self.air_ap.values[:,list(self.air_ap.columns).index('q')]
-
-                self.air_ap.values[:,list(self.air_ap.columns).index('R')] = \
-                        (self.Rd*(1.-qvalues) + self.Rv*qvalues)
-                #self.Ph = self.Ps - self.h * self.g * Density(self.T2m,self.Ps,self.q)
-                self.P_h    = self.Ps - self.rho * self.g * self.h
-                self.air_ap.values[:3,list(self.air_ap.columns).index('p')] = \
-                        [self.Ps,  self.P_h, self.P_h-0.1]
-
-                self.air_ap.t = \
-                            self.air_ap.theta * \
-                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
-
-
-        # WARNING: self.sw_ac always requires self.sw_ap for now!!!
-
-
-
-
-        # else:
-            # in the other case, it is updated at the time the statistics are
-            # calculated 
-
-        if (self.sw_ac is not None) and ('adv' in self.sw_ac):
-
-
-            self.P_h    = self.Ps - self.rho * self.g * self.h
-            in_ml = (self.air_ac.p >= self.P_h)
-
-            if in_ml.sum() == 0:
-                warnings.warn(" no circulation points in the mixed layer \
-                              found. We just take the bottom one.")
-                in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-            for var in ['t','q','u','v']:
-
-                # calculation of the advection variables for the mixed-layer
-                # these will be used for the next timestep
-                # Warning: w is excluded for now.
-
-                self.__dict__['adv'+var] = \
-                        ((self.air_ac['adv'+var+'_x'][in_ml] \
-                         + \
-                         self.air_ac['adv'+var+'_y'][in_ml])* \
-                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                        self.air_ac['delpdgrav'][in_ml].sum()
-
-                # calculation of the advection variables for the profile above
-                # the mixed layer (also for the next timestep)
-                self.air_ap['adv'+var] = \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p,\
-                                              self.air_ac['adv'+var+'_x']) \
-                                    + \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p, \
-                                              self.air_ac['adv'+var+'_y'])
-                # if var == 't':
-                #     print(self.air_ap['adv'+var])
-                #     stop
-
-            # as an approximation, we consider that advection of theta in the
-            # mixed layer is equal to advection of t. This is a sufficient
-            # approximation since theta and t are very similar at the surface
-            # pressure.
-
-            self.__dict__['advtheta'] = self.__dict__['advt']
-
-        if (self.sw_ac is not None) and ('w' in self.sw_ac):
-            # update the vertical wind profile
-            self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                          self.air_ac.p, \
-                                          self.air_ac['wp'])
-            self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
-                                                 self.Rv*self.air_ap.q)
-            self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
-            
-            air_apwold = self.air_ap['w']
-            self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
-            #print('hello w upd')
-
-            # # # WARNING, THIS DOESN't GIVE THE EXPECTED VALUE!!!
-            # # interpolate subsidence x density
-            # self.air_ap['wrho'] = \
-            #            np.interp(self.air_ap.p,\
-            #                      self.air_ach.p,\
-            #                      self.air_ach['wrho']) \
-            #     
-            # self.air_ap['w'] = \
-            #     self.air_ap['wrho']/(self.air_ap.p/ \
-            #                          (self.Rd*(1.-self.air_ap.q) + \
-            #                           self.Rv*self.air_ap.q)* \
-            #                          self.air_ap.TEMP)
-            # # self.wrho = np.interp(self.P_h,\
-            # #                      self.air_ach.p,\
-            # #                      self.air_ach['wrho']) \
-
-
-
-            # Also update the vertical wind at the mixed-layer height
-            # (subsidence)
-            self.ws   = self.air_ap.w[1]
-        #    print('ws',self.ws,self.air_ap.wp[1],self.air_ap.R[1],self.air_ap.t[1],self.air_ap.q[1])
-
-            ## Finally, we update he 
-            #self.__dict__['divU'] = ((self.air_ac['divU_x'][in_ml] \
-            #                        + \
-            #                        self.air_ac['divU_y'][in_ml])* \
-            #            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-            #            self.air_ac['delpdgrav'][in_ml].sum() 
-            
-
-        if self.sw_ap:
-            for var in ['theta','q','u','v']:
-
-                # update of the slope (gamma) for the different variables, for
-                # the next timestep!
-
-                # there is an warning message that tells about dividing through
-                # zero, which we ignore
-
-                with np.errstate(divide='ignore'):
-                    gammavar = list(np.array(self.air_ap[var][1:].values - \
-                                             self.air_ap[var][:-1].values) \
-                                    / np.array(self.air_ap['z'][1:].values - \
-                                               self.air_ap['z'][:-1].values))
-
-                    # add last element twice (since we have one element less)
-                gammavar.append(gammavar[-1])
-                gammavar = np.array(gammavar)
-                self.air_ap['gamma'+var] = gammavar
-
-                # Based on the above, update the gamma value at the mixed-layer
-                # top
-                self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
-                                                                     self.air_ap.z)[0][-1]]
-
-            
-    def run_radiation(self):
-        sda    = 0.409 * np.cos(2. * np.pi * (self.doy - 173.) / 365.)
-        sinlea = np.sin(2. * np.pi * self.lat / 360.) * np.sin(sda) - np.cos(2. * np.pi * self.lat / 360.) * np.cos(sda) * np.cos(2. * np.pi * (self.t * self.dt + self.tstart * 3600.) / 86400. - 2. * np.pi * self.lon / 360.)
-        sinlea = max(sinlea, 0.0001)
-        
-        Ta  = self.theta * ((self.Ps - 0.1 * self.h * self.rho * self.g) / self.Ps ) ** (self.Rd / self.cp)
-  
-        Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
-  
-        self.Swin  = self.S0 * Tr * sinlea
-        self.Swout = self.alpha * self.S0 * Tr * sinlea
-        
-        
-        self.Lwin  = 0.8 * self.bolz * Ta ** 4.
-        self.Lwout = self.bolz * self.Ts ** 4.
-          
-        self.Q     = self.Swin - self.Swout + self.Lwin - self.Lwout
-        #print('Q',self.Q,self.Swin,self.Swout,self.Lwin,self.Lwout)
-  
-    def run_surface_layer(self):
-        # HW: I had to raise the minimum wind speed to make the simulation with
-        # the non-iterative solution stable (this solution was a wild guess, so I don't
-        # know the exact problem of the instability in case of very low wind
-        # speeds yet)
-        #ueff           = max(0.01, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        # version of 20180730 where there are still some runs crashing. Maybe
-        # an upper limit should be set on the monin-obukhov length instead of
-        # a lower limmit on the wind speed?
-        #ueff           = max(0.1, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        ueff           = max(0.5, np.sqrt(self.u**2. + self.v**2. + self.wstar**2.))
-
-        
-        self.thetasurf = self.theta + self.wtheta / (self.Cs * ueff)
-        qsatsurf       = qsat(self.thetasurf, self.Ps)
-        cq             = (1. + self.Cs * ueff * self.rs) ** -1.
-        self.qsurf     = (1. - cq) * self.q + cq * qsatsurf
-
-        self.thetavsurf = self.thetasurf * (1. + 0.61 * self.qsurf)
-  
-        zsl       = 0.1 * self.h
-        self.Rib  = self.g / self.thetav * zsl * (self.thetav - self.thetavsurf) / ueff**2.
-        
-
-
-        if self.sw_lit:
-            self.Rib  = min(self.Rib, 0.2)
-            self.L     = ribtol(self.Rib, zsl, self.z0m, self.z0h)  # Slow python iteration
-            self.zeta  = zsl/self.L
-            #self.L    = ribtol.ribtol(self.Rib, zsl, self.z0m, self.z0h) # Fast C++ iteration
-            
-        
-            self.Cm   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) ** 2.
-            self.Cs   = self.k**2. / (np.log(zsl / self.z0m) - psim(self.zeta) + psim(self.z0m / zsl* self.zeta)) / (np.log(zsl / self.z0h) - self.psih(self.zeta) + self.psih(self.z0h / zsl* self.zeta))
-            
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-        
-     
-            # diagnostic meteorological variables
-            self.T2m    = self.thetasurf - self.wtheta / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.q2m    = self.qsurf     - self.wq     / self.ustar / self.k * (np.log(2. / self.z0h) - psih(2. / zsl* self.zeta) + psih(self.z0h / zsl* self.zeta))
-            self.u2m    =                - self.uw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + psim(self.z0m / zsl* self.zeta))
-            self.v2m    =                - self.vw     / self.ustar / self.k * (np.log(2. / self.z0m) - psim(2. / zsl* self.zeta) + self.psim(self.z0m / zsl* self.zeta))
-            
-            # diagnostic meteorological variables
-        else:
-            
-            ## circumventing any iteration with Wouters et al., 2012
-            self.zslz0m = np.max((zsl/self.z0m,10.))
-            #self.Rib  = self.Rib / zsl*self.z0m *self.zslz0m
-            self.zeta = zeta_hs2(self.Rib, self.zslz0m, np.log(self.z0m/self.z0h))
-            #print(str(self.t)+'/'+str(self.tsteps)+' zeta: ',self.zeta,self.Rib, zsl,self.z0m,self.z0h)
-            self.L = zsl/self.zeta
-            funm,funh = funcsche(self.zeta,self.zslz0m, np.log(self.z0m/self.z0h))
-        
-            self.Cm = self.k**2.0/funm/funm
-            self.Cs = self.k**2.0/funm/funh
-            
-            self.ustar = np.sqrt(self.Cm) * ueff
-            self.uw    = - self.Cm * ueff * self.u
-            self.vw    = - self.Cm * ueff * self.v
-            
-            # extrapolation from mixed layer (instead of from surface) to 2meter
-            self.T2m    = self.theta - self.wtheta / self.ustar / self.k * funh
-            self.q2m    = self.q     - self.wq     / self.ustar / self.k * funh
-            self.u2m    =                - self.uw     / self.ustar / self.k * funm
-            self.v2m    =                - self.vw     / self.ustar / self.k * funm
-        
-        
-        self.esat2m = 0.611e3 * np.exp(17.2694 * (self.T2m - 273.16) / (self.T2m - 35.86))
-        self.e2m    = self.q2m * self.Ps / 0.622
-     
-    def ribtol(self, Rib, zsl, z0m, z0h): 
-        if(Rib > 0.):
-            L    = 1.
-            L0   = 2.
-        else:
-            L  = -1.
-            L0 = -2.
-        #print(Rib,zsl,z0m,z0h)
-        
-        while (abs(L - L0) > 0.001):
-            L0      = L
-            fx      = Rib - zsl / L * (np.log(zsl / z0h) - self.psih(zsl / L) + self.psih(z0h / L)) / (np.log(zsl / z0m) - self.psim(zsl / L) + self.psim(z0m / L))**2.
-            Lstart  = L - 0.001*L
-            Lend    = L + 0.001*L
-            fxdif   = ( (- zsl / Lstart * (np.log(zsl / z0h) - self.psih(zsl / Lstart) + self.psih(z0h / Lstart)) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lstart) + self.psim(z0m / Lstart))**2.) \
-                      - (-zsl /  Lend   * (np.log(zsl / z0h) - self.psih(zsl / Lend  ) + self.psih(z0h / Lend  )) / \
-                                          (np.log(zsl / z0m) - self.psim(zsl / Lend  ) + self.psim(z0m / Lend  ))**2.) ) / (Lstart - Lend)
-            L       = L - fx / fxdif
-            #print(L)
-            if(abs(L) > 1e12):
-                break
-
-        return L
-      
-    def psim(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psim  = 3.14159265 / 2. - 2. * np.arctan(x) + np.log((1. + x)**2. * (1. + x**2.) / 8.)
-            #x     = (1. + 3.6 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psim = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psim  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - zeta - (10./3.) / 0.35
-        return psim
-      
-    def psih(self, zeta):
-        if(zeta <= 0):
-            x     = (1. - 16. * zeta)**(0.25)
-            psih  = 2. * np.log( (1. + x*x) / 2.)
-            #x     = (1. + 7.9 * abs(zeta) ** (2./3.)) ** (-0.5)
-            #psih  = 3. * np.log( (1. + 1. / x) / 2.)
-        else:
-            psih  = -2./3. * (zeta - 5./0.35) * np.exp(-0.35 * zeta) - (1. + (2./3.) * zeta) ** (1.5) - (10./3.) / 0.35 + 1.
-        return psih
- 
-    def jarvis_stewart(self):
-        # calculate surface resistances using Jarvis-Stewart model
-        if(self.sw_rad):
-            f1 = 1. / min(1.,((0.004 * self.Swin + 0.05) / (0.81 * (0.004 * self.Swin + 1.))))
-        else:
-            f1 = 1.
-  
-        if(self.w2 > self.wwilt):# and self.w2 <= self.wfc):
-            f2 = (self.wfc - self.wwilt) / (self.w2 - self.wwilt)
-        else:
-            f2 = 1.e8
- 
-        # Limit f2 in case w2 > wfc, where f2 < 1
-        f2 = max(f2, 1.);
- 
-        f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
-        f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
-  
-        self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
-
-    def factorial(self,k):
-        factorial = 1
-        for n in range(2,k+1):
-            factorial = factorial * float(n)
-        return factorial;
-
-    def E1(self,x):
-        E1sum = 0
-        for k in range(1,100):
-            E1sum += pow((-1.),(k + 0.0)) * pow(x,(k + 0.0)) / ((k + 0.0) * self.factorial(k))
-        return -0.57721566490153286060 - np.log(x) - E1sum
- 
-    def ags(self):
-        # Select index for plant type
-        if(self.c3c4 == 'c3'):
-            c = 0
-        elif(self.c3c4 == 'c4'):
-            c = 1
-        else:
-            sys.exit('option \"%s\" for \"c3c4\" invalid'%self.c3c4)
-
-        # calculate CO2 compensation concentration
-        CO2comp       = self.CO2comp298[c] * self.rho * pow(self.Q10CO2[c],(0.1 * (self.thetasurf - 298.)))  
-
-        # calculate mesophyll conductance
-        gm            = self.gm298[c] *  pow(self.Q10gm[c],(0.1 * (self.thetasurf-298.))) \
-                          / ( (1. + np.exp(0.3 * (self.T1gm[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2gm[c]))))
-        gm            = gm / 1000. # conversion from mm s-1 to m s-1
-  
-        # calculate CO2 concentration inside the leaf (ci)
-        fmin0         = self.gmin[c] / self.nuco2q - 1. / 9. * gm
-        fmin          = -fmin0 + pow((pow(fmin0,2.) + 4 * self.gmin[c]/self.nuco2q * gm),0.5) / (2. * gm)
-  
-        Ds            = (esat(self.Ts) - self.e) / 1000. # kPa
-        D0            = (self.f0[c] - fmin) / self.ad[c]
-  
-        cfrac         = self.f0[c] * (1. - (Ds / D0)) + fmin * (Ds / D0)
-        co2abs        = self.CO2 * (self.mco2 / self.mair) * self.rho # conversion mumol mol-1 (ppm) to mgCO2 m3
-        ci            = cfrac * (co2abs - CO2comp) + CO2comp
-  
-        # calculate maximal gross primary production in high light conditions (Ag)
-        Ammax         = self.Ammax298[c] *  pow(self.Q10Am[c],(0.1 * (self.thetasurf - 298.))) / ( (1. + np.exp(0.3 * (self.T1Am[c] - self.thetasurf))) * (1. + np.exp(0.3 * (self.thetasurf - self.T2Am[c]))))
-  
-        # calculate effect of soil moisture stress on gross assimilation rate
-        betaw         = max(1e-3, min(1.,(self.w2 - self.wwilt)/(self.wfc - self.wwilt)))
-  
-        # calculate stress function
-        if (self.c_beta == 0):
-            fstr = betaw;
-        else:
-            # Following Combe et al (2016)
-            if (self.c_beta < 0.25):
-                P = 6.4 * self.c_beta
-            elif (self.c_beta < 0.50):
-                P = 7.6 * self.c_beta - 0.3
-            else:
-                P = 2**(3.66 * self.c_beta + 0.34) - 1
-            fstr = (1. - np.exp(-P * betaw)) / (1 - np.exp(-P))
-  
-        # calculate gross assimilation rate (Am)
-        Am           = Ammax * (1. - np.exp(-(gm * (ci - CO2comp) / Ammax)))
-        Rdark        = (1. / 9.) * Am
-        PAR          = 0.5 * max(1e-1,self.Swin * self.cveg)
-  
-        # calculate  light use efficiency
-        alphac       = self.alpha0[c] * (co2abs - CO2comp) / (co2abs + 2. * CO2comp)
-  
-        # calculate gross primary productivity
-        Ag           = (Am + Rdark) * (1 - np.exp(alphac * PAR / (Am + Rdark)))
-  
-        # 1.- calculate upscaling from leaf to canopy: net flow CO2 into the plant (An)
-        y            =  alphac * self.Kx[c] * PAR / (Am + Rdark)
-        An           = (Am + Rdark) * (1. - 1. / (self.Kx[c] * self.LAI) * (self.E1(y * np.exp(-self.Kx[c] * self.LAI)) - self.E1(y)))
-  
-        # 2.- calculate upscaling from leaf to canopy: CO2 conductance at canopy level
-        a1           = 1. / (1. - self.f0[c])
-        Dstar        = D0 / (a1 * (self.f0[c] - fmin))
-  
-        gcco2        = self.LAI * (self.gmin[c] / self.nuco2q + a1 * fstr * An / ((co2abs - CO2comp) * (1. + Ds / Dstar)))
-  
-        # calculate surface resistance for moisture and carbon dioxide
-        self.rs      = 1. / (1.6 * gcco2)
-        rsCO2        = 1. / gcco2
-  
-        # calculate net flux of CO2 into the plant (An)
-        An           = -(co2abs - ci) / (self.ra + rsCO2)
-  
-        # CO2 soil surface flux
-        fw           = self.Cw * self.wmax / (self.wg + self.wmin)
-        Resp         = self.R10 * (1. - fw) * np.exp(self.E0 / (283.15 * 8.314) * (1. - 283.15 / (self.Tsoil)))
-  
-        # CO2 flux
-        self.wCO2A   = An   * (self.mair / (self.rho * self.mco2))
-        self.wCO2R   = Resp * (self.mair / (self.rho * self.mco2))
-        self.wCO2    = self.wCO2A + self.wCO2R
- 
-    def run_land_surface(self):
-        # compute ra
-        ueff = np.sqrt(self.u ** 2. + self.v ** 2. + self.wstar**2.)
-        #print('ueff',self.u,self.v,self.wstar)
-
-        if(self.sw_sl):
-          self.ra = (self.Cs * ueff)**-1.
-        else:
-          self.ra = ueff / max(1.e-3, self.ustar)**2.
-
-        #print('ra',self.ra,self.ustar,ueff)
-
-        # first calculate essential thermodynamic variables
-        self.esat    = esat(self.theta)
-        self.qsat    = qsat(self.theta, self.Ps)
-        desatdT      = self.esat * (17.2694 / (self.theta - 35.86) - 17.2694 * (self.theta - 273.16) / (self.theta - 35.86)**2.)
-        self.dqsatdT = 0.622 * desatdT / self.Ps
-        self.e       = self.q * self.Ps / 0.622
-
-        if(self.ls_type == 'js'): 
-            self.jarvis_stewart() 
-        elif(self.ls_type == 'ags'):
-            self.ags()
-        else:
-            sys.exit('option \"%s\" for \"ls_type\" invalid'%self.ls_type)
-
-        # recompute f2 using wg instead of w2
-        if(self.wg > self.wwilt):# and self.w2 <= self.wfc):
-          f2          = (self.wfc - self.wwilt) / (self.wg - self.wwilt)
-        else:
-          f2        = 1.e8
-        self.rssoil = self.rssoilmin * f2 
- 
-        Wlmx = self.LAI * self.Wmax
-        #print('Wlmx',Wlmx,self.LAI,self.Wmax,self.Wl)
-        self.cliq = min(1., self.Wl / Wlmx) 
-     
-        # calculate skin temperature implictly
-        self.Ts   = (self.Q  + self.rho * self.cp / self.ra * self.theta \
-            + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs    ) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + (1. - self.cveg)             * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * self.theta - self.qsat + self.q) \
-            + self.cveg * self.cliq        * self.rho * self.Lv /  self.ra                * (self.dqsatdT * self.theta - self.qsat + self.q) + self.Lambda * self.Tsoil) \
-            / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
-            + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
-
-        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
-        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
-        #print('Ts',self.rs)
-
-        esatsurf      = esat(self.Ts)
-        self.qsatsurf = qsat(self.Ts, self.Ps)
-
-        self.LEveg  = (1. - self.cliq) * self.cveg * self.rho * self.Lv / (self.ra + self.rs) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEliq  = self.cliq * self.cveg * self.rho * self.Lv / self.ra * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-        self.LEsoil = (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * (self.dqsatdT * (self.Ts - self.theta) + self.qsat - self.q)
-  
-        self.Wltend      = - self.LEliq / (self.rhow * self.Lv)
-  
-        self.LE     = self.LEsoil + self.LEveg + self.LEliq
-        self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
-        #print('H',self.ra,self.Ts,self.theta)
-        self.G      = self.Lambda * (self.Ts - self.Tsoil)
-        self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
-        self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
-        
-        CG          = self.CGsat * (self.wsat / self.w2)**(self.b / (2. * np.log(10.)))
-  
-        self.Tsoiltend   = CG * self.G - 2. * np.pi / 86400. * (self.Tsoil - self.T2)
-   
-        d1          = 0.1
-        C1          = self.C1sat * (self.wsat / self.wg) ** (self.b / 2. + 1.)
-        C2          = self.C2ref * (self.w2 / (self.wsat - self.w2) )
-        wgeq        = self.w2 - self.wsat * self.a * ( (self.w2 / self.wsat) ** self.p * (1. - (self.w2 / self.wsat) ** (8. * self.p)) )
-        self.wgtend = - C1 / (self.rhow * d1) * self.LEsoil / self.Lv - C2 / 86400. * (self.wg - wgeq)
-  
-        # calculate kinematic heat fluxes
-        self.wtheta   = self.H  / (self.rho * self.cp)
-        #print('wtheta',self.wtheta,self.H,self.rho,self.cp)
-        self.wq       = self.LE / (self.rho * self.Lv)
- 
-    def integrate_land_surface(self):
-        # integrate soil equations
-        Tsoil0        = self.Tsoil
-        wg0           = self.wg
-        Wl0           = self.Wl
-  
-        self.Tsoil    = Tsoil0  + self.dtcur * self.Tsoiltend
-        self.wg       = wg0     + self.dtcur * self.wgtend
-        self.Wl       = Wl0     + self.dtcur * self.Wltend
-  
-    # store model output
-    def store(self):
-        t                      = self.t
-        
-        self.out.time[t]          = t * self.dt / 3600. + self.tstart
-
-        # in case we are at the end of the simulation, we store the vertical
-        # profiles to the output
-        
-        # if t == (len(self.out.time) - 1):
-        #     self.out.air_ac = self.air_ac
-        #     self.out.air_ap = self.air_ap
-
-        
-        # this way, we only need to define the output variables in the output class, so we don't need to specify het again here.
-        #  for key in self.out.__dict__.keys():
-        #      if key in self.__dict__:
-        #          self.out.__dict__[key][t]  = self.__dict__[key]
-        
-        self.out.h[t]          = self.h
-        
-        # HW20171003 note: most of these updates could also be done with the self.out.__dict__ and self.__dict__ , namely with the key-loop above:
-        
-        self.out.gammatheta[t] = self.gammatheta
-        self.out.gammau[t]     = self.gammau
-        self.out.gammav[t]     = self.gammav
-        self.out.gammaq[t]     = self.gammaq
-        self.out.theta[t]      = self.theta
-        self.out.thetav[t]     = self.thetav
-        self.out.dtheta[t]     = self.dtheta
-        self.out.dthetav[t]    = self.dthetav
-        self.out.wtheta[t]     = self.wtheta
-        self.out.wthetav[t]    = self.wthetav
-        self.out.wthetae[t]    = self.wthetae
-        self.out.wthetave[t]   = self.wthetave
-        
-        self.out.q[t]          = self.q
-        self.out.dq[t]         = self.dq
-        self.out.wq[t]         = self.wq
-        self.out.wqe[t]        = self.wqe
-        self.out.wqM[t]        = self.wqM
-      
-        self.out.qsat[t]       = self.qsat
-        self.out.e[t]          = self.e
-        self.out.esat[t]       = self.esat
-      
-        fac = (self.rho*self.mco2)/self.mair
-        self.out.CO2[t]        = self.CO2
-        self.out.dCO2[t]       = self.dCO2
-        self.out.wCO2[t]       = self.wCO2  * fac
-        self.out.wCO2e[t]      = self.wCO2e * fac
-        self.out.wCO2R[t]      = self.wCO2R * fac
-        self.out.wCO2A[t]      = self.wCO2A * fac
-
-        self.out.u[t]          = self.u
-        self.out.du[t]         = self.du
-        self.out.uw[t]         = self.uw
-        
-        self.out.v[t]          = self.v
-        self.out.dv[t]         = self.dv
-        self.out.vw[t]         = self.vw
-        
-        self.out.T2m[t]        = self.T2m
-        self.out.q2m[t]        = self.q2m
-        self.out.u2m[t]        = self.u2m
-        self.out.v2m[t]        = self.v2m
-        self.out.e2m[t]        = self.e2m
-        self.out.esat2m[t]     = self.esat2m
-
-
-        self.out.Tsoil[t]      = self.Tsoil
-        self.out.T2[t]         = self.T2
-        self.out.Ts[t]         = self.Ts
-        self.out.wg[t]         = self.wg
-        
-        self.out.thetasurf[t]  = self.thetasurf
-        self.out.thetavsurf[t] = self.thetavsurf
-        self.out.qsurf[t]      = self.qsurf
-        self.out.ustar[t]      = self.ustar
-        self.out.Cm[t]         = self.Cm
-        self.out.Cs[t]         = self.Cs
-        self.out.L[t]          = self.L
-        self.out.Rib[t]        = self.Rib
-  
-        self.out.Swin[t]       = self.Swin
-        self.out.Swout[t]      = self.Swout
-        self.out.Lwin[t]       = self.Lwin
-        self.out.Lwout[t]      = self.Lwout
-        self.out.Q[t]          = self.Q
-  
-        self.out.ra[t]         = self.ra
-        self.out.rs[t]         = self.rs
-        self.out.H[t]          = self.H
-        self.out.LE[t]         = self.LE
-        self.out.LEliq[t]      = self.LEliq
-        self.out.LEveg[t]      = self.LEveg
-        self.out.LEsoil[t]     = self.LEsoil
-        self.out.LEpot[t]      = self.LEpot
-        self.out.LEref[t]      = self.LEref
-        self.out.G[t]          = self.G
-
-        self.out.zlcl[t]       = self.lcl
-        self.out.RH_h[t]       = self.RH_h
-
-        self.out.ac[t]         = self.ac
-        self.out.M[t]          = self.M
-        self.out.dz[t]         = self.dz_h
-        self.out.substeps[t]   = self.substeps
-  
-    # delete class variables to facilitate analysis in ipython
-    def exitmodel(self):
-        del(self.Lv)
-        del(self.cp)
-        del(self.rho)
-        del(self.k)
-        del(self.g)
-        del(self.Rd)
-        del(self.Rv)
-        del(self.bolz)
-        del(self.S0)
-        del(self.rhow)
-  
-        del(self.t)
-        del(self.dt)
-        del(self.tsteps)
-         
-        del(self.h)          
-        del(self.Ps)        
-        del(self.fc)        
-        del(self.ws)
-        del(self.we)
-        
-        del(self.theta)
-        del(self.dtheta)
-        del(self.gammatheta)
-        del(self.advtheta)
-        del(self.beta)
-        del(self.wtheta)
-    
-        del(self.T2m)
-        del(self.q2m)
-        del(self.e2m)
-        del(self.esat2m)
-        del(self.u2m)
-        del(self.v2m)
-        
-        del(self.thetasurf)
-        del(self.qsatsurf)
-        del(self.thetav)
-        del(self.dthetav)
-        del(self.thetavsurf)
-        del(self.qsurf)
-        del(self.wthetav)
-        
-        del(self.q)
-        del(self.qsat)
-        del(self.dqsatdT)
-        del(self.e)
-        del(self.esat)
-        del(self.dq)
-        del(self.gammaq)
-        del(self.advq)
-        del(self.wq)
-        
-        del(self.u)
-        del(self.du)
-        del(self.gammau)
-        del(self.advu)
-        
-        del(self.v)
-        del(self.dv)
-        del(self.gammav)
-        del(self.advv)
-  
-        del(self.htend)
-        del(self.thetatend)
-        del(self.dthetatend)
-        del(self.qtend)
-        del(self.dqtend)
-        del(self.utend)
-        del(self.dutend)
-        del(self.vtend)
-        del(self.dvtend)
-     
-        del(self.Tsoiltend) 
-        del(self.wgtend)  
-        del(self.Wltend) 
-  
-        del(self.ustar)
-        del(self.uw)
-        del(self.vw)
-        del(self.z0m)
-        del(self.z0h)        
-        del(self.Cm)         
-        del(self.Cs)
-        del(self.L)
-        del(self.Rib)
-        del(self.ra)
-  
-        del(self.lat)
-        del(self.lon)
-        del(self.doy)
-        del(self.tstart)
-   
-        del(self.Swin)
-        del(self.Swout)
-        del(self.Lwin)
-        del(self.Lwout)
-        del(self.cc)
-  
-        del(self.wg)
-        del(self.w2)
-        del(self.cveg)
-        del(self.cliq)
-        del(self.Tsoil)
-        del(self.T2)
-        del(self.a)
-        del(self.b)
-        del(self.p)
-        del(self.CGsat)
-  
-        del(self.wsat)
-        del(self.wfc)
-        del(self.wwilt)
-  
-        del(self.C1sat)
-        del(self.C2ref)
-  
-        del(self.LAI)
-        del(self.rs)
-        del(self.rssoil)
-        del(self.rsmin)
-        del(self.rssoilmin)
-        del(self.alpha)
-        del(self.gD)
-  
-        del(self.Ts)
-  
-        del(self.Wmax)
-        del(self.Wl)
-  
-        del(self.Lambda)
-        
-        del(self.Q)
-        del(self.H)
-        del(self.LE)
-        del(self.LEliq)
-        del(self.LEveg)
-        del(self.LEsoil)
-        del(self.LEpot)
-        del(self.LEref)
-        del(self.G)
-  
-        del(self.sw_ls)
-        del(self.sw_rad)
-        del(self.sw_sl)
-        del(self.sw_wind)
-        del(self.sw_shearwe)
-
-# class for storing mixed-layer model output data
-class model_output:
-    def __init__(self, tsteps):
-        self.time          = np.zeros(tsteps)    # time [s]
-
-        # mixed-layer variables
-        self.h          = np.zeros(tsteps)    # ABL height [m]
-        
-        self.theta      = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammatheta = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammaq     = np.zeros(tsteps)    # initial mixed-layer potential temperature [K]
-        self.gammau     = np.zeros(tsteps)
-        self.gammav     = np.zeros(tsteps)
-        self.thetav     = np.zeros(tsteps)    # initial mixed-layer virtual potential temperature [K]
-        self.dtheta     = np.zeros(tsteps)    # initial potential temperature jump at h [K]
-        self.dthetav    = np.zeros(tsteps)    # initial virtual potential temperature jump at h [K]
-        self.wtheta     = np.zeros(tsteps)    # surface kinematic heat flux [K m s-1]
-        self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
-        self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
-        self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
-        
-        self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
-        self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
-        self.wq         = np.zeros(tsteps)    # surface kinematic moisture flux [kg kg-1 m s-1]
-        self.wqe        = np.zeros(tsteps)    # entrainment kinematic moisture flux [kg kg-1 m s-1]
-        self.wqM        = np.zeros(tsteps)    # cumulus mass-flux kinematic moisture flux [kg kg-1 m s-1]
-
-        self.qsat       = np.zeros(tsteps)    # mixed-layer saturated specific humidity [kg kg-1]
-        self.e          = np.zeros(tsteps)    # mixed-layer vapor pressure [Pa]
-        self.esat       = np.zeros(tsteps)    # mixed-layer saturated vapor pressure [Pa]
-
-        self.CO2        = np.zeros(tsteps)    # mixed-layer CO2 [ppm]
-        self.dCO2       = np.zeros(tsteps)    # initial CO2 jump at h [ppm]
-        self.wCO2       = np.zeros(tsteps)    # surface total CO2 flux [mgC m-2 s-1]
-        self.wCO2A      = np.zeros(tsteps)    # surface assimilation CO2 flux [mgC m-2 s-1]
-        self.wCO2R      = np.zeros(tsteps)    # surface respiration CO2 flux [mgC m-2 s-1]
-        self.wCO2e      = np.zeros(tsteps)    # entrainment CO2 flux [mgC m-2 s-1]
-        self.wCO2M      = np.zeros(tsteps)    # CO2 mass flux [mgC m-2 s-1]
-        
-        self.u          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.du         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.uw         = np.zeros(tsteps)    # surface momentum flux u [m2 s-2]
-        
-        self.v          = np.zeros(tsteps)    # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = np.zeros(tsteps)    # initial u-wind jump at h [m s-1]
-        self.vw         = np.zeros(tsteps)    # surface momentum flux v [m2 s-2]
-
-        # diagnostic meteorological variables
-        self.T2m        = np.zeros(tsteps)    # 2m temperature [K]   
-        self.q2m        = np.zeros(tsteps)    # 2m specific humidity [kg kg-1]
-        self.u2m        = np.zeros(tsteps)    # 2m u-wind [m s-1]    
-        self.v2m        = np.zeros(tsteps)    # 2m v-wind [m s-1]    
-        self.e2m        = np.zeros(tsteps)    # 2m vapor pressure [Pa]
-        self.esat2m     = np.zeros(tsteps)    # 2m saturated vapor pressure [Pa]
-
-        # ground variables
-        self.Tsoil       = np.zeros(tsteps)
-        self.T2          = np.zeros(tsteps)
-        self.Ts          = np.zeros(tsteps)
-        self.wg          = np.zeros(tsteps)
-
-        # surface-layer variables
-        self.thetasurf  = np.zeros(tsteps)    # surface potential temperature [K]
-        self.thetavsurf = np.zeros(tsteps)    # surface virtual potential temperature [K]
-        self.qsurf      = np.zeros(tsteps)    # surface specific humidity [kg kg-1]
-        self.ustar      = np.zeros(tsteps)    # surface friction velocity [m s-1]
-        self.z0m        = np.zeros(tsteps)    # roughness length for momentum [m]
-        self.z0h        = np.zeros(tsteps)    # roughness length for scalars [m]
-        self.Cm         = np.zeros(tsteps)    # drag coefficient for momentum []
-        self.Cs         = np.zeros(tsteps)    # drag coefficient for scalars []
-        self.L          = np.zeros(tsteps)    # Obukhov length [m]
-        self.Rib        = np.zeros(tsteps)    # bulk Richardson number [-]
-
-        # radiation variables
-        self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
-        self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
-        self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
-        self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
-        self.Q          = np.zeros(tsteps)    # net radiation [W m-2]
-
-        # land surface variables
-        self.ra         = np.zeros(tsteps)    # aerodynamic resistance [s m-1]
-        self.rs         = np.zeros(tsteps)    # surface resistance [s m-1]
-        self.H          = np.zeros(tsteps)    # sensible heat flux [W m-2]
-        self.LE         = np.zeros(tsteps)    # evapotranspiration [W m-2]
-        self.LEliq      = np.zeros(tsteps)    # open water evaporation [W m-2]
-        self.LEveg      = np.zeros(tsteps)    # transpiration [W m-2]
-        self.LEsoil     = np.zeros(tsteps)    # soil evaporation [W m-2]
-        self.LEpot      = np.zeros(tsteps)    # potential evaporation [W m-2]
-        self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
-        self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
-
-        # Mixed-layer top variables
-        self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
-        self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
-
-        # cumulus variables
-        self.ac         = np.zeros(tsteps)    # cloud core fraction [-]
-        self.M          = np.zeros(tsteps)    # cloud core mass flux [m s-1]
-        self.dz         = np.zeros(tsteps)    # transition layer thickness [m]
-        
-        
-        self.substeps   = np.zeros(tsteps)    # number of additional substep time integrations needed [-]
-
-# class for storing mixed-layer model input data
-class model_input:
-    def __init__(self):
-
-        # # comment not valid
-        # we comment out the initialization, because there is a problem when
-        # inheriting values from one the another class4gl_iput. We also expect
-        # that the user specifies all the required parmameters (if not, an error
-        # is raised). 
-
-        # general model variables
-        self.runtime    = None  # duration of model run [s]
-        self.dt         = None  # time step [s]
-
-        # mixed-layer variables
-        self.sw_ml      = None  # mixed-layer model switch
-        self.sw_shearwe = None  # Shear growth ABL switch
-        self.sw_fixft   = None  # Fix the free-troposphere switch
-        self.h          = None  # initial ABL height [m]
-        self.Ps         = None  # surface pressure [Pa]
-        self.divU       = None  # horizontal large-scale divergence of wind [s-1]
-        self.fc         = None  # Coriolis parameter [s-1]
-        
-        self.theta      = None  # initial mixed-layer potential temperature [K]
-        #self.air_ap.THTA  = None  # optional/initial profile of potential temperature [K]
-
-        #self.z_pro      = None  # height coordinate of the optional input profiles [m]
-
-        self.dtheta     = None  # initial temperature jump at h [K]
-        self.gammatheta = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advtheta   = None  # advection of heat [K s-1]
-        self.beta       = None  # entrainment ratio for virtual heat [-]
-        self.wtheta     = None  # surface kinematic heat flux [K m s-1]
-        
-        self.q          = None  # initial mixed-layer specific humidity [kg kg-1]
-        #self.q_pro      = None  # optional/initial profile of specific humidity [kg kg-1]
-        #self.p_pro      = None  # optional/initial profile of pressure, just for diagnosis purposes [Pa]
-
-        self.dq         = None  # initial specific humidity jump at h [kg kg-1]
-        self.gammaq     = None  # free atmosphere specific humidity lapse rate [kg kg-1 m-1]
-        self.advq       = None  # advection of moisture [kg kg-1 s-1]
-        self.wq         = None  # surface kinematic moisture flux [kg kg-1 m s-1]
-
-        self.CO2        = None  # initial mixed-layer potential temperature [K]
-        self.dCO2       = None  # initial temperature jump at h [K]
-        self.gammaCO2   = None  # free atmosphere potential temperature lapse rate [K m-1]
-        self.advCO2     = None  # advection of heat [K s-1]
-        self.wCO2       = None  # surface kinematic heat flux [K m s-1]
-        
-        self.sw_wind    = None  # prognostic wind switch
-        self.u          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.du         = None  # initial u-wind jump at h [m s-1]
-        self.gammau     = None  # free atmosphere u-wind speed lapse rate [s-1]
-        self.advu       = None  # advection of u-wind [m s-2]
-
-        self.v          = None  # initial mixed-layer u-wind speed [m s-1]
-        self.dv         = None  # initial u-wind jump at h [m s-1]
-        self.gammav     = None  # free atmosphere v-wind speed lapse rate [s-1]
-        self.advv       = None  # advection of v-wind [m s-2]
-
-        # surface layer variables
-        self.sw_sl      = None  # surface layer switch
-        self.ustar      = None  # surface friction velocity [m s-1]
-        self.z0m        = None  # roughness length for momentum [m]
-        self.z0h        = None  # roughness length for scalars [m]
-        self.Cm         = None  # drag coefficient for momentum [-]
-        self.Cs         = None  # drag coefficient for scalars [-]
-        self.L          = None  # Obukhov length [-]
-        self.Rib        = None  # bulk Richardson number [-]
-
-        # radiation parameters
-        self.sw_rad     = None  # radiation switch
-        self.lat        = None  # latitude [deg]
-        self.lon        = None  # longitude [deg]
-        self.doy        = None  # day of the year [-]
-        self.tstart     = None  # time of the day [h UTC]
-        self.cc         = None  # cloud cover fraction [-]
-        self.Q          = None  # net radiation [W m-2] 
-        self.dFz        = None  # cloud top radiative divergence [W m-2] 
-
-        # land surface parameters
-        self.sw_ls      = None  # land surface switch
-        self.ls_type    = None  # land-surface parameterization ('js' for Jarvis-Stewart or 'ags' for A-Gs)
-        self.wg         = None  # volumetric water content top soil layer [m3 m-3]
-        self.w2         = None  # volumetric water content deeper soil layer [m3 m-3]
-        self.Tsoil      = None  # temperature top soil layer [K]
-        self.T2         = None  # temperature deeper soil layer [K]
-        
-        self.a          = None  # Clapp and Hornberger retention curve parameter a
-        self.b          = None  # Clapp and Hornberger retention curve parameter b
-        self.p          = None  # Clapp and Hornberger retention curve parameter p 
-        self.CGsat      = None  # saturated soil conductivity for heat
-        
-        self.wsat       = None  # saturated volumetric water content ECMWF config [-]
-        self.wfc        = None  # volumetric water content field capacity [-]
-        self.wwilt      = None  # volumetric water content wilting point [-]
-        
-        self.C1sat      = None 
-        self.C2ref      = None
-
-        self.c_beta     = None  # Curvatur plant water-stress factor (0..1) [-]
-        
-        self.LAI        = None  # leaf area index [-]
-        self.gD         = None  # correction factor transpiration for VPD [-]
-        self.rsmin      = None  # minimum resistance transpiration [s m-1]
-        self.rssoilmin  = None  # minimum resistance soil evaporation [s m-1]
-        self.alpha      = None  # surface albedo [-]
-        
-        self.Ts         = None  # initial surface temperature [K]
-        
-        self.cveg       = None  # vegetation fraction [-]
-        self.Wmax       = None  # thickness of water layer on wet vegetation [m]
-        self.Wl         = None  # equivalent water layer depth for wet vegetation [m]
-        
-        self.Lambda     = None  # thermal diffusivity skin layer [-]
-
-        # A-Gs parameters
-        self.c3c4       = None  # Plant type ('c3' or 'c4')
-
-        # Cumulus parameters
-        self.sw_cu      = None  # Cumulus parameterization switch
-        self.dz_h       = None  # Transition layer thickness [m]
-        
-# BEGIN -- HW 20171027
-        # self.cala       = None      # soil heat conductivity [W/(K*m)]
-        # self.crhoc      = None      # soil heat capacity  [J/K*m**3]
-# END -- HW 20171027
diff --git a/dist/class4gl-0.1dev/setup.py b/dist/class4gl-0.1dev/setup.py
deleted file mode 100644
index a806fa0..0000000
--- a/dist/class4gl-0.1dev/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-
-setup(
-        name='class4gl',
-        version='0.1dev',
-        packages=['lib','bin'],
-        license='GPLv3 licence',
-        long_description=open('README.md').read(),
-)

From 44874f7d5ef7616783fbc913920c002209271d3e Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 18:40:21 +0200
Subject: [PATCH 021/129] get rid of skilearn dependency

---
 class4gl/evaluation/evaluation.py | 31 +++++++++++++++++--------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 75b3171..4b8c232 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -2,13 +2,24 @@
 
 import pandas as pd
 import sys
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load-globaldata',default=False)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
 from interface_multi import c4gl_interface_soundings,get_record_yaml
 from class4gl import class4gl_input, data_global,class4gl,units
-from sklearn.metrics import mean_squared_error
+#from sklearn.metrics import mean_squared_error
 import matplotlib as mpl
 import matplotlib.pyplot as plt
-import seaborn.apionly as sns
+#import seaborn.apionly as sns
 import pylab as pl
 import numpy as np
 import matplotlib.pyplot as plt
@@ -19,16 +30,7 @@
 # import importlib
 # importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
 
-import argparse
 
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--experiments')
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load-globaldata',default=False)
-args = parser.parse_args()
 
 
 def abline(slope, intercept,axis):
@@ -76,8 +78,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # which is the case for evaluating eg., mixed-layer estimates)
         y_predicted_temp = y_actual_temp*0. + y_predicted_temp
         
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
 
 
 

From 7e580428cad165edb8ce10ce136475614a5701f3 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 18:47:01 +0200
Subject: [PATCH 022/129] test

---
 class4gl/evaluation/evaluation.py | 31 +++++++++++++++++--------------
 class4gl/interface_multi.py       |  2 +-
 2 files changed, 18 insertions(+), 15 deletions(-)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 75b3171..4b8c232 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -2,13 +2,24 @@
 
 import pandas as pd
 import sys
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load-globaldata',default=False)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
 from interface_multi import c4gl_interface_soundings,get_record_yaml
 from class4gl import class4gl_input, data_global,class4gl,units
-from sklearn.metrics import mean_squared_error
+#from sklearn.metrics import mean_squared_error
 import matplotlib as mpl
 import matplotlib.pyplot as plt
-import seaborn.apionly as sns
+#import seaborn.apionly as sns
 import pylab as pl
 import numpy as np
 import matplotlib.pyplot as plt
@@ -19,16 +30,7 @@
 # import importlib
 # importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
 
-import argparse
 
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--experiments')
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load-globaldata',default=False)
-args = parser.parse_args()
 
 
 def abline(slope, intercept,axis):
@@ -76,8 +78,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # which is the case for evaluating eg., mixed-layer estimates)
         y_predicted_temp = y_actual_temp*0. + y_predicted_temp
         
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
 
 
 
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 6f70487..a9f6ce8 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -8,7 +8,7 @@
 from time import sleep
 
 
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+# sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl,units
 from interface_functions import *
 # from data_soundings import wyoming

From ed9bf770bf48fcb9a7759803bf082a862741d456 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 18:53:12 +0200
Subject: [PATCH 023/129] add eval report

---
 class4gl/evaluation/taylorDiagram.py | 246 +++++++++++++++++++++++++++
 1 file changed, 246 insertions(+)
 create mode 100644 class4gl/evaluation/taylorDiagram.py

diff --git a/class4gl/evaluation/taylorDiagram.py b/class4gl/evaluation/taylorDiagram.py
new file mode 100644
index 0000000..9c51b48
--- /dev/null
+++ b/class4gl/evaluation/taylorDiagram.py
@@ -0,0 +1,246 @@
+#!/usr/bin/env python
+# Copyright: This document has been placed in the public domain.
+
+"""
+Taylor diagram (Taylor, 2001) implementation.
+"""
+
+__version__ = "Time-stamp: <2018-05-17 19:41:54 ycopin>"
+__author__ = "Yannick Copin "
+
+import numpy as NP
+import matplotlib.pyplot as PLT
+
+
+class TaylorDiagram(object):
+    """
+    Taylor diagram.
+
+    Plot model standard deviation and correlation to reference (data)
+    sample in a single-quadrant polar plot, with r=stddev and
+    theta=arccos(correlation).
+    """
+
+    def __init__(self, refstd,
+                 fig=None, rect=111, label='_', srange=(0, 1.5), extend=False):
+        """
+        Set up Taylor diagram axes, i.e. single quadrant polar
+        plot, using `mpl_toolkits.axisartist.floating_axes`.
+
+        Parameters:
+
+        * refstd: reference standard deviation to be compared to
+        * fig: input Figure or None
+        * rect: subplot definition
+        * label: reference label
+        * srange: stddev axis extension, in units of *refstd*
+        * extend: extend diagram to negative correlations
+        """
+
+        from matplotlib.projections import PolarAxes
+        import mpl_toolkits.axisartist.floating_axes as FA
+        import mpl_toolkits.axisartist.grid_finder as GF
+
+        self.refstd = refstd            # Reference standard deviation
+
+        tr = PolarAxes.PolarTransform()
+
+        # Correlation labels
+        rlocs = NP.array([0, 0.2, 0.4, 0.6, 0.7, 0.8, 0.9, 0.95, 0.99, 1])
+        if extend:
+            # Diagram extended to negative correlations
+            self.tmax = NP.pi
+            rlocs = NP.concatenate((-rlocs[:0:-1], rlocs))
+        else:
+            # Diagram limited to positive correlations
+            self.tmax = NP.pi/2
+        tlocs = NP.arccos(rlocs)        # Conversion to polar angles
+        gl1 = GF.FixedLocator(tlocs)    # Positions
+        tf1 = GF.DictFormatter(dict(zip(tlocs, map(str, rlocs))))
+
+        # Standard deviation axis extent (in units of reference stddev)
+        self.smin = srange[0] * self.refstd
+        self.smax = srange[1] * self.refstd
+
+        ghelper = FA.GridHelperCurveLinear(
+            tr,
+            extremes=(0, self.tmax, self.smin, self.smax),
+            grid_locator1=gl1, tick_formatter1=tf1)
+
+        if fig is None:
+            fig = PLT.figure()
+
+        ax = FA.FloatingSubplot(fig, rect, grid_helper=ghelper)
+        fig.add_subplot(ax)
+
+        # Adjust axes
+        ax.axis["top"].set_axis_direction("bottom")   # "Angle axis"
+        ax.axis["top"].toggle(ticklabels=True, label=True)
+        ax.axis["top"].major_ticklabels.set_axis_direction("top")
+        ax.axis["top"].label.set_axis_direction("top")
+        ax.axis["top"].label.set_text("Correlation")
+
+        ax.axis["left"].set_axis_direction("bottom")  # "X axis"
+        #ax.axis["left"].label.set_text("Standard deviation (model)/ Observed (observations)")
+
+        ax.axis["right"].set_axis_direction("top")    # "Y-axis"
+        ax.axis["right"].toggle(ticklabels=True)
+        ax.axis["right"].major_ticklabels.set_axis_direction(
+            "bottom" if extend else "left")
+
+        ax.axis["bottom"].set_visible(False)          # Unused
+
+        self._ax = ax                   # Graphical axes
+        self.ax = ax.get_aux_axes(tr)   # Polar coordinates
+        # # DOESNT WORK!!!!
+        # ax.axes.set_xticks([0.,0.2,0.4,1.])
+        # ax.axes.set_yticks([0.,0.2,0.4,1.])
+
+        # Add reference point and stddev contour
+        l, = self.ax.plot([0], self.refstd, 'k*',
+                          ls='', ms=10, label=label)
+        t = NP.linspace(0, self.tmax)
+        r = NP.zeros_like(t) + self.refstd
+        self.ax.plot(t, r, 'k--', label='_')
+
+        # Collect sample points for latter use (e.g. legend)
+        self.samplePoints = [l]
+
+    def add_sample(self, stddev, corrcoef, *args, **kwargs):
+        """
+        Add sample (*stddev*, *corrcoeff*) to the Taylor
+        diagram. *args* and *kwargs* are directly propagated to the
+        `Figure.plot` command.
+        """
+
+        l, = self.ax.plot(NP.arccos(corrcoef), stddev,
+                          *args, **kwargs)  # (theta, radius)
+        self.samplePoints.append(l)
+
+        return l
+
+    def add_grid(self, *args, **kwargs):
+        """Add a grid."""
+
+        self._ax.grid(*args, **kwargs)
+
+    def add_contours(self, levels=5, **kwargs):
+        """
+        Add constant centered RMS difference contours, defined by *levels*.
+        """
+
+        rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),
+                             NP.linspace(0, self.tmax))
+        # Compute centered RMS difference
+        rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))
+
+        contours = self.ax.contour(ts, rs, rms, levels, **kwargs)
+
+        return contours
+
+
+def test1():
+    """Display a Taylor diagram in a separate axis."""
+
+    # Reference dataset
+    x = NP.linspace(0, 4*NP.pi, 100)
+    data = NP.sin(x)
+    refstd = data.std(ddof=1)           # Reference standard deviation
+
+    # Generate models
+    m1 = data + 0.2*NP.random.randn(len(x))     # Model 1
+    m2 = 0.8*data + .1*NP.random.randn(len(x))  # Model 2
+    m3 = NP.sin(x-NP.pi/10)                     # Model 3
+
+    # Compute stddev and correlation coefficient of models
+    samples = NP.array([ [m.std(ddof=1), NP.corrcoef(data, m)[0, 1]]
+                         for m in (m1, m2, m3)])
+
+    fig = PLT.figure(figsize=(10, 4))
+
+    ax1 = fig.add_subplot(1, 2, 1, xlabel='X', ylabel='Y')
+    # Taylor diagram
+    dia = TaylorDiagram(refstd, fig=fig, rect=122, label="Reference")
+
+    colors = PLT.matplotlib.cm.jet(NP.linspace(0, 1, len(samples)))
+
+    ax1.plot(x, data, 'ko', label='Data')
+    for i, m in enumerate([m1, m2, m3]):
+        ax1.plot(x, m, c=colors[i], label='Model %d' % (i+1))
+    ax1.legend(numpoints=1, prop=dict(size='small'), loc='best')
+
+    # Add the models to Taylor diagram
+    for i, (stddev, corrcoef) in enumerate(samples):
+        dia.add_sample(stddev, corrcoef,
+                       marker='$%d$' % (i+1), ms=10, ls='',
+                       mfc=colors[i], mec=colors[i],
+                       label="Model %d" % (i+1))
+
+    # Add grid
+    dia.add_grid()
+
+    # Add RMS contours, and label them
+    contours = dia.add_contours(colors='0.5')
+    PLT.clabel(contours, inline=1, fontsize=10, fmt='%.2f')
+
+    # Add a figure legend
+    fig.legend(dia.samplePoints,
+               [ p.get_label() for p in dia.samplePoints ],
+               numpoints=1, prop=dict(size='small'), loc='upper right')
+
+    return dia
+
+
+def test2():
+    """
+    Climatology-oriented example (after iteration w/ Michael A. Rawlins).
+    """
+
+    # Reference std
+    stdref = 48.491
+
+    # Samples std,rho,name
+    samples = [[25.939, 0.385, "Model A"],
+               [29.593, 0.509, "Model B"],
+               [33.125, 0.585, "Model C"],
+               [29.593, 0.509, "Model D"],
+               [71.215, 0.473, "Model E"],
+               [27.062, 0.360, "Model F"],
+               [38.449, 0.342, "Model G"],
+               [35.807, 0.609, "Model H"],
+               [17.831, 0.360, "Model I"]]
+
+    fig = PLT.figure()
+
+    dia = TaylorDiagram(stdref, fig=fig, label='Reference', extend=True)
+    dia.samplePoints[0].set_color('r')  # Mark reference point as a red star
+
+    # Add models to Taylor diagram
+    for i, (stddev, corrcoef, name) in enumerate(samples):
+        dia.add_sample(stddev, corrcoef,
+                       marker='$%d$' % (i+1), ms=10, ls='',
+                       mfc='k', mec='k',
+                       label=name)
+
+    # Add RMS contours, and label them
+    contours = dia.add_contours(levels=5, colors='0.5')  # 5 levels in grey
+    PLT.clabel(contours, inline=1, fontsize=10, fmt='%.0f')
+
+    dia.add_grid()                                  # Add grid
+    dia._ax.axis[:].major_ticks.set_tick_out(True)  # Put ticks outward
+
+    # Add a figure legend and title
+    fig.legend(dia.samplePoints,
+               [ p.get_label() for p in dia.samplePoints ],
+               numpoints=1, prop=dict(size='small'), loc='upper right')
+    fig.suptitle("Taylor diagram", size='x-large')  # Figure title
+
+    return dia
+
+
+if __name__ == '__main__':
+
+    dia = test1()
+    dia = test2()
+
+    PLT.show()

From 2f7e875822cfa4e859247b6578f44c0091dcabd4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:03:27 +0200
Subject: [PATCH 024/129] evaluation

---
 class4gl/evaluation/evaluation.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 4b8c232..8cbc2cb 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -150,7 +150,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     #dia.ax.plot(x99,y99,color='k')
 
     
-    for ikey,key in enumerate(EXPS.keys()):
+    for ikey,key in enumerate(args.experiments.split(';')):
         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
         # clearsky = (cc < 0.05)
         # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
@@ -194,8 +194,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 i = 0
 for varkey in ['h','theta','q']:                                                    
-    ikey = 2
-    key = list(EXPS.keys())[ikey]
+    ikey = 0
+    key = list(args.experiments.split(';'))[ikey]
     cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
     clearsky = (cc < 0.05)
 
@@ -260,12 +260,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 # legend for different forcing simulations (colors)
 ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
 leg = []
-for ikey,key in enumerate(EXPS.keys()):
+for ikey,key in enumerate(args.experiments.split(';')):
     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
     leg.append(leg1)
 ax.axis('off')
 #leg1 =
-ax.legend(leg,list(EXPS.keys()),loc=2,fontsize=10)
+ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
 
 
 # # legend for different stations (symbols)

From d9594ed6b29937198426e544cddf96828907ff0f Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:11:39 +0200
Subject: [PATCH 025/129] tkagg

---
 class4gl/evaluation/evaluation.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 8cbc2cb..902882e 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -29,6 +29,8 @@
 from matplotlib import ticker
 # import importlib
 # importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+import matplotlib
+matplotlib.use('TkAgg')
 
 
 

From 568a3ab2c25b61b1396afb0cd9e9e056ce1c4797 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:14:27 +0200
Subject: [PATCH 026/129] tkagg

---
 class4gl/evaluation/evaluation.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 902882e..12c9e48 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -3,6 +3,8 @@
 import pandas as pd
 import sys
 
+import matplotlib
+matplotlib.use('TkAgg')
 import argparse
 parser = argparse.ArgumentParser()
 parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
@@ -29,8 +31,6 @@
 from matplotlib import ticker
 # import importlib
 # importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
-import matplotlib
-matplotlib.use('TkAgg')
 
 
 

From d6f72e617a9b31c90c5fc977ec9cb76c2b0573be Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:16:28 +0200
Subject: [PATCH 027/129] tkagg

---
 class4gl/evaluation/evaluation.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 12c9e48..742bc9b 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -5,6 +5,7 @@
 
 import matplotlib
 matplotlib.use('TkAgg')
+
 import argparse
 parser = argparse.ArgumentParser()
 parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')

From 25f852a201a00e03e3d989d069ed3c000e3e54d2 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:22:49 +0200
Subject: [PATCH 028/129] evaluation

---
 class4gl/evaluation/evaluation.py | 350 +++++++++++++++---------------
 1 file changed, 175 insertions(+), 175 deletions(-)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/evaluation/evaluation.py
index 742bc9b..7becde2 100644
--- a/class4gl/evaluation/evaluation.py
+++ b/class4gl/evaluation/evaluation.py
@@ -118,184 +118,184 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-fig = plt.figure(figsize=(10,7))   #width,height
-i = 1                                                                           
-axes = {}         
-axes_taylor = {}         
-
-colors = ['r','g','b','m']
-symbols = ['*','x','+']
-dias = {}
-
-for varkey in ['h','theta','q']:                                                    
-    axes[varkey] = fig.add_subplot(2,3,i)                                       
-    #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
-
-    #print(obs.std())
-    dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-    if i == 0:
-        print("hello")
-        dias[varkey]._ax.axis["left"].label.set_text(\
-            "Standard deviation (model) / Standard deviation (observations)")
-        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
-    #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-    # Q95 = obs.quantile(0.95)
-    # Q95 = obs.quantile(0.90)
-    # Add RMS contours, and label them
-    contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-    dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
-    #dia._ax.set_title(season.capitalize())
-
-    dias[varkey].add_grid()
-
-
-    #dia.ax.plot(x99,y99,color='k')
-
-    
-    for ikey,key in enumerate(args.experiments.split(';')):
-        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-        # clearsky = (cc < 0.05)
-        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-        x, y = obs.values,mod.values
-        print(key,len(obs.values))
-
-        STD_OBS = obs.std()
-        #scores
-        PR = pearsonr(mod,obs)[0]
-        RMSE = rmse(obs,mod)                                               
-        BIAS = np.mean(mod) - np.mean(obs)
-        STD = mod.std()
-        
-        # fit = np.polyfit(x,y,deg=1)
-        # axes[varkey].plot(x, fit[0] * x + fit[1],\
-        #                   color=colors[ikey],alpha=0.8,lw=2,\
-        #                   label=key+", "+\
-        #                               'R = '+str(round(PR,3))+', '+\
-        #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-        #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-        # axes[varkey].legend(fontsize=5)
-        
-        # print(STD)
-        # print(PR)
-        dias[varkey].add_sample(STD/STD_OBS, PR,
-                       marker='o', ms=5, ls='',
-                       #mfc='k', mec='k', # B&W
-                       mfc=colors[ikey], mec=colors[ikey], # Colors
-                       label=key)
-
-    # put ticker position, see
-    # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
-    # dia.ax.axis['bottom'].
-    # dia.ax.axis['left'].
-    # dia.ax.axis['left'].
-
-    i += 1
-
-i = 0
-for varkey in ['h','theta','q']:                                                    
-    ikey = 0
-    key = list(args.experiments.split(';'))[ikey]
-    cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-    clearsky = (cc < 0.05)
-
-    mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-    obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-
-
-    nbins=40       
-    x, y = obs.values,mod.values
-    
-    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
-    zi = np.zeros_like(xi)*np.nan       
-    for ibin in range(nbins):
-        xmin = x.min() + ibin * (x.max() - x.min())/nbins
-        xmax = xmin + (x.max() - x.min())/nbins
-        in_bin = ((x >= xmin) & (x < xmax))
-        ybin = y[in_bin]
-        xbin = x[in_bin]
-        if len(ybin) > 20:
-            k = kde.gaussian_kde((ybin))
-            zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
-    zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
-    zi_int = zi.cumsum(axis=1) 
-                 #  label=key+", "+\
-                 #                    'R = '+str(round(PR[0],3))+', '+\
-                 #                    'RMSE = '+str(round(RMSE,5))+', '+\
-                 #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-    axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
-            colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
-    axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
-            colors=['darkred'],alpha=0.5,)
-
-
-    latex = {}
-    latex['dthetadt'] =  r'$d \theta / dt $'
-    latex['dqdt'] =      r'$d q / dt $'
-    latex['dhdt'] =      r'$d h / dt $'
-
-    axes[varkey].set_xlabel('observations')     
-    axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-
-    PR = pearsonr(mod,obs)[0]
-    RMSE = rmse(obs,mod)                                               
-    BIAS = np.mean(mod) - np.mean(obs)
-    STD = mod.std()
-
-    axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
-                                  'R = '+str(round(PR,3))+', '+\
-                                  'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-                                  'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
-                         s=0.1,alpha=0.14,color='k')
-    axes[varkey].legend(fontsize=5)
-                   
-    axes[varkey].set_xlabel('observations')     
-    if i==0:                                    
-        axes[varkey].set_ylabel('model')                                            
-    abline(1,0,axis=axes[varkey])
-    i +=1
-
-
-
-# legend for different forcing simulations (colors)
-ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-leg = []
-for ikey,key in enumerate(args.experiments.split(';')):
-    leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
-    leg.append(leg1)
-ax.axis('off')
-#leg1 =
-ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
-
-
-# # legend for different stations (symbols)
-# ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# # the lines below activate TaylorPlots but it is disabled for now
+# fig = plt.figure(figsize=(10,7))   #width,height
+# i = 1                                                                           
+# axes = {}         
+# axes_taylor = {}         
+# 
+# colors = ['r','g','b','m']
+# symbols = ['*','x','+']
+# dias = {}
+# 
+# for varkey in ['h','theta','q']:                                                    
+#     axes[varkey] = fig.add_subplot(2,3,i)                                       
+#     #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+# 
+#     #print(obs.std())
+#     dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+#     if i == 0:
+#         dias[varkey]._ax.axis["left"].label.set_text(\
+#             "Standard deviation (model) / Standard deviation (observations)")
+#         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+#     #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#     # Q95 = obs.quantile(0.95)
+#     # Q95 = obs.quantile(0.90)
+#     # Add RMS contours, and label them
+#     contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+#     dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+#     #dia._ax.set_title(season.capitalize())
+# 
+#     dias[varkey].add_grid()
+# 
+# 
+#     #dia.ax.plot(x99,y99,color='k')
+# 
+#     
+#     for ikey,key in enumerate(args.experiments.split(';')):
+#         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+#         # clearsky = (cc < 0.05)
+#         # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+#         # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+#         mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+#         obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+#         x, y = obs.values,mod.values
+#         print(key,len(obs.values))
+# 
+#         STD_OBS = obs.std()
+#         #scores
+#         PR = pearsonr(mod,obs)[0]
+#         RMSE = rmse(obs,mod)                                               
+#         BIAS = np.mean(mod) - np.mean(obs)
+#         STD = mod.std()
+#         
+#         # fit = np.polyfit(x,y,deg=1)
+#         # axes[varkey].plot(x, fit[0] * x + fit[1],\
+#         #                   color=colors[ikey],alpha=0.8,lw=2,\
+#         #                   label=key+", "+\
+#         #                               'R = '+str(round(PR,3))+', '+\
+#         #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+#         #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+#         # axes[varkey].legend(fontsize=5)
+#         
+#         # print(STD)
+#         # print(PR)
+#         dias[varkey].add_sample(STD/STD_OBS, PR,
+#                        marker='o', ms=5, ls='',
+#                        #mfc='k', mec='k', # B&W
+#                        mfc=colors[ikey], mec=colors[ikey], # Colors
+#                        label=key)
+# 
+#     # put ticker position, see
+#     # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+#     # dia.ax.axis['bottom'].
+#     # dia.ax.axis['left'].
+#     # dia.ax.axis['left'].
+# 
+#     i += 1
+# 
+# i = 0
+# for varkey in ['h','theta','q']:                                                    
+#     ikey = 0
+#     key = list(args.experiments.split(';'))[ikey]
+#     cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+#     clearsky = (cc < 0.05)
+# 
+#     mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+#     obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+# 
+# 
+#     nbins=40       
+#     x, y = obs.values,mod.values
+#     
+#     xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+#     zi = np.zeros_like(xi)*np.nan       
+#     for ibin in range(nbins):
+#         xmin = x.min() + ibin * (x.max() - x.min())/nbins
+#         xmax = xmin + (x.max() - x.min())/nbins
+#         in_bin = ((x >= xmin) & (x < xmax))
+#         ybin = y[in_bin]
+#         xbin = x[in_bin]
+#         if len(ybin) > 20:
+#             k = kde.gaussian_kde((ybin))
+#             zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+#     zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+#     zi_int = zi.cumsum(axis=1) 
+#                  #  label=key+", "+\
+#                  #                    'R = '+str(round(PR[0],3))+', '+\
+#                  #                    'RMSE = '+str(round(RMSE,5))+', '+\
+#                  #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+#     axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+#             colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+#     axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+#             colors=['darkred'],alpha=0.5,)
+# 
+# 
+#     latex = {}
+#     latex['dthetadt'] =  r'$d \theta / dt $'
+#     latex['dqdt'] =      r'$d q / dt $'
+#     latex['dhdt'] =      r'$d h / dt $'
+# 
+#     axes[varkey].set_xlabel('observations')     
+#     axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+# 
+#     PR = pearsonr(mod,obs)[0]
+#     RMSE = rmse(obs,mod)                                               
+#     BIAS = np.mean(mod) - np.mean(obs)
+#     STD = mod.std()
+# 
+#     axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+#                                   'R = '+str(round(PR,3))+', '+\
+#                                   'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+#                                   'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+#                          s=0.1,alpha=0.14,color='k')
+#     axes[varkey].legend(fontsize=5)
+#                    
+#     axes[varkey].set_xlabel('observations')     
+#     if i==0:                                    
+#         axes[varkey].set_ylabel('model')                                            
+#     abline(1,0,axis=axes[varkey])
+#     i +=1
+# 
+# 
+# 
+# # legend for different forcing simulations (colors)
+# ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
 # leg = []
-# isymbol = 0
-# for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-#     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+# for ikey,key in enumerate(args.experiments.split(';')):
+#     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
 #     leg.append(leg1)
-#     isymbol += 1
-# 
-# # symbol for all stations
-# leg1, = ax.plot([],'ko',markersize=10)
-# leg.append(leg1)
-
-
 # ax.axis('off')
-# ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
-
-
-fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
-
-
-#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
-figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
-fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
-fig.show()  
+# #leg1 =
+# ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
+# 
+# 
+# # # legend for different stations (symbols)
+# # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# # leg = []
+# # isymbol = 0
+# # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+# #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+# #     leg.append(leg1)
+# #     isymbol += 1
+# # 
+# # # symbol for all stations
+# # leg1, = ax.plot([],'ko',markersize=10)
+# # leg.append(leg1)
+# 
+# 
+# # ax.axis('off')
+# # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+# 
+# 
+# fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+# 
+# 
+# #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+# # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+# # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+# fig.show()  
 
 
 

From 9ab98caaafba1e5a0c3990445c1f8efa79d57b4a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 19:24:27 +0200
Subject: [PATCH 029/129] evaluation

---
 class4gl/{evaluation/evaluation.py => interface/interface.py} | 0
 class4gl/{evaluation => interface}/taylorDiagram.py           | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 rename class4gl/{evaluation/evaluation.py => interface/interface.py} (100%)
 rename class4gl/{evaluation => interface}/taylorDiagram.py (100%)

diff --git a/class4gl/evaluation/evaluation.py b/class4gl/interface/interface.py
similarity index 100%
rename from class4gl/evaluation/evaluation.py
rename to class4gl/interface/interface.py
diff --git a/class4gl/evaluation/taylorDiagram.py b/class4gl/interface/taylorDiagram.py
similarity index 100%
rename from class4gl/evaluation/taylorDiagram.py
rename to class4gl/interface/taylorDiagram.py

From 86a6dc9bfd988f029e146ab54fa11b4c6cd7c149 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 22 Aug 2018 20:52:40 +0200
Subject: [PATCH 030/129] wilting point experiment

---
 class4gl/simulations/simulations_wilt.py | 273 +++++++++++++++++++++++
 1 file changed, 273 insertions(+)
 create mode 100644 class4gl/simulations/simulations_wilt.py

diff --git a/class4gl/simulations/simulations_wilt.py b/class4gl/simulations/simulations_wilt.py
new file mode 100644
index 0000000..9e9a8f7
--- /dev/null
+++ b/class4gl/simulations/simulations_wilt.py
@@ -0,0 +1,273 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--first-station-row')
+parser.add_argument('--last-station-row')
+parser.add_argument('--station-id') # run a specific station id
+parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--error-handling',default='dump_on_success')
+parser.add_argument('--experiments')
+parser.add_argument('--split-by',default=-1)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'WILT':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+}
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+# path_soundingsSET = args.path_soundings+'/'+SET+'/'
+
+print("getting stations")
+all_stations = stations(args.path_soundings,suffix='morning',refetch_stations=False)
+
+if args.global_chunk is not None:
+    
+    all_records_morning = get_records(all_stations.table,\
+                                  args.path_soundings,\
+                                  subset='morning',
+                                  refetch_records=False,
+                                  )
+    totalchunks = 0
+    stations_iter = all_stations.table.iterrows()
+    in_current_chunk = False
+    while not in_current_chunk:
+        istation,current_station = stations_iter.__next__()
+        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
+        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
+        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+
+        if in_current_chunk:
+            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+            run_station_chunk = int(args.global_chunk) - totalchunks 
+
+        totalchunks +=chunks_current_station
+
+else:
+    if args.station_id is not None:
+        print("Selecting station by ID")
+        print(all_stations.table)
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        run_stations = pd.DataFrame([run_station])
+    else:
+        print("Selecting stations from a row range in the table")
+        run_stations = pd.DataFrame(all_stations.table)
+        if args.last_station_row is not None:
+            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            run_stations = run_stations.iloc[int(args.first_station):]
+    run_station_chunk = args.station_chunk
+
+#print(all_stations)
+records_morning = get_records(run_stations,\
+                              args.path_soundings,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+records_afternoon = get_records(run_stations,\
+                                args.path_soundings,\
+                                subset='afternoon',
+                                refetch_records=False,
+                                )
+
+# print(records_morning.index)
+# print(records_afternoon.index)
+# align afternoon records with the noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+
+experiments = args.experiments.split(';')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+            isim = 0
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    print('starting '+str(isim)+' out of '+\
+                      str(len(records_morning_station_chunk) )+\
+                      ' (station total: ',str(len(records_morning_station)),')')  
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gli_morning.update(source=expname, \
+                                         pars={'wg':c4gli_morning.pars.wwilt,\
+                                               'w2':c4gli_morning.pars.wwilt},
+                                        )
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_success':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+                    isim += 1
+
+
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+

From eb0f601bad4678958cc0d352b9a5e8b2de6e9aaf Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 23 Aug 2018 11:41:30 +0200
Subject: [PATCH 031/129] fix loading class4gl libraries

---
 class4gl/interface_multi.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 6f70487..a9f6ce8 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -8,7 +8,7 @@
 from time import sleep
 
 
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+# sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl,units
 from interface_functions import *
 # from data_soundings import wyoming

From fd3c2ecc77c2efab6f7d048d72787c43d042b704 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 23 Aug 2018 11:43:24 +0200
Subject: [PATCH 032/129] fix loading class4gl libraries

---
 class4gl/interface_multi.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index a9f6ce8..25bde26 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -9,6 +9,7 @@
 
 
 # sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
 from class4gl import class4gl_input, data_global,class4gl,units
 from interface_functions import *
 # from data_soundings import wyoming

From aa571f80fad2757211767add96af5f4bede1e112 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:02:24 +0200
Subject: [PATCH 033/129] batch qsub array jobs + clean ups + revision of
 arguments

---
 class4gl/interface/interface.py               |   8 +-
 class4gl/simulations/batch_simulations.pbs    |  30 +++
 class4gl/simulations/batch_simulations.py     | 144 ++++++++----
 class4gl/simulations/batch_simulations_old.py |  77 +++++++
 class4gl/simulations/simulations.py           | 206 ++++++++++++------
 5 files changed, 347 insertions(+), 118 deletions(-)
 create mode 100644 class4gl/simulations/batch_simulations.pbs
 create mode 100644 class4gl/simulations/batch_simulations_old.py

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 7becde2..f5891f2 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -8,11 +8,11 @@
 
 import argparse
 parser = argparse.ArgumentParser()
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--experiments')
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load-globaldata',default=False)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
new file mode 100644
index 0000000..6a7c2e5
--- /dev/null
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -0,0 +1,30 @@
+#!/bin/bash 
+#
+#PBS -j oe
+#PBS -M hendrik.wouters@ugent.be
+#PBS -m b
+#PBS -m e
+#PBS -m a
+#PBS -N batch_simulation
+
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python
+
+EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
+
+for var in $(compgen -v | grep C4GLJOB_ ); do
+    echo $var
+    if [ "$var" != "C4GLJOB_exec" ]
+    then
+    EXEC_ALL=$EXEC_ALL" --"`echo $var | cut -c9-`"="${!var}
+    fi
+done
+
+
+# EXEC_ALL="python $exec --global-chunk-number $PBS_ARRAYID \
+#                        --split-by $split_by \
+#                        --dataset $dataset \
+#                        --experiments $experiments"
+#                  #      --path-soundings $path_soundings \
+echo Executing: $EXEC_ALL
+$EXEC_ALL
+
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index b5d4cc3..efacd60 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -1,70 +1,126 @@
-
-import argparse
+# -*- coding: utf-8 -*-
 
 import pandas as pd
+import io
 import os
-import math
 import numpy as np
+import datetime as dt
 import sys
+import pytz
 import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
+import argparse
+
+parser = argparse.ArgumentParser()
+#if __name__ == '__main__':
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--pbs_string',default=' -l walltime=:2:0:0')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling')
+parser.add_argument('--subset_forcing',default='morning') 
+                                        # this tells which yaml subset
+                                        # to initialize with.
+                                        # Most common options are
+                                        # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=50)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--path_forcing') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
 from class4gl import class4gl_input, data_global,class4gl
 from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
 
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-df_stations = pd.read_csv(fn_stations)
 
-# if 'path-soundings' in args.__dict__.keys():
-#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-# else:
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
 
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
 
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
-    parser.add_argument('--exec')
-    parser.add_argument('--experiments')#should be ';'-seperated list
-    parser.add_argument('--split-by',default=-1)
-    args = parser.parse_args()
 
-experiments = args.experiments.split(';')
-#SET = 'GLOBAL'
-SET = args.dataset
-print(args.experiments)
+# #SET = 'GLOBAL'
+# SET = args.dataset
 
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+# path_forcingSET = args.path_forcing+'/'+SET+'/'
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
 
-for expname in experiments:
-    #exp = EXP_DEFS[expname]
-    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-    os.system('rm -R '+path_exp)
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,\
+                                         refetch_records=False,\
+                                        )
 
+print('splitting batch in --split_by='+args.split_by+' jobs.')
 totalchunks = 0
-for istation,current_station in all_stations.iterrows():
-    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
-    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
+for istation,current_station in all_stations_select.iterrows():
+    records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_station_select))/float(args.split_by))
     totalchunks +=chunks_current_station
 
+print('total chunks (= size of array-job) per experiment: ' + str(totalchunks))
+
+
 #if sys.argv[1] == 'qsub':
 # with qsub
-os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
-                                       ',split_by='+str(args.split_by)+\
-                                       ',exec='+str(args.exec)+\
-                                       ',experiments='+str(args.experiments))
+for EXP in args.experiments.strip().split(" "):
+
+    command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+                str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
+    # propagate arguments towards the job script
+    for argkey in args.__dict__.keys():
+        if ((argkey not in ['experiments','pbs_string']) and \
+            # default values are specified in the simulation script, so
+            # excluded here
+            (args.__dict__[argkey] is not None)
+           ):
+                command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+
+    print('Submitting array job for experiment '+EXP+': '+command)
+    os.system(command)
+
+
+    #os.system(command)
 # elif sys.argv[1] == 'wsub':
 #     
 #     # with wsub
diff --git a/class4gl/simulations/batch_simulations_old.py b/class4gl/simulations/batch_simulations_old.py
new file mode 100644
index 0000000..b5d4cc3
--- /dev/null
+++ b/class4gl/simulations/batch_simulations_old.py
@@ -0,0 +1,77 @@
+
+import argparse
+
+import pandas as pd
+import os
+import math
+import numpy as np
+import sys
+import math
+sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+fn_stations = odir+'/igra-stations_sel.txt'
+df_stations = pd.read_csv(fn_stations)
+
+# if 'path-soundings' in args.__dict__.keys():
+#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+# else:
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dataset')
+    parser.add_argument('--path-soundings')
+    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
+    parser.add_argument('--exec')
+    parser.add_argument('--experiments')#should be ';'-seperated list
+    parser.add_argument('--split-by',default=-1)
+    args = parser.parse_args()
+
+experiments = args.experiments.split(';')
+#SET = 'GLOBAL'
+SET = args.dataset
+print(args.experiments)
+
+if 'path-soundings' in args.__dict__.keys():
+    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
+else:
+    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+
+all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+records_morning = get_records(all_stations,\
+                              path_soundingsSET,\
+                              subset='morning',
+                              refetch_records=False,
+                              )
+
+for expname in experiments:
+    #exp = EXP_DEFS[expname]
+    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+    os.system('rm -R '+path_exp)
+
+totalchunks = 0
+for istation,current_station in all_stations.iterrows():
+    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
+    totalchunks +=chunks_current_station
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
+                                       ',split_by='+str(args.split_by)+\
+                                       ',exec='+str(args.exec)+\
+                                       ',experiments='+str(args.experiments))
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index dd3d799..2191c3b 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -13,19 +13,29 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
-parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--first-station-row')
-parser.add_argument('--last-station-row')
-parser.add_argument('--station-id') # run a specific station id
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--error-handling',default='dump_on_success')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
 parser.add_argument('--experiments')
-parser.add_argument('--split-by',default=-1)# station soundings are split
+parser.add_argument('--split_by',default=-1)# station soundings are split
                                             # up in chunks
 
-parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
 args = parser.parse_args()
 
 sys.path.insert(0, args.c4gl_path_lib)
@@ -57,69 +67,120 @@
 # #SET = 'GLOBAL'
 # SET = args.dataset
 
-# path_soundingsSET = args.path_soundings+'/'+SET+'/'
+# path_forcingSET = args.path_forcing+'/'+SET+'/'
 
 print("getting stations")
-all_stations = stations(args.path_soundings,suffix='morning',refetch_stations=False)
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
-if args.global_chunk is not None:
-    
-    all_records_morning = get_records(all_stations.table,\
-                                  args.path_soundings,\
-                                  subset='morning',
-                                  refetch_records=False,
-                                  )
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
     totalchunks = 0
-    stations_iter = all_stations.table.iterrows()
+    stations_iter = all_stations_select.iterrows()
     in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iter.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
 
-        totalchunks +=chunks_current_station
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
 
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
 else:
-    if args.station_id is not None:
-        print("Selecting station by ID")
-        print(all_stations.table)
-        stations_iter = stations_iterator(all_stations)
-        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-        run_stations = pd.DataFrame([run_station])
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
     else:
-        print("Selecting stations from a row range in the table")
-        run_stations = pd.DataFrame(all_stations.table)
-        if args.last_station_row is not None:
-            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
-        if args.first_station_row is not None:
-            run_stations = run_stations.iloc[int(args.first_station):]
-    run_station_chunk = args.station_chunk
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
 
 #print(all_stations)
+print('Fetching initial/forcing records')
 records_morning = get_records(run_stations,\
-                              args.path_soundings,\
-                              subset='morning',
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
                               refetch_records=False,
                               )
-records_afternoon = get_records(run_stations,\
-                                args.path_soundings,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# print(records_morning.index)
-# print(records_afternoon.index)
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
+
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning.ldatetime.dt.date
+    records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
 for expname in experiments:
     exp = EXP_DEFS[expname]
     path_exp = args.path_experiments+'/'+expname+'/'
@@ -131,8 +192,8 @@
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -149,7 +210,7 @@
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                    print('starting '+str(isim)+' out of '+\
+                    print('starting '+str(isim+1)+' out of '+\
                       str(len(records_morning_station_chunk) )+\
                       ' (station total: ',str(len(records_morning_station)),')')  
                 
@@ -162,15 +223,20 @@
                     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
                     
                     
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
+                    if args.runtime == 'from_afternoon_profile':
+                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                        c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                          record_afternoon.index_start, 
+                                                          record_afternoon.index_end,
+                                                        mode='ini')
+                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())
+                    else:
+                        runtime = int(args.runtime)
+
             
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                                        runtime})
                     c4gli_morning.update(source=expname, pars=exp)
 
                     c4gl = class4gl(c4gli_morning)
@@ -247,7 +313,7 @@
     #     with \
     #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
     #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
     #         for (STNID,index),record_ini in records_iterator(records_ini):
     #             c4gli_ini = get_record_yaml(file_station_ini, 
     #                                         record_ini.index_start, 

From 190a5721c079762b2807d54339f938b36eaa99b4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:03:34 +0200
Subject: [PATCH 034/129] remove old file

---
 class4gl/class4gl.py | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index c87e0be..51d58e1 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -94,18 +94,26 @@ def __init__(self):
 }
 
 class class4gl_input(object):
-# this was the way it was defined previously.
-#class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+    """
+    this is the class4gl_input. It extends the model_input, which is now
+    assigned to self.pars. It now also includes initial profiles as pandas
+    Dataframes:
+        self.air_balloon: raw profile input for profile of u,v,theta,q (not used)
+        self.air_ap : the same as self.air_balloonm, but for which a mixed
+                      layer is fitted. Thi profile is used as input.
+        self.air_ac : atmospheric circulation profiles for advection and
+                      subsidence
+
+    # FYI this was the way it was defined in an early version:
+    #    class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
+    """
 
     def __init__(self,set_pars_defaults=True,debug_level=None):
 
-
         """ set up logger (see: https://docs.python.org/2/howto/logging.html)
         """
 
-        print('hello')
         self.logger = logging.getLogger('class4gl_input')
-        print(self.logger)
         if debug_level is not None:
             self.logger.setLevel(debug_level)
 

From 804edc18aa8bd4eb2513aa7ef30ca74424837fa4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:12:02 +0200
Subject: [PATCH 035/129] align simulations_wilt.py with simulations.py

---
 class4gl/simulations/simulations_wilt.py | 203 +++++++++++++++--------
 1 file changed, 135 insertions(+), 68 deletions(-)

diff --git a/class4gl/simulations/simulations_wilt.py b/class4gl/simulations/simulations_wilt.py
index 9e9a8f7..bbfac45 100644
--- a/class4gl/simulations/simulations_wilt.py
+++ b/class4gl/simulations/simulations_wilt.py
@@ -13,19 +13,29 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
-parser.add_argument('--global-chunk') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--first-station-row')
-parser.add_argument('--last-station-row')
-parser.add_argument('--station-id') # run a specific station id
-parser.add_argument('--path-experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path-soundings')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--error-handling',default='dump_on_success')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
 parser.add_argument('--experiments')
-parser.add_argument('--split-by',default=-1)# station soundings are split
+parser.add_argument('--split_by',default=-1)# station soundings are split
                                             # up in chunks
 
-parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl-path-lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
 args = parser.parse_args()
 
 sys.path.insert(0, args.c4gl_path_lib)
@@ -60,67 +70,120 @@
 
 # path_soundingsSET = args.path_soundings+'/'+SET+'/'
 
+
 print("getting stations")
-all_stations = stations(args.path_soundings,suffix='morning',refetch_stations=False)
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
-if args.global_chunk is not None:
-    
-    all_records_morning = get_records(all_stations.table,\
-                                  args.path_soundings,\
-                                  subset='morning',
-                                  refetch_records=False,
-                                  )
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
     totalchunks = 0
-    stations_iter = all_stations.table.iterrows()
+    stations_iter = all_stations_select.iterrows()
     in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iter.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
-
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
 
-        totalchunks +=chunks_current_station
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
 
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
 else:
-    if args.station_id is not None:
-        print("Selecting station by ID")
-        print(all_stations.table)
-        stations_iter = stations_iterator(all_stations)
-        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-        run_stations = pd.DataFrame([run_station])
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
     else:
-        print("Selecting stations from a row range in the table")
-        run_stations = pd.DataFrame(all_stations.table)
-        if args.last_station_row is not None:
-            run_stations = run_stations.iloc[:(int(args.last_station)+1)]
-        if args.first_station_row is not None:
-            run_stations = run_stations.iloc[int(args.first_station):]
-    run_station_chunk = args.station_chunk
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
 
 #print(all_stations)
+print('Fetching initial/forcing records')
 records_morning = get_records(run_stations,\
-                              args.path_soundings,\
-                              subset='morning',
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
                               refetch_records=False,
                               )
-records_afternoon = get_records(run_stations,\
-                                args.path_soundings,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# print(records_morning.index)
-# print(records_afternoon.index)
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
+
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning.ldatetime.dt.date
+    records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
+
 for expname in experiments:
     exp = EXP_DEFS[expname]
     path_exp = args.path_experiments+'/'+expname+'/'
@@ -132,8 +195,8 @@
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(args.path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -150,7 +213,7 @@
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                    print('starting '+str(isim)+' out of '+\
+                    print('starting '+str(isim+1)+' out of '+\
                       str(len(records_morning_station_chunk) )+\
                       ' (station total: ',str(len(records_morning_station)),')')  
                 
@@ -163,15 +226,19 @@
                     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
                     
                     
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
+                    if args.runtime == 'from_afternoon_profile':
+                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                        c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                          record_afternoon.index_start, 
+                                                          record_afternoon.index_end,
+                                                        mode='ini')
+                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())
+                    else:
+                        runtime = int(args.runtime)
             
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                                        runtime})
                     c4gli_morning.update(source=expname, pars=exp)
 
                     c4gli_morning.update(source=expname, \

From faf3b4d053438396b5c2610d2cfbcf3eeb361699 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:24:36 +0200
Subject: [PATCH 036/129] small updates

---
 class4gl/simulations/simulations.py      | 1 -
 class4gl/simulations/simulations_wilt.py | 1 -
 2 files changed, 2 deletions(-)

diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 2191c3b..49eb282 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -67,7 +67,6 @@
 # #SET = 'GLOBAL'
 # SET = args.dataset
 
-# path_forcingSET = args.path_forcing+'/'+SET+'/'
 
 print("getting stations")
 # these are all the stations that are found in the input dataset
diff --git a/class4gl/simulations/simulations_wilt.py b/class4gl/simulations/simulations_wilt.py
index bbfac45..414d35f 100644
--- a/class4gl/simulations/simulations_wilt.py
+++ b/class4gl/simulations/simulations_wilt.py
@@ -68,7 +68,6 @@
 # #SET = 'GLOBAL'
 # SET = args.dataset
 
-# path_soundingsSET = args.path_soundings+'/'+SET+'/'
 
 
 print("getting stations")

From 124d46eccc9c73f8401a8499fdcd883a424b697a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:25:04 +0200
Subject: [PATCH 037/129] small updates

---
 class4gl/simulations/batch_simulations_old.py | 77 -------------------
 1 file changed, 77 deletions(-)
 delete mode 100644 class4gl/simulations/batch_simulations_old.py

diff --git a/class4gl/simulations/batch_simulations_old.py b/class4gl/simulations/batch_simulations_old.py
deleted file mode 100644
index b5d4cc3..0000000
--- a/class4gl/simulations/batch_simulations_old.py
+++ /dev/null
@@ -1,77 +0,0 @@
-
-import argparse
-
-import pandas as pd
-import os
-import math
-import numpy as np
-import sys
-import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
-df_stations = pd.read_csv(fn_stations)
-
-# if 'path-soundings' in args.__dict__.keys():
-#     path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-# else:
-
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    #parser.add_argument('--exec',default='/user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.py')
-    parser.add_argument('--exec')
-    parser.add_argument('--experiments')#should be ';'-seperated list
-    parser.add_argument('--split-by',default=-1)
-    args = parser.parse_args()
-
-experiments = args.experiments.split(';')
-#SET = 'GLOBAL'
-SET = args.dataset
-print(args.experiments)
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
-
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
-records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
-
-for expname in experiments:
-    #exp = EXP_DEFS[expname]
-    path_exp = '/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
-    os.system('rm -R '+path_exp)
-
-totalchunks = 0
-for istation,current_station in all_stations.iterrows():
-    records_morning_query = records_morning.query('STNID == '+str(current_station.name))
-    chunks_current_station = math.ceil(float(len(records_morning_query))/float(args.split_by))
-    totalchunks +=chunks_current_station
-
-#if sys.argv[1] == 'qsub':
-# with qsub
-os.system('qsub /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(totalchunks-1)+" -v dataset="+args.dataset+\
-                                       ',split_by='+str(args.split_by)+\
-                                       ',exec='+str(args.exec)+\
-                                       ',experiments='+str(args.experiments))
-# elif sys.argv[1] == 'wsub':
-#     
-#     # with wsub
-#     STNlist = list(df_stations.iterrows())
-#     NUMSTNS = len(STNlist)
-#     PROCS = NUMSTNS 
-#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-# 
-#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
-

From c9aaa5063195ce23e91d569a4551574886f8cae9 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:30:46 +0200
Subject: [PATCH 038/129] small updates

---
 class4gl/interface_functions.py           | 4 ++--
 class4gl/simulations/batch_simulations.py | 6 +++---
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 42dba38..b005ca4 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -213,8 +213,8 @@ def set_row(self,row):
         return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
     def set_STNID(self,STNID):
         self.ix = np.where((self.stations.table.index == STNID))[0][0]
-        print(self.ix)
-        print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
+        #print(self.ix)
+        #print( self.stations.table.index[self.ix], self.stations.table.iloc[self.ix])
         return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
 
     def __prev__(self):
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index efacd60..58b12ce 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -62,7 +62,7 @@
 
 # path_forcingSET = args.path_forcing+'/'+SET+'/'
 
-print("getting stations")
+print("getting all stations from --path_forcing")
 # these are all the stations that are found in the input dataset
 all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
@@ -70,12 +70,12 @@
 # these are all the stations that are supposed to run by the whole batch (all
 # chunks). We narrow it down according to the station(s) specified.
 if args.station_id is not None:
-    print("Selecting station by ID")
+    print("Selecting stations by --station_id")
     stations_iter = stations_iterator(all_stations)
     STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
     all_stations_select = pd.DataFrame([run_station])
 else:
-    print("Selecting stations from a row range in the table")
+    print("Selecting stations from a row range in the table [--first_station_row,--last_station_row]")
     all_stations_select = pd.DataFrame(all_stations.table)
     if args.last_station_row is not None:
         all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]

From de6c9cb219a2c4e2c9657ce8f0a7e912be700c5f Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:43:34 +0200
Subject: [PATCH 039/129] small updates

---
 class4gl/simulations/batch_simulations.pbs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index 6a7c2e5..d7bb06c 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -5,7 +5,7 @@
 #PBS -m b
 #PBS -m e
 #PBS -m a
-#PBS -N batch_simulation
+#PBS -N c4gl_sim
 
 module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python
 

From 032acc1f0939525ad6823c2d2bfd2b5909ee0317 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 17:50:47 +0200
Subject: [PATCH 040/129] make it also work for the standard simulation (for
 the tutorial)

---
 class4gl/simulations/simulations_wilt.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/class4gl/simulations/simulations_wilt.py b/class4gl/simulations/simulations_wilt.py
index 414d35f..8da29e4 100644
--- a/class4gl/simulations/simulations_wilt.py
+++ b/class4gl/simulations/simulations_wilt.py
@@ -239,11 +239,12 @@
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gli_morning.update(source=expname, \
-                                         pars={'wg':c4gli_morning.pars.wwilt,\
-                                               'w2':c4gli_morning.pars.wwilt},
-                                        )
+                    
+                    if expname == 'WILT':
+                       c4gli_morning.update(source=expname, \
+                                            pars={'wg':c4gli_morning.pars.wwilt,\
+                                                  'w2':c4gli_morning.pars.wwilt},
+                                           )
                     c4gl = class4gl(c4gli_morning)
 
                     if args.error_handling == 'dump_always':

From 40c1982ad4961963580a5703cc719210ec3cc582 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 18:17:21 +0200
Subject: [PATCH 041/129] make tempdir

---
 class4gl/interface_functions.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index b005ca4..5b529d2 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -58,6 +58,7 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
 
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
+    os.system('mkdir -p '+gettempdir())
     filebuffer = open(gettempdir()+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
     filebuffer.write(buf)
     filebuffer.close()
@@ -419,6 +420,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             current_tell = next_tell
                             next_record_found = False
                             yaml_file.seek(current_tell)
+                            os.system('mkdir -p '+gettempdir())
                             filebuffer = open(gettempdir()+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
                             linebuffer = ''
                             while ( (not next_record_found) and (not end_of_file)):

From f3b282622a0d0e55a8ed42166070fe6506da012b Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 18:40:41 +0200
Subject: [PATCH 042/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface_functions.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 5b529d2..e21ed69 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -10,9 +10,6 @@
 from tempfile import gettempdir
 
 
-
-
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl,units
 from interface_functions import *
 #from data_soundings import wyoming

From effb426f444657a2f628a4b7313d2fefd4cade47 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 18:57:21 +0200
Subject: [PATCH 043/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface_functions.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index e21ed69..3b9fff1 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -24,6 +24,8 @@
 
 from matplotlib.colors import LinearSegmentedColormap
 
+TEMPDIR = gettempdir().replace('[',"").replace(']',"")
+
 class records_iterator(object):
     def __init__(self,records):
             
@@ -55,27 +57,27 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
 
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
-    os.system('mkdir -p '+gettempdir())
-    filebuffer = open(gettempdir()+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
+    os.system('mkdir -p '+TEMPDIR
+    filebuffer = open(TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
     filebuffer.write(buf)
     filebuffer.close()
     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
     
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
+    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
 
     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
     print(command)
     os.system(command)
-    jsonstream = open(gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
+    jsonstream = open(TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
     record_dict = json.load(jsonstream)
     jsonstream.close()
-    os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.yaml.'+str(index_start))
+    os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start))
 
 
     if mode =='mod':
         modelout = class4gl()
         modelout.load_yaml_dict(record_dict)
-        os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
+        os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
 
         return modelout
     elif mode == 'ini':
@@ -104,7 +106,7 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
         c4gli = class4gl_input()
         #print(c4gli.logger,'hello')
         c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+gettempdir()+'/'+shortfn+'.buffer.json.'+str(index_start))
+        os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
         return c4gli
 
 
@@ -417,8 +419,8 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             current_tell = next_tell
                             next_record_found = False
                             yaml_file.seek(current_tell)
-                            os.system('mkdir -p '+gettempdir())
-                            filebuffer = open(gettempdir()+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                            os.system('mkdir -p '+TEMPDIR)
+                            filebuffer = open(TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
                             linebuffer = ''
                             while ( (not next_record_found) and (not end_of_file)):
                                 filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
@@ -433,14 +435,14 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
                             
                             #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+gettempdir()+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
                             print(command)
                             
                             os.system(command)
                             #jsonoutput = subprocess.check_output(command,shell=True) 
                             #print(jsonoutput)
                             #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                            jsonstream = open(TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
                             record = json.load(jsonstream)
                             dictouttemp = {}
                             for key,value in record['pars'].items():
@@ -458,14 +460,14 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             dictouttemp['chunk'] = chunk
                             dictouttemp['index_start'] = index_start
                             dictouttemp['index_end'] = index_end
-                            os.system('rm '+gettempdir()+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                            os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
                             for key,value in dictouttemp.items():
                                 if key not in dictout.keys():
                                     dictout[key] = {}
                                 dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
                             print(' obs record registered')
                             jsonstream.close()
-                            os.system('rm '+gettempdir()+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                            os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
                     records_station = pd.DataFrame.from_dict(dictout)
                     records_station.index.set_names(('STNID','chunk','index'),inplace=True)
                     print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\

From d947a258b8c782248d9e96d315ae357b1c57963a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 18:58:54 +0200
Subject: [PATCH 044/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface_functions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 3b9fff1..2615846 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -57,7 +57,7 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
 
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
-    os.system('mkdir -p '+TEMPDIR
+    os.system('mkdir -p '+TEMPDIR)
     filebuffer = open(TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
     filebuffer.write(buf)
     filebuffer.close()

From 286eb0e7d73984d4b89436ac284e06d7ae84dcde Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 19:23:19 +0200
Subject: [PATCH 045/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface/interface.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index f5891f2..fa4ba4e 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -109,11 +109,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     globaldata = None
 
 c4gldata = {}
-for key in args.experiments.split(';'):
+for key in args.experiments.strip(' ').split(' '):
     
     c4gldata[key] = c4gl_interface_soundings( \
                       args.path_experiments+'/'+key+'/',\
-                      args.path_soundings+'/',\
+                      args.path_forcing+'/',\
                       globaldata,\
                       refetch_records=False
                     )

From 121462c01c34c98fd845d89eb313652c2b2843d8 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 21:07:02 +0200
Subject: [PATCH 046/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface_functions.py            | 3 ++-
 class4gl/simulations/batch_simulations.pbs | 2 +-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 2615846..26429a2 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -24,7 +24,8 @@
 
 from matplotlib.colors import LinearSegmentedColormap
 
-TEMPDIR = gettempdir().replace('[',"").replace(']',"")
+#TEMPDIR = gettempdir().replace('[',"").replace(']',"")
+TEMPDIR = '/tmp/'
 
 class records_iterator(object):
     def __init__(self,records):
diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index d7bb06c..54e3168 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -7,7 +7,7 @@
 #PBS -m a
 #PBS -N c4gl_sim
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python Ruby
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 

From d42486fe770f3ddde14f03662a6cc6e2c12065c8 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 21:07:46 +0200
Subject: [PATCH 047/129]  temp dir interface_fucntions.py wrong

---
 class4gl/interface_functions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 26429a2..3791a79 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -24,8 +24,8 @@
 
 from matplotlib.colors import LinearSegmentedColormap
 
-#TEMPDIR = gettempdir().replace('[',"").replace(']',"")
-TEMPDIR = '/tmp/'
+TEMPDIR = gettempdir() #.replace('[',"").replace(']',"")
+#TEMPDIR = '/tmp/'
 
 class records_iterator(object):
     def __init__(self,records):

From e7c973aa453c0d7e01c572fefb8c79b24a0ef8f3 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 21:39:51 +0200
Subject: [PATCH 048/129] fixing ruby

---
 class4gl/interface_functions.py | 25 ++++++++++++++++++++++++-
 1 file changed, 24 insertions(+), 1 deletion(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 3791a79..43f0be6 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -24,6 +24,25 @@
 
 from matplotlib.colors import LinearSegmentedColormap
 
+def which(program):
+    import os
+    def is_exe(fpath):
+        return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
+
+    fpath, fname = os.path.split(program)
+    if fpath:
+        if is_exe(program):
+            return program
+    else:
+        for path in os.environ["PATH"].split(os.pathsep):
+            exe_file = os.path.join(path, program)
+            if is_exe(exe_file):
+                return exe_file
+
+    return None
+
+
+
 TEMPDIR = gettempdir() #.replace('[',"").replace(']',"")
 #TEMPDIR = '/tmp/'
 
@@ -64,7 +83,9 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
     filebuffer.close()
     # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
     
-    command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
+    if which('ruby') is None:
+        raise RuntimeError ('ruby is not found. Aborting...')
+    command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
 
     #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
     print(command)
@@ -435,6 +456,8 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                             index_end = next_tell
 
                             
+                            if which('ruby') is None:
+                                raise RuntimeError ('ruby is not found. Aborting...')
                             #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
                             command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
                             print(command)

From 0e6bae74ae15aaf3346cae994512d60847fd5bba Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 28 Aug 2018 21:48:18 +0200
Subject: [PATCH 049/129] add cleanup_experiments feature

---
 class4gl/simulations/batch_simulations.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index 58b12ce..ce6ae69 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -28,7 +28,8 @@
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
 parser.add_argument('--runtime')
-
+# delete folders of experiments before running them
+parser.add_argument('--cleanup_experiments',default=False)
 parser.add_argument('--experiments')
 parser.add_argument('--split_by',default=50)# station soundings are split
                                             # up in chunks
@@ -104,6 +105,8 @@
 #if sys.argv[1] == 'qsub':
 # with qsub
 for EXP in args.experiments.strip().split(" "):
+    if args.cleanup_experiments:
+        os.system("rm -R "+args.path_experiments+'/'+EXP)
 
     command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
                 str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)

From b1edc0d9f4a0348b92fe37a35b956d9e79749b68 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 29 Aug 2018 10:08:41 +0200
Subject: [PATCH 050/129] fix to avoid boolean cleanup_experiments passed
 through pbs job

---
 class4gl/simulations/batch_simulations.py     |   6 +-
 class4gl/simulations/c4gl_sim.o643256-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643256-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643257-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643258-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643268-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643269-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643271-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643272-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643274-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643275-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643276-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643277-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643278-7       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-0       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-1       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-2       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-3       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-4       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-5       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-6       |  28 ++
 class4gl/simulations/c4gl_sim.o643290-7       |  28 ++
 class4gl/simulations/simulations_smchange2.py | 357 ++++++++++++++++++
 105 files changed, 3245 insertions(+), 2 deletions(-)
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643256-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643257-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643258-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643268-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643269-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643271-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643272-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643274-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643275-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643276-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643277-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643278-7
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-0
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-1
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-2
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-3
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-4
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-5
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-6
 create mode 100644 class4gl/simulations/c4gl_sim.o643290-7
 create mode 100644 class4gl/simulations/simulations_smchange2.py

diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index ce6ae69..c5bb7c9 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -101,9 +101,11 @@
 
 print('total chunks (= size of array-job) per experiment: ' + str(totalchunks))
 
-
 #if sys.argv[1] == 'qsub':
 # with qsub
+
+print(args.experiments.strip().split(" "))
+
 for EXP in args.experiments.strip().split(" "):
     if args.cleanup_experiments:
         os.system("rm -R "+args.path_experiments+'/'+EXP)
@@ -112,7 +114,7 @@
                 str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
     # propagate arguments towards the job script
     for argkey in args.__dict__.keys():
-        if ((argkey not in ['experiments','pbs_string']) and \
+        if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
             # default values are specified in the simulation script, so
             # excluded here
             (args.__dict__[argkey] is not None)
diff --git a/class4gl/simulations/c4gl_sim.o643256-0 b/class4gl/simulations/c4gl_sim.o643256-0
new file mode 100644
index 0000000..8cc36ec
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-1 b/class4gl/simulations/c4gl_sim.o643256-1
new file mode 100644
index 0000000..5f3860e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-2 b/class4gl/simulations/c4gl_sim.o643256-2
new file mode 100644
index 0000000..961e883
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-3 b/class4gl/simulations/c4gl_sim.o643256-3
new file mode 100644
index 0000000..c473bda
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-4 b/class4gl/simulations/c4gl_sim.o643256-4
new file mode 100644
index 0000000..3011ce2
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-5 b/class4gl/simulations/c4gl_sim.o643256-5
new file mode 100644
index 0000000..ceb34e5
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-6 b/class4gl/simulations/c4gl_sim.o643256-6
new file mode 100644
index 0000000..4bb288e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-7 b/class4gl/simulations/c4gl_sim.o643256-7
new file mode 100644
index 0000000..a3d1eeb
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643256-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643257-0 b/class4gl/simulations/c4gl_sim.o643257-0
new file mode 100644
index 0000000..23fcd02
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-1 b/class4gl/simulations/c4gl_sim.o643257-1
new file mode 100644
index 0000000..461c192
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-2 b/class4gl/simulations/c4gl_sim.o643257-2
new file mode 100644
index 0000000..7d9eb94
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-3 b/class4gl/simulations/c4gl_sim.o643257-3
new file mode 100644
index 0000000..3dabc1a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-4 b/class4gl/simulations/c4gl_sim.o643257-4
new file mode 100644
index 0000000..ea5fb1d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-5 b/class4gl/simulations/c4gl_sim.o643257-5
new file mode 100644
index 0000000..681b989
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-6 b/class4gl/simulations/c4gl_sim.o643257-6
new file mode 100644
index 0000000..3029ba0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-7 b/class4gl/simulations/c4gl_sim.o643257-7
new file mode 100644
index 0000000..ce30f0a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643257-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643258-0 b/class4gl/simulations/c4gl_sim.o643258-0
new file mode 100644
index 0000000..217999c
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-1 b/class4gl/simulations/c4gl_sim.o643258-1
new file mode 100644
index 0000000..9795172
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-2 b/class4gl/simulations/c4gl_sim.o643258-2
new file mode 100644
index 0000000..2666ea7
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-3 b/class4gl/simulations/c4gl_sim.o643258-3
new file mode 100644
index 0000000..4cb628d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-4 b/class4gl/simulations/c4gl_sim.o643258-4
new file mode 100644
index 0000000..0ad9bd0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-5 b/class4gl/simulations/c4gl_sim.o643258-5
new file mode 100644
index 0000000..ba1d645
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-6 b/class4gl/simulations/c4gl_sim.o643258-6
new file mode 100644
index 0000000..3e2e3da
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-7 b/class4gl/simulations/c4gl_sim.o643258-7
new file mode 100644
index 0000000..a2ad87b
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643258-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643268-0 b/class4gl/simulations/c4gl_sim.o643268-0
new file mode 100644
index 0000000..8cc36ec
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-1 b/class4gl/simulations/c4gl_sim.o643268-1
new file mode 100644
index 0000000..5f3860e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-2 b/class4gl/simulations/c4gl_sim.o643268-2
new file mode 100644
index 0000000..961e883
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-3 b/class4gl/simulations/c4gl_sim.o643268-3
new file mode 100644
index 0000000..c473bda
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-4 b/class4gl/simulations/c4gl_sim.o643268-4
new file mode 100644
index 0000000..3011ce2
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-5 b/class4gl/simulations/c4gl_sim.o643268-5
new file mode 100644
index 0000000..ceb34e5
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-6 b/class4gl/simulations/c4gl_sim.o643268-6
new file mode 100644
index 0000000..4bb288e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-7 b/class4gl/simulations/c4gl_sim.o643268-7
new file mode 100644
index 0000000..a3d1eeb
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643268-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643269-0 b/class4gl/simulations/c4gl_sim.o643269-0
new file mode 100644
index 0000000..23fcd02
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-1 b/class4gl/simulations/c4gl_sim.o643269-1
new file mode 100644
index 0000000..461c192
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-2 b/class4gl/simulations/c4gl_sim.o643269-2
new file mode 100644
index 0000000..7d9eb94
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-3 b/class4gl/simulations/c4gl_sim.o643269-3
new file mode 100644
index 0000000..3dabc1a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-4 b/class4gl/simulations/c4gl_sim.o643269-4
new file mode 100644
index 0000000..ea5fb1d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-5 b/class4gl/simulations/c4gl_sim.o643269-5
new file mode 100644
index 0000000..681b989
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-6 b/class4gl/simulations/c4gl_sim.o643269-6
new file mode 100644
index 0000000..3029ba0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-7 b/class4gl/simulations/c4gl_sim.o643269-7
new file mode 100644
index 0000000..ce30f0a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643269-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643271-0 b/class4gl/simulations/c4gl_sim.o643271-0
new file mode 100644
index 0000000..217999c
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-1 b/class4gl/simulations/c4gl_sim.o643271-1
new file mode 100644
index 0000000..9795172
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-2 b/class4gl/simulations/c4gl_sim.o643271-2
new file mode 100644
index 0000000..2666ea7
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-3 b/class4gl/simulations/c4gl_sim.o643271-3
new file mode 100644
index 0000000..4cb628d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-4 b/class4gl/simulations/c4gl_sim.o643271-4
new file mode 100644
index 0000000..0ad9bd0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-5 b/class4gl/simulations/c4gl_sim.o643271-5
new file mode 100644
index 0000000..ba1d645
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-6 b/class4gl/simulations/c4gl_sim.o643271-6
new file mode 100644
index 0000000..3e2e3da
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-7 b/class4gl/simulations/c4gl_sim.o643271-7
new file mode 100644
index 0000000..a2ad87b
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643271-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643272-0 b/class4gl/simulations/c4gl_sim.o643272-0
new file mode 100644
index 0000000..3cc76c0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-1 b/class4gl/simulations/c4gl_sim.o643272-1
new file mode 100644
index 0000000..a6f9c52
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-2 b/class4gl/simulations/c4gl_sim.o643272-2
new file mode 100644
index 0000000..aa569ec
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-3 b/class4gl/simulations/c4gl_sim.o643272-3
new file mode 100644
index 0000000..a065842
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-4 b/class4gl/simulations/c4gl_sim.o643272-4
new file mode 100644
index 0000000..61b092e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-5 b/class4gl/simulations/c4gl_sim.o643272-5
new file mode 100644
index 0000000..8a030e2
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-6 b/class4gl/simulations/c4gl_sim.o643272-6
new file mode 100644
index 0000000..ac15fba
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-7 b/class4gl/simulations/c4gl_sim.o643272-7
new file mode 100644
index 0000000..95252f1
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643272-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643274-0 b/class4gl/simulations/c4gl_sim.o643274-0
new file mode 100644
index 0000000..02ba97b
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-1 b/class4gl/simulations/c4gl_sim.o643274-1
new file mode 100644
index 0000000..df17474
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-2 b/class4gl/simulations/c4gl_sim.o643274-2
new file mode 100644
index 0000000..d3fdbfd
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-3 b/class4gl/simulations/c4gl_sim.o643274-3
new file mode 100644
index 0000000..b403bf3
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-4 b/class4gl/simulations/c4gl_sim.o643274-4
new file mode 100644
index 0000000..4e68631
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-5 b/class4gl/simulations/c4gl_sim.o643274-5
new file mode 100644
index 0000000..08f9aed
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-6 b/class4gl/simulations/c4gl_sim.o643274-6
new file mode 100644
index 0000000..cfb6f68
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-7 b/class4gl/simulations/c4gl_sim.o643274-7
new file mode 100644
index 0000000..5fb4e4f
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643274-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643275-0 b/class4gl/simulations/c4gl_sim.o643275-0
new file mode 100644
index 0000000..3bcff7a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-1 b/class4gl/simulations/c4gl_sim.o643275-1
new file mode 100644
index 0000000..f65c900
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-2 b/class4gl/simulations/c4gl_sim.o643275-2
new file mode 100644
index 0000000..e418bbd
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-3 b/class4gl/simulations/c4gl_sim.o643275-3
new file mode 100644
index 0000000..d1b0803
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-4 b/class4gl/simulations/c4gl_sim.o643275-4
new file mode 100644
index 0000000..e466956
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-6 b/class4gl/simulations/c4gl_sim.o643275-6
new file mode 100644
index 0000000..3264a04
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-7 b/class4gl/simulations/c4gl_sim.o643275-7
new file mode 100644
index 0000000..b7582e0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643275-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643276-0 b/class4gl/simulations/c4gl_sim.o643276-0
new file mode 100644
index 0000000..bc5f648
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-1 b/class4gl/simulations/c4gl_sim.o643276-1
new file mode 100644
index 0000000..8794be3
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-2 b/class4gl/simulations/c4gl_sim.o643276-2
new file mode 100644
index 0000000..a7da9c0
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-3 b/class4gl/simulations/c4gl_sim.o643276-3
new file mode 100644
index 0000000..69bf48a
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-4 b/class4gl/simulations/c4gl_sim.o643276-4
new file mode 100644
index 0000000..6b44c82
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-5 b/class4gl/simulations/c4gl_sim.o643276-5
new file mode 100644
index 0000000..e189294
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-6 b/class4gl/simulations/c4gl_sim.o643276-6
new file mode 100644
index 0000000..453e682
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-7 b/class4gl/simulations/c4gl_sim.o643276-7
new file mode 100644
index 0000000..a8c3544
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643276-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643277-0 b/class4gl/simulations/c4gl_sim.o643277-0
new file mode 100644
index 0000000..4dae9b5
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-1 b/class4gl/simulations/c4gl_sim.o643277-1
new file mode 100644
index 0000000..385b39b
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-2 b/class4gl/simulations/c4gl_sim.o643277-2
new file mode 100644
index 0000000..77d3895
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-3 b/class4gl/simulations/c4gl_sim.o643277-3
new file mode 100644
index 0000000..27737f2
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-4 b/class4gl/simulations/c4gl_sim.o643277-4
new file mode 100644
index 0000000..91915db
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-5 b/class4gl/simulations/c4gl_sim.o643277-5
new file mode 100644
index 0000000..40dbcf2
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-6 b/class4gl/simulations/c4gl_sim.o643277-6
new file mode 100644
index 0000000..d6ad526
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-7 b/class4gl/simulations/c4gl_sim.o643277-7
new file mode 100644
index 0000000..8fec120
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643277-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643278-0 b/class4gl/simulations/c4gl_sim.o643278-0
new file mode 100644
index 0000000..980578e
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-1 b/class4gl/simulations/c4gl_sim.o643278-1
new file mode 100644
index 0000000..33d1e33
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-2 b/class4gl/simulations/c4gl_sim.o643278-2
new file mode 100644
index 0000000..7d22207
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-3 b/class4gl/simulations/c4gl_sim.o643278-3
new file mode 100644
index 0000000..15b6190
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-4 b/class4gl/simulations/c4gl_sim.o643278-4
new file mode 100644
index 0000000..ca44618
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-5 b/class4gl/simulations/c4gl_sim.o643278-5
new file mode 100644
index 0000000..a9eae55
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-6 b/class4gl/simulations/c4gl_sim.o643278-6
new file mode 100644
index 0000000..327b4d1
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-7 b/class4gl/simulations/c4gl_sim.o643278-7
new file mode 100644
index 0000000..a846ec1
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643278-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643290-0 b/class4gl/simulations/c4gl_sim.o643290-0
new file mode 100644
index 0000000..e6eb9c6
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-0
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-1 b/class4gl/simulations/c4gl_sim.o643290-1
new file mode 100644
index 0000000..273a974
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-1
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-2 b/class4gl/simulations/c4gl_sim.o643290-2
new file mode 100644
index 0000000..bef9d8d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-2
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-3 b/class4gl/simulations/c4gl_sim.o643290-3
new file mode 100644
index 0000000..21bc334
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-3
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-4 b/class4gl/simulations/c4gl_sim.o643290-4
new file mode 100644
index 0000000..fbc5a1d
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-4
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (4)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 4
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-5 b/class4gl/simulations/c4gl_sim.o643290-5
new file mode 100644
index 0000000..89c55da
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-5
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (5)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 5
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-6 b/class4gl/simulations/c4gl_sim.o643290-6
new file mode 100644
index 0000000..55c69ee
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-6
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (6)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 6
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-7 b/class4gl/simulations/c4gl_sim.o643290-7
new file mode 100644
index 0000000..e646e64
--- /dev/null
+++ b/class4gl/simulations/c4gl_sim.o643290-7
@@ -0,0 +1,28 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+getting stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (7)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 7
+Fetching initial/forcing records
+Fetching afternoon records for determining the simulation runtimes
+hello
+396
+396
+aligning morning and afternoon records
+Traceback (most recent call last):
+  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
+    exp = EXP_DEFS[expname]
+KeyError: ''
diff --git a/class4gl/simulations/simulations_smchange2.py b/class4gl/simulations/simulations_smchange2.py
new file mode 100644
index 0000000..99c38a9
--- /dev/null
+++ b/class4gl/simulations/simulations_smchange2.py
@@ -0,0 +1,357 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'SMa':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.05},
+  'SMb':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.1},
+  'SMc':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.15},
+  'SMd':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.02},
+  'SMe':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.25},
+  'SMf':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.3},
+  'SMg':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.35},
+  'SMh':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.4},
+  'SMi':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.45},
+  'SMj':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.5},
+  'SMk':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.55},
+  'SMl':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.6},
+  'SMm':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.65},
+  'SMo':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.7},
+  'SMp':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.75},
+  'SMq':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.08},
+  'SMr':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.85},
+  'SMs':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.9},
+  'SMt':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 0.95},
+  'SMu':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange': 1.},
+}
+
+##for i in range(1,20):
+##    EXP_DEFS['SM'+str(i)] = {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'wgchange':float(i)/20.}
+
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_morning = get_records(run_stations,\
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
+                              refetch_records=False,
+                              )
+
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning.ldatetime.dt.date
+    records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+            isim = 0
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    print('starting '+str(isim+1)+' out of '+\
+                      str(len(records_morning_station_chunk) )+\
+                      ' (station total: ',str(len(records_morning_station)),')')  
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+
+                    sm = exp['wgchange']*(c4gli_morning.pars.wfc - c4gli_morning.pars.wwilt) + c4gli_morning.pars.wwilt
+                    c4gli_morning.update(source=expname, pars={'wg': sm})
+                    c4gli_morning.update(source=expname, pars={'w2': sm})
+                    
+                    
+                    if args.runtime == 'from_afternoon_profile':
+                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                        c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                          record_afternoon.index_start, 
+                                                          record_afternoon.index_end,
+                                                        mode='ini')
+                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())
+                    else:
+                        runtime = int(args.runtime)
+
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        runtime})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_success':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+                    isim += 1
+
+
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+

From 2f26d34cfeb87c924d87a0ac7f2060a968a04b58 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 29 Aug 2018 10:09:17 +0200
Subject: [PATCH 051/129] fix to avoid boolean cleanup_experiments passed
 through pbs job

---
 class4gl/simulations/c4gl_sim.o643256-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643256-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643257-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643258-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643268-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643269-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643271-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643272-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643274-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-4       | 28 -------------------
 ...{c4gl_sim.o643256-5 => c4gl_sim.o643275-5} |  4 +--
 class4gl/simulations/c4gl_sim.o643275-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643275-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643276-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643277-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643278-7       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-0       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-1       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-2       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-3       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-4       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-5       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-6       | 28 -------------------
 class4gl/simulations/c4gl_sim.o643290-7       | 28 -------------------
 103 files changed, 2 insertions(+), 2858 deletions(-)
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-0
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-1
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-2
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-3
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-4
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-6
 delete mode 100644 class4gl/simulations/c4gl_sim.o643256-7
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-0
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-1
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-2
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-3
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-4
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-5
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-6
 delete mode 100644 class4gl/simulations/c4gl_sim.o643257-7
 delete mode 100644 class4gl/simulations/c4gl_sim.o643258-0
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diff --git a/class4gl/simulations/c4gl_sim.o643256-0 b/class4gl/simulations/c4gl_sim.o643256-0
deleted file mode 100644
index 8cc36ec..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-1 b/class4gl/simulations/c4gl_sim.o643256-1
deleted file mode 100644
index 5f3860e..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-2 b/class4gl/simulations/c4gl_sim.o643256-2
deleted file mode 100644
index 961e883..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-3 b/class4gl/simulations/c4gl_sim.o643256-3
deleted file mode 100644
index c473bda..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-4 b/class4gl/simulations/c4gl_sim.o643256-4
deleted file mode 100644
index 3011ce2..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-6 b/class4gl/simulations/c4gl_sim.o643256-6
deleted file mode 100644
index 4bb288e..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643256-7 b/class4gl/simulations/c4gl_sim.o643256-7
deleted file mode 100644
index a3d1eeb..0000000
--- a/class4gl/simulations/c4gl_sim.o643256-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643257-0 b/class4gl/simulations/c4gl_sim.o643257-0
deleted file mode 100644
index 23fcd02..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-1 b/class4gl/simulations/c4gl_sim.o643257-1
deleted file mode 100644
index 461c192..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-2 b/class4gl/simulations/c4gl_sim.o643257-2
deleted file mode 100644
index 7d9eb94..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-3 b/class4gl/simulations/c4gl_sim.o643257-3
deleted file mode 100644
index 3dabc1a..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-4 b/class4gl/simulations/c4gl_sim.o643257-4
deleted file mode 100644
index ea5fb1d..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-5 b/class4gl/simulations/c4gl_sim.o643257-5
deleted file mode 100644
index 681b989..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-6 b/class4gl/simulations/c4gl_sim.o643257-6
deleted file mode 100644
index 3029ba0..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643257-7 b/class4gl/simulations/c4gl_sim.o643257-7
deleted file mode 100644
index ce30f0a..0000000
--- a/class4gl/simulations/c4gl_sim.o643257-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643258-0 b/class4gl/simulations/c4gl_sim.o643258-0
deleted file mode 100644
index 217999c..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-1 b/class4gl/simulations/c4gl_sim.o643258-1
deleted file mode 100644
index 9795172..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-2 b/class4gl/simulations/c4gl_sim.o643258-2
deleted file mode 100644
index 2666ea7..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-3 b/class4gl/simulations/c4gl_sim.o643258-3
deleted file mode 100644
index 4cb628d..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-4 b/class4gl/simulations/c4gl_sim.o643258-4
deleted file mode 100644
index 0ad9bd0..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-5 b/class4gl/simulations/c4gl_sim.o643258-5
deleted file mode 100644
index ba1d645..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-6 b/class4gl/simulations/c4gl_sim.o643258-6
deleted file mode 100644
index 3e2e3da..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643258-7 b/class4gl/simulations/c4gl_sim.o643258-7
deleted file mode 100644
index a2ad87b..0000000
--- a/class4gl/simulations/c4gl_sim.o643258-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643268-0 b/class4gl/simulations/c4gl_sim.o643268-0
deleted file mode 100644
index 8cc36ec..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-1 b/class4gl/simulations/c4gl_sim.o643268-1
deleted file mode 100644
index 5f3860e..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-2 b/class4gl/simulations/c4gl_sim.o643268-2
deleted file mode 100644
index 961e883..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-3 b/class4gl/simulations/c4gl_sim.o643268-3
deleted file mode 100644
index c473bda..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-4 b/class4gl/simulations/c4gl_sim.o643268-4
deleted file mode 100644
index 3011ce2..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-5 b/class4gl/simulations/c4gl_sim.o643268-5
deleted file mode 100644
index ceb34e5..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-6 b/class4gl/simulations/c4gl_sim.o643268-6
deleted file mode 100644
index 4bb288e..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643268-7 b/class4gl/simulations/c4gl_sim.o643268-7
deleted file mode 100644
index a3d1eeb..0000000
--- a/class4gl/simulations/c4gl_sim.o643268-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM0'
diff --git a/class4gl/simulations/c4gl_sim.o643269-0 b/class4gl/simulations/c4gl_sim.o643269-0
deleted file mode 100644
index 23fcd02..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-1 b/class4gl/simulations/c4gl_sim.o643269-1
deleted file mode 100644
index 461c192..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-2 b/class4gl/simulations/c4gl_sim.o643269-2
deleted file mode 100644
index 7d9eb94..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-3 b/class4gl/simulations/c4gl_sim.o643269-3
deleted file mode 100644
index 3dabc1a..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-4 b/class4gl/simulations/c4gl_sim.o643269-4
deleted file mode 100644
index ea5fb1d..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-5 b/class4gl/simulations/c4gl_sim.o643269-5
deleted file mode 100644
index 681b989..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-6 b/class4gl/simulations/c4gl_sim.o643269-6
deleted file mode 100644
index 3029ba0..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643269-7 b/class4gl/simulations/c4gl_sim.o643269-7
deleted file mode 100644
index ce30f0a..0000000
--- a/class4gl/simulations/c4gl_sim.o643269-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM1 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM1'
diff --git a/class4gl/simulations/c4gl_sim.o643271-0 b/class4gl/simulations/c4gl_sim.o643271-0
deleted file mode 100644
index 217999c..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-1 b/class4gl/simulations/c4gl_sim.o643271-1
deleted file mode 100644
index 9795172..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-2 b/class4gl/simulations/c4gl_sim.o643271-2
deleted file mode 100644
index 2666ea7..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-3 b/class4gl/simulations/c4gl_sim.o643271-3
deleted file mode 100644
index 4cb628d..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-4 b/class4gl/simulations/c4gl_sim.o643271-4
deleted file mode 100644
index 0ad9bd0..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-5 b/class4gl/simulations/c4gl_sim.o643271-5
deleted file mode 100644
index ba1d645..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-6 b/class4gl/simulations/c4gl_sim.o643271-6
deleted file mode 100644
index 3e2e3da..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643271-7 b/class4gl/simulations/c4gl_sim.o643271-7
deleted file mode 100644
index a2ad87b..0000000
--- a/class4gl/simulations/c4gl_sim.o643271-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM2 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM2'
diff --git a/class4gl/simulations/c4gl_sim.o643272-0 b/class4gl/simulations/c4gl_sim.o643272-0
deleted file mode 100644
index 3cc76c0..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-1 b/class4gl/simulations/c4gl_sim.o643272-1
deleted file mode 100644
index a6f9c52..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-2 b/class4gl/simulations/c4gl_sim.o643272-2
deleted file mode 100644
index aa569ec..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-3 b/class4gl/simulations/c4gl_sim.o643272-3
deleted file mode 100644
index a065842..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-4 b/class4gl/simulations/c4gl_sim.o643272-4
deleted file mode 100644
index 61b092e..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-5 b/class4gl/simulations/c4gl_sim.o643272-5
deleted file mode 100644
index 8a030e2..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-6 b/class4gl/simulations/c4gl_sim.o643272-6
deleted file mode 100644
index ac15fba..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643272-7 b/class4gl/simulations/c4gl_sim.o643272-7
deleted file mode 100644
index 95252f1..0000000
--- a/class4gl/simulations/c4gl_sim.o643272-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM3 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM3'
diff --git a/class4gl/simulations/c4gl_sim.o643274-0 b/class4gl/simulations/c4gl_sim.o643274-0
deleted file mode 100644
index 02ba97b..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-1 b/class4gl/simulations/c4gl_sim.o643274-1
deleted file mode 100644
index df17474..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-2 b/class4gl/simulations/c4gl_sim.o643274-2
deleted file mode 100644
index d3fdbfd..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-3 b/class4gl/simulations/c4gl_sim.o643274-3
deleted file mode 100644
index b403bf3..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-4 b/class4gl/simulations/c4gl_sim.o643274-4
deleted file mode 100644
index 4e68631..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-5 b/class4gl/simulations/c4gl_sim.o643274-5
deleted file mode 100644
index 08f9aed..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-6 b/class4gl/simulations/c4gl_sim.o643274-6
deleted file mode 100644
index cfb6f68..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643274-7 b/class4gl/simulations/c4gl_sim.o643274-7
deleted file mode 100644
index 5fb4e4f..0000000
--- a/class4gl/simulations/c4gl_sim.o643274-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM4 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM4'
diff --git a/class4gl/simulations/c4gl_sim.o643275-0 b/class4gl/simulations/c4gl_sim.o643275-0
deleted file mode 100644
index 3bcff7a..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-1 b/class4gl/simulations/c4gl_sim.o643275-1
deleted file mode 100644
index f65c900..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-2 b/class4gl/simulations/c4gl_sim.o643275-2
deleted file mode 100644
index e418bbd..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-3 b/class4gl/simulations/c4gl_sim.o643275-3
deleted file mode 100644
index d1b0803..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-4 b/class4gl/simulations/c4gl_sim.o643275-4
deleted file mode 100644
index e466956..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643256-5 b/class4gl/simulations/c4gl_sim.o643275-5
similarity index 93%
rename from class4gl/simulations/c4gl_sim.o643256-5
rename to class4gl/simulations/c4gl_sim.o643275-5
index ceb34e5..df8103f 100644
--- a/class4gl/simulations/c4gl_sim.o643256-5
+++ b/class4gl/simulations/c4gl_sim.o643275-5
@@ -6,7 +6,7 @@ C4GLJOB_path_forcing
 C4GLJOB_split_by
 C4GLJOB_station_id
 C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM0 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
 getting stations
 defining all_stations_select
 Selecting station by ID
@@ -25,4 +25,4 @@ aligning morning and afternoon records
 Traceback (most recent call last):
   File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
     exp = EXP_DEFS[expname]
-KeyError: 'SM0'
+KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-6 b/class4gl/simulations/c4gl_sim.o643275-6
deleted file mode 100644
index 3264a04..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643275-7 b/class4gl/simulations/c4gl_sim.o643275-7
deleted file mode 100644
index b7582e0..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/c4gl_sim.o643276-0 b/class4gl/simulations/c4gl_sim.o643276-0
deleted file mode 100644
index bc5f648..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-1 b/class4gl/simulations/c4gl_sim.o643276-1
deleted file mode 100644
index 8794be3..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-2 b/class4gl/simulations/c4gl_sim.o643276-2
deleted file mode 100644
index a7da9c0..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-3 b/class4gl/simulations/c4gl_sim.o643276-3
deleted file mode 100644
index 69bf48a..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-4 b/class4gl/simulations/c4gl_sim.o643276-4
deleted file mode 100644
index 6b44c82..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-5 b/class4gl/simulations/c4gl_sim.o643276-5
deleted file mode 100644
index e189294..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-6 b/class4gl/simulations/c4gl_sim.o643276-6
deleted file mode 100644
index 453e682..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643276-7 b/class4gl/simulations/c4gl_sim.o643276-7
deleted file mode 100644
index a8c3544..0000000
--- a/class4gl/simulations/c4gl_sim.o643276-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM6 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM6'
diff --git a/class4gl/simulations/c4gl_sim.o643277-0 b/class4gl/simulations/c4gl_sim.o643277-0
deleted file mode 100644
index 4dae9b5..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-1 b/class4gl/simulations/c4gl_sim.o643277-1
deleted file mode 100644
index 385b39b..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-2 b/class4gl/simulations/c4gl_sim.o643277-2
deleted file mode 100644
index 77d3895..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-3 b/class4gl/simulations/c4gl_sim.o643277-3
deleted file mode 100644
index 27737f2..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-4 b/class4gl/simulations/c4gl_sim.o643277-4
deleted file mode 100644
index 91915db..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-5 b/class4gl/simulations/c4gl_sim.o643277-5
deleted file mode 100644
index 40dbcf2..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-6 b/class4gl/simulations/c4gl_sim.o643277-6
deleted file mode 100644
index d6ad526..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643277-7 b/class4gl/simulations/c4gl_sim.o643277-7
deleted file mode 100644
index 8fec120..0000000
--- a/class4gl/simulations/c4gl_sim.o643277-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM7 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM7'
diff --git a/class4gl/simulations/c4gl_sim.o643278-0 b/class4gl/simulations/c4gl_sim.o643278-0
deleted file mode 100644
index 980578e..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-1 b/class4gl/simulations/c4gl_sim.o643278-1
deleted file mode 100644
index 33d1e33..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-2 b/class4gl/simulations/c4gl_sim.o643278-2
deleted file mode 100644
index 7d22207..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-3 b/class4gl/simulations/c4gl_sim.o643278-3
deleted file mode 100644
index 15b6190..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-4 b/class4gl/simulations/c4gl_sim.o643278-4
deleted file mode 100644
index ca44618..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-5 b/class4gl/simulations/c4gl_sim.o643278-5
deleted file mode 100644
index a9eae55..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-6 b/class4gl/simulations/c4gl_sim.o643278-6
deleted file mode 100644
index 327b4d1..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643278-7 b/class4gl/simulations/c4gl_sim.o643278-7
deleted file mode 100644
index a846ec1..0000000
--- a/class4gl/simulations/c4gl_sim.o643278-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM9 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM9'
diff --git a/class4gl/simulations/c4gl_sim.o643290-0 b/class4gl/simulations/c4gl_sim.o643290-0
deleted file mode 100644
index e6eb9c6..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-0
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-1 b/class4gl/simulations/c4gl_sim.o643290-1
deleted file mode 100644
index 273a974..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-1
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-2 b/class4gl/simulations/c4gl_sim.o643290-2
deleted file mode 100644
index bef9d8d..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-2
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-3 b/class4gl/simulations/c4gl_sim.o643290-3
deleted file mode 100644
index 21bc334..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-3
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-4 b/class4gl/simulations/c4gl_sim.o643290-4
deleted file mode 100644
index fbc5a1d..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-4
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (4)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 4
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-5 b/class4gl/simulations/c4gl_sim.o643290-5
deleted file mode 100644
index 89c55da..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-6 b/class4gl/simulations/c4gl_sim.o643290-6
deleted file mode 100644
index 55c69ee..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-6
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (6)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 6
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''
diff --git a/class4gl/simulations/c4gl_sim.o643290-7 b/class4gl/simulations/c4gl_sim.o643290-7
deleted file mode 100644
index e646e64..0000000
--- a/class4gl/simulations/c4gl_sim.o643290-7
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments= --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (7)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 7
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: ''

From e443be17ecc9a84cf551ed3af0a388d31de8e991 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 08:49:21 +0200
Subject: [PATCH 052/129] add evaluation interfaces

---
 class4gl/data_global.py                       |   6 +-
 class4gl/interface/interface.py               |   2 +-
 class4gl/interface/interface_eval.py          | 375 ++++++++++++++++++
 class4gl/interface/interface_eval_stations.py | 290 ++++++++++++++
 class4gl/interface/interface_stations.py      | 309 +++++++++++++++
 class4gl/interface_multi.py                   |   2 +-
 6 files changed, 979 insertions(+), 5 deletions(-)
 create mode 100644 class4gl/interface/interface_eval.py
 create mode 100644 class4gl/interface/interface_eval_stations.py
 create mode 100644 class4gl/interface/interface_stations.py

diff --git a/class4gl/data_global.py b/class4gl/data_global.py
index 9c3d9b5..63081f1 100644
--- a/class4gl/data_global.py
+++ b/class4gl/data_global.py
@@ -221,10 +221,10 @@ def __init__(self,sources= {
         # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
         # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
         # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
         #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/EF_*_GLEAM_v3.2a.nc:EF',
+        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc:EF',
         "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
         "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
         "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index fa4ba4e..f6d718f 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -100,7 +100,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 # # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 # }
 
-if args.load_globaldata:
+if bool(args.load_globaldata):
     # iniitialize global data
     globaldata = data_global()
     # ...  and load initial data pages
diff --git a/class4gl/interface/interface_eval.py b/class4gl/interface/interface_eval.py
new file mode 100644
index 0000000..f72d4fc
--- /dev/null
+++ b/class4gl/interface/interface_eval.py
@@ -0,0 +1,375 @@
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=True)
+parser.add_argument('--show_control_parameters',default=True)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--figure_filename_2',default=None)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if args.load_globaldata:
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing+'/',\
+                      globaldata,\
+                      refetch_records=False
+                    )
+
+# the lines below activate TaylorPlots but it is disabled for now
+fig = plt.figure(figsize=(10,7))   #width,height
+i = 1                                                                           
+axes = {}         
+axes_taylor = {}         
+
+colors = ['r','g','b','m']
+symbols = ['*','x','+']
+dias = {}
+
+for varkey in ['h','theta','q']:                                                    
+    axes[varkey] = fig.add_subplot(2,3,i)                                       
+    #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+
+    #print(obs.std())
+    dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+    if i == 0:
+        dias[varkey]._ax.axis["left"].label.set_text(\
+            "Standard deviation (model) / Standard deviation (observations)")
+        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+    #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+    # Q95 = obs.quantile(0.95)
+    # Q95 = obs.quantile(0.90)
+    # Add RMS contours, and label them
+    contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+    dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+    #dia._ax.set_title(season.capitalize())
+
+    dias[varkey].add_grid()
+
+
+    #dia.ax.plot(x99,y99,color='k')
+
+    
+    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        # clearsky = (cc < 0.05)
+        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        x, y = obs.values,mod.values
+        print(key,len(obs.values))
+
+        STD_OBS = obs.std()
+        #scores
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+        
+        # fit = np.polyfit(x,y,deg=1)
+        # axes[varkey].plot(x, fit[0] * x + fit[1],\
+        #                   color=colors[ikey],alpha=0.8,lw=2,\
+        #                   label=key+", "+\
+        #                               'R = '+str(round(PR,3))+', '+\
+        #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+        #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+        # axes[varkey].legend(fontsize=5)
+        
+        # print(STD)
+        # print(PR)
+        dias[varkey].add_sample(STD/STD_OBS, PR,
+                       marker='o', ms=5, ls='',
+                       #mfc='k', mec='k', # B&W
+                       mfc=colors[ikey], mec=colors[ikey], # Colors
+                       label=key)
+
+    # put ticker position, see
+    # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+    # dia.ax.axis['bottom'].
+    # dia.ax.axis['left'].
+    # dia.ax.axis['left'].
+
+    i += 1
+
+i = 0
+for varkey in ['h','theta','q']:                                                    
+    ikey = 0
+    key = list(args.experiments.strip().split(' '))[ikey]
+    cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+    clearsky = (cc < 0.05)
+
+    mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+    obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+
+
+    nbins=40       
+    x, y = obs.values,mod.values
+    
+    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+    zi = np.zeros_like(xi)*np.nan       
+    for ibin in range(nbins):
+        xmin = x.min() + ibin * (x.max() - x.min())/nbins
+        xmax = xmin + (x.max() - x.min())/nbins
+        in_bin = ((x >= xmin) & (x < xmax))
+        ybin = y[in_bin]
+        xbin = x[in_bin]
+        if len(ybin) > 20:
+            k = kde.gaussian_kde((ybin))
+            zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+    zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+    zi_int = zi.cumsum(axis=1) 
+                 #  label=key+", "+\
+                 #                    'R = '+str(round(PR[0],3))+', '+\
+                 #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                 #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+    axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+            colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+    axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+            colors=['darkred'],alpha=0.5,)
+
+
+    latex = {}
+    latex['dthetadt'] =  r'$d \theta / dt $'
+    latex['dqdt'] =      r'$d q / dt $'
+    latex['dhdt'] =      r'$d h / dt $'
+
+    axes[varkey].set_xlabel('observations')     
+    axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+
+    PR = pearsonr(mod,obs)[0]
+    RMSE = rmse(obs,mod)                                               
+    BIAS = np.mean(mod) - np.mean(obs)
+    STD = mod.std()
+
+    axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+                                  'R = '+str(round(PR,3))+', '+\
+                                  'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                  'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+                         s=0.1,alpha=0.14,color='k')
+    axes[varkey].legend(fontsize=5)
+                   
+    axes[varkey].set_xlabel('observations')     
+    if i==0:                                    
+        axes[varkey].set_ylabel('model')                                            
+    abline(1,0,axis=axes[varkey])
+    i +=1
+
+
+
+# legend for different forcing simulations (colors)
+ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+leg = []
+for ikey,key in enumerate(args.experiments.strip().split(' ')):
+    leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+    leg.append(leg1)
+ax.axis('off')
+#leg1 =
+ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+
+
+# # legend for different stations (symbols)
+# ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# leg = []
+# isymbol = 0
+# for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+#     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+#     leg.append(leg1)
+#     isymbol += 1
+# 
+# # symbol for all stations
+# leg1, = ax.plot([],'ko',markersize=10)
+# leg.append(leg1)
+
+
+# ax.axis('off')
+# ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+
+
+fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+
+
+#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+# figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+# fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+
+if args.figure_filename is not None:
+    fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+fig.show()  
+
+
+if bool(args.show_control_parameters):
+    import seaborn as sns
+    sns.set()
+    fig = pl.figure(figsize=(12,8))
+    i = 1
+    axes = {}
+    data_all = pd.DataFrame()
+    
+    
+    
+    # #for varkey in ['theta','q']:     
+    # EF =\
+    #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
+    #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
+    # EF[EF<0] = np.nan
+    # EF[EF>1] = np.nan
+    
+    # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
+    
+    ikey = 0
+    key = list(args.experiments.strip().split(' '))[ikey]
+    for varkey in ['h','theta','q']:
+        for input_key in ['wg','cc']:
+            data_all = pd.DataFrame()
+            data = pd.DataFrame()
+            data[varkey] = ""
+            data[varkey] = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            data["source"] = "soundings"
+            data_all = pd.concat([data_all,data])
+                
+            data = pd.DataFrame()
+            
+            
+            data[varkey] = ""
+            data[varkey] = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            data["source"] = "model"
+            data_all = pd.concat([data_all,data])
+            data_input = pd.concat([c4gldata[key].frames['stats']['records_all_stations_ini'],
+                                   c4gldata[key].frames['stats']['records_all_stations_ini']],axis=0)
+            input_key_full = input_key + "["+units[input_key]+"]"
+            data_all[input_key_full] =  pd.cut(x=data_input[input_key].values,bins=10)
+            
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+            data_all = data_all.rename(columns={varkey:varkey_full})
+            
+            qvalmax = data_all[varkey_full].quantile(0.999)
+            qvalmin = data_all[varkey_full].quantile(0.001)
+            data_all = data_all[(data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)]
+            
+            ax = fig.add_subplot(3,2,i)
+            sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+            ax.grid()
+            plt.xticks(rotation=45,ha='right')
+            i +=1
+            plt.legend(loc='lower right')
+    fig.tight_layout()
+    if args.figure_filename_2 is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:", args.figure_filename)
+    fig.show()
+
+
+
+
+
+
+
+
+
+
diff --git a/class4gl/interface/interface_eval_stations.py b/class4gl/interface/interface_eval_stations.py
new file mode 100644
index 0000000..49deebc
--- /dev/null
+++ b/class4gl/interface/interface_eval_stations.py
@@ -0,0 +1,290 @@
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=True)
+parser.add_argument('--figure_filename',default=None)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+
+
+latex = {}
+latex['dthetadt'] =  r'$d \theta / dt $'
+latex['dqdt'] =      r'$d q / dt $'
+latex['dhdt'] =      r'$d h / dt $'
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if args.load_globaldata:
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing+'/',\
+                      globaldata,\
+                      refetch_records=False
+                    )
+
+fig = plt.figure(figsize=(10,7))   #width,height
+i = 1                                                                           
+axes = {}         
+axes_taylor = {}         
+
+colors = ['r','g','b','m']
+symbols = ['*','x','+']
+dias = {}
+
+for varkey in ['h','theta','q']:                                                    
+    axes[varkey] = fig.add_subplot(2,3,i)                                       
+    #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+
+    #print(obs.std())
+    obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+    STD_OBS = obs.std()
+    dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+    if i == 2:
+        dias[varkey]._ax.axis["left"].label.set_text(\
+            "Standard deviation (model) / Standard deviation (observations)")
+        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+    #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+    # Q95 = obs.quantile(0.95)
+    # Q95 = obs.quantile(0.90)
+    # Add RMS contours, and label them
+    contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+    dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+    #dia._ax.set_title(season.capitalize())
+
+    dias[varkey].add_grid()
+
+
+    #dia.ax.plot(x99,y99,color='k')
+
+    
+    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        x, y = obs.values,mod.values
+        print(key,len(obs.values))
+
+        #scores
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+        
+        fit = np.polyfit(x,y,deg=1)
+        axes[varkey].plot(x, fit[0] * x + fit[1],\
+                          color=colors[ikey],alpha=0.8,lw=2,\
+                          label=key+", "+\
+                                      'R = '+str(round(PR,3))+', '+\
+                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+        axes[varkey].legend(fontsize=5)
+        
+        # print(STD)
+        # print(PR)
+        dias[varkey].add_sample(STD/STD_OBS, PR,
+                       marker='o', ms=5, ls='',
+                       #mfc='k', mec='k', # B&W
+                       mfc=colors[ikey], mec=colors[ikey], # Colors
+                       label=key)
+
+    # put ticker position, see
+    # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+    # dia.ax.axis['bottom'].
+    # dia.ax.axis['left'].
+    # dia.ax.axis['left'].
+
+    i += 1
+
+i = 0
+for varkey in ['h','theta','q']:                                                    
+    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        isymbol = 0
+        for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+            indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
+            station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
+            station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
+
+            axes[varkey].scatter(station_obs,station_mod,marker=symbols[isymbol],color=colors[ikey])
+                     #  label=key+", "+\
+                     #                    'R = '+str(round(PR[0],3))+', '+\
+                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+
+
+
+        # # pl.scatter(obs,mod,label=key+", "+\
+        # #                              'R = '+str(round(PR[0],3))+', '+\
+        # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+        # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+            
+            dias[varkey].add_sample(station_mod.std()/station_obs.std(),
+                           pearsonr(station_mod,station_obs)[0],
+                           marker=symbols[isymbol], ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=key)
+            isymbol += 1
+
+
+        axes[varkey].set_xlabel('observations')     
+        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+    if i==0:                                    
+        axes[varkey].set_ylabel('model')                                            
+    abline(1,0,axis=axes[varkey])
+    i +=1
+
+
+
+# legend for different forcing simulations (colors)
+ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+leg = []
+for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+    leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
+    leg.append(leg1)
+ax.axis('off')
+#leg1 =
+ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
+
+
+# legend for different stations (symbols)
+ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+leg = []
+isymbol = 0
+for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+    leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+    leg.append(leg1)
+    isymbol += 1
+
+# symbol for all stations
+leg1, = ax.plot([],'ko',markersize=10)
+leg.append(leg1)
+
+
+ax.axis('off')
+ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+
+
+fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+
+
+#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+#figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/iops_eval_report.png'
+
+if args.figure_filename is not None:
+    fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+fig.show()  
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
new file mode 100644
index 0000000..f6d718f
--- /dev/null
+++ b/class4gl/interface/interface_stations.py
@@ -0,0 +1,309 @@
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if bool(args.load_globaldata):
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing+'/',\
+                      globaldata,\
+                      refetch_records=False
+                    )
+
+# # the lines below activate TaylorPlots but it is disabled for now
+# fig = plt.figure(figsize=(10,7))   #width,height
+# i = 1                                                                           
+# axes = {}         
+# axes_taylor = {}         
+# 
+# colors = ['r','g','b','m']
+# symbols = ['*','x','+']
+# dias = {}
+# 
+# for varkey in ['h','theta','q']:                                                    
+#     axes[varkey] = fig.add_subplot(2,3,i)                                       
+#     #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+# 
+#     #print(obs.std())
+#     dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+#     if i == 0:
+#         dias[varkey]._ax.axis["left"].label.set_text(\
+#             "Standard deviation (model) / Standard deviation (observations)")
+#         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+#     #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#     # Q95 = obs.quantile(0.95)
+#     # Q95 = obs.quantile(0.90)
+#     # Add RMS contours, and label them
+#     contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+#     dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+#     #dia._ax.set_title(season.capitalize())
+# 
+#     dias[varkey].add_grid()
+# 
+# 
+#     #dia.ax.plot(x99,y99,color='k')
+# 
+#     
+#     for ikey,key in enumerate(args.experiments.split(';')):
+#         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+#         # clearsky = (cc < 0.05)
+#         # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+#         # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+#         mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+#         obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+#         x, y = obs.values,mod.values
+#         print(key,len(obs.values))
+# 
+#         STD_OBS = obs.std()
+#         #scores
+#         PR = pearsonr(mod,obs)[0]
+#         RMSE = rmse(obs,mod)                                               
+#         BIAS = np.mean(mod) - np.mean(obs)
+#         STD = mod.std()
+#         
+#         # fit = np.polyfit(x,y,deg=1)
+#         # axes[varkey].plot(x, fit[0] * x + fit[1],\
+#         #                   color=colors[ikey],alpha=0.8,lw=2,\
+#         #                   label=key+", "+\
+#         #                               'R = '+str(round(PR,3))+', '+\
+#         #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+#         #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+#         # axes[varkey].legend(fontsize=5)
+#         
+#         # print(STD)
+#         # print(PR)
+#         dias[varkey].add_sample(STD/STD_OBS, PR,
+#                        marker='o', ms=5, ls='',
+#                        #mfc='k', mec='k', # B&W
+#                        mfc=colors[ikey], mec=colors[ikey], # Colors
+#                        label=key)
+# 
+#     # put ticker position, see
+#     # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+#     # dia.ax.axis['bottom'].
+#     # dia.ax.axis['left'].
+#     # dia.ax.axis['left'].
+# 
+#     i += 1
+# 
+# i = 0
+# for varkey in ['h','theta','q']:                                                    
+#     ikey = 0
+#     key = list(args.experiments.split(';'))[ikey]
+#     cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+#     clearsky = (cc < 0.05)
+# 
+#     mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+#     obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+# 
+# 
+#     nbins=40       
+#     x, y = obs.values,mod.values
+#     
+#     xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+#     zi = np.zeros_like(xi)*np.nan       
+#     for ibin in range(nbins):
+#         xmin = x.min() + ibin * (x.max() - x.min())/nbins
+#         xmax = xmin + (x.max() - x.min())/nbins
+#         in_bin = ((x >= xmin) & (x < xmax))
+#         ybin = y[in_bin]
+#         xbin = x[in_bin]
+#         if len(ybin) > 20:
+#             k = kde.gaussian_kde((ybin))
+#             zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+#     zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+#     zi_int = zi.cumsum(axis=1) 
+#                  #  label=key+", "+\
+#                  #                    'R = '+str(round(PR[0],3))+', '+\
+#                  #                    'RMSE = '+str(round(RMSE,5))+', '+\
+#                  #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+#     axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+#             colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+#     axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+#             colors=['darkred'],alpha=0.5,)
+# 
+# 
+#     latex = {}
+#     latex['dthetadt'] =  r'$d \theta / dt $'
+#     latex['dqdt'] =      r'$d q / dt $'
+#     latex['dhdt'] =      r'$d h / dt $'
+# 
+#     axes[varkey].set_xlabel('observations')     
+#     axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+# 
+#     PR = pearsonr(mod,obs)[0]
+#     RMSE = rmse(obs,mod)                                               
+#     BIAS = np.mean(mod) - np.mean(obs)
+#     STD = mod.std()
+# 
+#     axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+#                                   'R = '+str(round(PR,3))+', '+\
+#                                   'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+#                                   'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+#                          s=0.1,alpha=0.14,color='k')
+#     axes[varkey].legend(fontsize=5)
+#                    
+#     axes[varkey].set_xlabel('observations')     
+#     if i==0:                                    
+#         axes[varkey].set_ylabel('model')                                            
+#     abline(1,0,axis=axes[varkey])
+#     i +=1
+# 
+# 
+# 
+# # legend for different forcing simulations (colors)
+# ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# leg = []
+# for ikey,key in enumerate(args.experiments.split(';')):
+#     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+#     leg.append(leg1)
+# ax.axis('off')
+# #leg1 =
+# ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
+# 
+# 
+# # # legend for different stations (symbols)
+# # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+# # leg = []
+# # isymbol = 0
+# # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+# #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+# #     leg.append(leg1)
+# #     isymbol += 1
+# # 
+# # # symbol for all stations
+# # leg1, = ax.plot([],'ko',markersize=10)
+# # leg.append(leg1)
+# 
+# 
+# # ax.axis('off')
+# # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+# 
+# 
+# fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+# 
+# 
+# #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+# # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+# # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+# fig.show()  
+
+
+
+
+
+
+
+
+
+
+
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 25bde26..8672499 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -826,7 +826,7 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
         frames = self.frames
         fig = self.fig
  
-        if globaldata is not None:
+        if self.globaldata is not None:
             if (only is None) or ('worldmap' in only):
                 globaldata = self.globaldata
                 if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:

From 41b6f19309a9f3e45f82bdacd491a0968a5bf51e Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 08:53:24 +0200
Subject: [PATCH 053/129] evaluation interfaces

---
 trash/data_ground.py | 393 -------------------------------------------
 1 file changed, 393 deletions(-)
 delete mode 100644 trash/data_ground.py

diff --git a/trash/data_ground.py b/trash/data_ground.py
deleted file mode 100644
index d4e0b5a..0000000
--- a/trash/data_ground.py
+++ /dev/null
@@ -1,393 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Nov  7 10:51:03 2017
-
-@author: vsc42247
-
-Purpose: Set surface conditions for the CLASS boundary-layer model
-"""
-
-
-import netCDF4 as nc4
-import numpy as np
-import datetime as dt
-#you can install with
-import pynacolada as pcd
-import pandas as pd
-
-def get_class4gl_ground(class_settings,**kwargs):   
-    
-    key = "IGBPDIS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-    
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc"
-        print('reading soil water saturation from '+input_fn)
-
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wsat'] = input_nc.variables['wsat'][ilon,ilat]
-        input_nc.close()
-
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc"
-        print('reading soil water field capacity from '+input_fn)
-    
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wfc'] = input_nc.variables['wfc'][ilon,ilat]
-        input_nc.close()
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc"
-        print('reading soil wilting point from '+input_fn)
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__['wwilt'] = input_nc.variables['wwp'][ilon,ilat]
-        input_nc.close()
-        
-    key = "GLEAM"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-        
-        #INPUT_gleam = gleam() 
-        #INPUT_gleam.path = "/kyukon/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/" 
-        
-        gleam_path = "/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/"
-        print('reading soil-water content for "+str(class_settings,datetime.year)+" from '+gleam_path)
-        
-        gleam_files = {}
-        
-        gleam_vars = ['SMroot','SMsurf']
-        
-        for VAR in gleam_vars:
-            gleam_files[VAR] = nc4.Dataset(gleam_path+'/'+str(class_settings.datetime.year)+'/'+VAR+'_'+str(class_settings.datetime.year)+'_GLEAM_v3.1a.nc','r')
-        
-
-        year = class_settings.datetime.year
-        day = class_settings.datetime.day
-        hour = class_settings.datetime.hour
-  
-        ilat = np.where(gleam_files['SMsurf'].variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(gleam_files['SMsurf'].variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        VAR = 'SMsurf'; class_settings.wg = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
-        VAR = 'SMroot'; class_settings.w2 = gleam_files[VAR].variables[VAR][day-1,ilon,ilat]
-        
-        for VAR in gleam_vars:
-            gleam_files[VAR].close()
-    
-    key = "MOD44B"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-    
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc"
-        print('initializing vegetation fraction from '+input_fn)
-        var = 'cveg'
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        class_settings.__dict__[var] = input_nc.variables['fv'][ilon,ilat]
-        input_nc.close()
-        
-    key = "DSMW"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):
-         # Procedure of the thermal properties:
-         # 1. determine soil texture from DSMW
-         # 2. soil type with look-up table (according to DWD/EXTPAR)
-         # 3. Thermal properties used in the force-restore method (Clapp and Hornberger, 1987) 
-         #    with parameter look-up table from Noilhan and Planton (1989). 
-         #    Note: The look-up table is inspired on DWD/COSMO
-                 
-       
-        #preparing for soil thermal properties
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc"
-        
-        print("deriving soil thermal properties for the force-restore methodes from the soil texture file "+ input_fn)
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        DSMW = input_nc.variables['DSMW'][ilat,ilon]
-        
-        
-        #EXTPAR: zfine   = soil_texslo(soil_unit)%tex_fine
-        SP = {}; SPKEYS = ['tex_coarse', 'tex_medium', 'tex_fine', 'code']
-        for SPKEY in SPKEYS: 
-            SP[SPKEY] = np.array(input_nc.variables[SPKEY][DSMW])
-        input_nc.close()
-        
-        SP['texture'] = (0.5*SP['tex_medium']+1.0*SP['tex_coarse']) /(SP['tex_coarse']+SP['tex_medium']+SP['tex_fine'])
-        
-        if pd.isnull(SP['texture']):
-            print('Warning, texture is invalid> Setting to Ocean')
-            SP['itex'] = 9
-        
-        else:
-            SP['itex'] = int(SP['texture']*100)
-        
-        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-        SP['isoil'] = np.zeros_like(SP['itex'],dtype=np.int)
-        LOOKUP = [
-                  [0 ,7],# fine textured, clay (soil type 7)
-                  [20,6],# medium to fine textured, loamy clay (soil type 6)
-                  [40,5],# medium textured, loam (soil type 5)
-                  [60,4],# coarse to medium textured, sandy loam (soil type 4)
-                  [80,3],# coarse textured, sand (soil type 3)
-                ]
-        for iitex,iisoil in LOOKUP: 
-            SP['isoil'][SP['itex'] >= iitex ] = iisoil 
-        
-        #adopted from mo_agg_soil.f90 (EXTPAR3.0)
-        LOOKUP = [
-                  [9001, 1 ], # ice, glacier (soil type 1) 
-                  [9002, 2 ], # rock, lithosols (soil type 2)
-                  [9003, 3 ], # salt, set soiltype to sand (soil type 3)
-                  [9004, 8 ], # histosol, e.g. peat (soil type 8)
-                  [9,    9 ], # undefined (ocean)
-                  [9005, 3 ], # shifting sands or dunes, set soiltype to sand (soil type 3)
-                  [9000, 9 ], # undefined (inland lake)
-                  [9009, 5 ], #  default_soiltype ! undefined (nodata), set soiltype to loam (soil type )
-                  [9012, 5 ], #  default_soiltype undefined (dominant part undefined), set soiltype to loam (soil type 5)
-                ]
-        # EXTPAR: soil_code = soil_texslo(soil_unit)%dsmw_code # the legend has some special cases for the "soil_code"
-        for icode,iisoil in LOOKUP: 
-            SP['isoil'][SP['code'] == icode] = iisoil 
-        
-        #adopted from data_soil.f90 (COSMO5.0)
-        SP_LOOKUP = { 
-          # soil type:         ice        rock       sand        sandy      loam         clay        clay        peat        sea        sea  
-          # (by index)                                           loam                    loam                                water      ice
-          'cporv'  : [ np.nan, 1.E-10   , 1.E-10   , 0.364     , 0.445     , 0.455     , 0.475     , 0.507     , 0.863     , 1.E-10   , 1.E-10   ],
-          'cfcap'  : [ np.nan, 1.E-10   , 1.E-10   , 0.196     , 0.260     , 0.340     , 0.370     , 0.463     , 0.763     , 1.E-10   , 1.E-10   ],
-          'cpwp'   : [ np.nan, 0.0      , 0.0      , 0.042     , 0.100     , 0.110     , 0.185     , 0.257     , 0.265     , 0.0      ,  0.0     ],
-          'cadp'   : [ np.nan, 0.0      , 0.0      , 0.012     , 0.030     , 0.035     , 0.060     , 0.065     , 0.098     , 0.0      ,  0.0     ],
-          'crhoc'  : [ np.nan, 1.92E6   , 2.10E6   , 1.28E6    , 1.35E6    , 1.42E6    , 1.50E6    , 1.63E6    , 0.58E6    , 4.18E6   , 1.92E6   ],
-          'cik2'   : [ np.nan, 0.0      , 0.0      , 0.0035    , 0.0023    , 0.0010    , 0.0006    , 0.0001    , 0.0002    , 0.0      ,  0.0     ],
-          'ckw0'   : [ np.nan, 0.0      , 0.0      , 479.E-7   , 943.E-8   , 531.E-8   , 764.E-9   , 17.E-9    , 58.E-9    , 0.0      ,  0.0     ],
-          'ckw1'   : [ np.nan, 0.0      , 0.0      , -19.27    , -20.86    , -19.66    , -18.52    , -16.32    , -16.48    , 0.0      ,  0.0     ],
-          'cdw0'   : [ np.nan, 0.0      , 0.0      , 184.E-7   , 346.E-8   , 357.E-8   , 118.E-8   , 442.E-9   , 106.E-9   , 0.0      ,  0.0     ],
-          'cdw1'   : [ np.nan, 0.0      , 0.0      , -8.45     , -9.47     , -7.44     , -7.76     , -6.74     , -5.97     , 0.0      ,  0.0     ],
-          'crock'  : [ np.nan, 0.0      , 0.0      , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 1.0       , 0.0      ,  0.0     ],
-          'cala0'  : [ np.nan, 2.26     , 2.41     , 0.30      , 0.28      , 0.25      , 0.21      , 0.18      , 0.06      , 1.0      ,  2.26    ],
-          'cala1'  : [ np.nan, 2.26     , 2.41     , 2.40      , 2.40      , 1.58      , 1.55      , 1.50      , 0.50      , 1.0      ,  2.26    ],
-          'csalb'  : [ np.nan, 0.70     , 0.30     , 0.30      , 0.25      , 0.25      , 0.25      , 0.25      , 0.20      , 0.07     ,  0.70    ],
-          'csalbw' : [ np.nan, 0.00     , 0.00     , 0.44      , 0.27      , 0.24      , 0.23      , 0.22      , 0.10      , 0.00     ,  0.00    ],
-          'ck0di'  : [ np.nan, 1.E-4    , 1.E-4    , 2.E-4     , 2.E-5     , 6.E-6     , 2.E-6     , 1.E-6     , 1.5E-6    , 0.00     ,  0.00    ],
-          'cbedi'  : [ np.nan, 1.00     , 1.00     , 3.5       , 4.8       , 6.1       , 8.6       , 10.0      , 9.0       , 0.00     ,  0.00    ],
-          'csandf' : [ np.nan, 0.0      , 0.0      , 90.       , 65.       , 40.       , 35.       , 15.       , 90.       , 0.00     ,  0.00    ],
-          'cclayf' : [ np.nan, 0.0      , 0.0      , 5.0       , 10.       , 20.       , 35.       , 70.       , 5.0       , 0.00     ,  0.00    ],
-          #supplement Noihhan andf Planton 1989 soil texture parameters for the force-restore method.
-          'b'      : [ np.nan, np.nan   , np.nan   , 4.05      , 4.90      , 5.39      , 8.52      , 11.40     , np.nan    , np.nan   ,  np.nan  ],
-          #error in table 2 of NP89: values need to be multiplied by e-6
-          'CGsat'  : [ np.nan, np.nan   , np.nan   , 3.222e-6     , 3.560e-6     , 4.111e-6     , 3.995e-6     , 3.600e-6     , np.nan    , np.nan   ,  np.nan  ],
-          'p'  :     [ np.nan, np.nan   , np.nan   , 4.        , 4.        , 6.        , 10.       , 12.       , np.nan    , np.nan   ,  np.nan  ],
-          'a'  :     [ np.nan, np.nan   , np.nan   , 0.387     , 0.219     , 0.148     , 0.084     , 0.083     , np.nan    , np.nan   ,  np.nan  ],
-          'C1sat'  : [ np.nan, np.nan   , np.nan   , 0.082     , 0.132     , 0.191     , 0.227     , 0.342     , np.nan    , np.nan   ,  np.nan  ],
-          'C2ref'  : [ np.nan, np.nan   , np.nan   , 3.9       , 1.8       , 0.8       , 0.6       , 0.3       , np.nan    , np.nan   ,  np.nan  ],
-        }
-        
-        for SPKEY in SP_LOOKUP.keys(): 
-            SP[SPKEY] = np.zeros_like(SP['isoil'],dtype=np.float)
-        
-        for i in range(11):
-            SELECT = (SP['isoil'] == i)
-            for SPKEY in SP_LOOKUP.keys(): 
-                SP[SPKEY][SELECT] = SP_LOOKUP[SPKEY][i]
-        
-        for SPKEY in list(SP_LOOKUP.keys())[-6:]: 
-            var = SPKEY
-            class_settings.__dict__[var] = np.float(SP[SPKEY])
-            
-        # only print the last parameter value in the plot
-        
-        #inputs.append(cp.deepcopy(class_settings))
-        #var = 'cala'
-        #class_settings.__dict__[var] = np.float(SP['cala0'])
-        #valnew = class_settings.__dict__[var]
-        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-        
-        #inputs.append(cp.deepcopy(class_settings))
-        #var = 'crhoc'
-        #class_settings.__dict__[var] = np.float(SP['crhoc'])
-        #valnew = class_settings.__dict__[var]
-        #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-        
-    key = "CERES"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
-        
-        CERES_start_date = dt.datetime(2000,3,1)
-        DT_CERES_START = (CERES_start_date + dt.timedelta(days=(int((class_settings.datetime - CERES_start_date ).days/61) * 61)))
-        DT_CERES_END   = DT_CERES_START +dt.timedelta(days=60)
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_"+DT_CERES_START.strftime("%Y%m%d")+"-"+DT_CERES_END.strftime("%Y%m%d")+".nc"
-        print("Reading afternoon cloud cover for "+str(class_settings.datetime)+" from "+input_fn)
-            
-        var = 'cc'
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        print(class_settings.lat,class_settings.lon)
-        
-        class_settings.__dict__[var] = np.nanmean(input_nc.variables['cldarea_total_1h'][idatetime:(idatetime+class_settings.runtime),ilat,ilon])/100.
-   
-        input_nc.close()
-    
-    key = "GIMMS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):    
-       
-    
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean.nc"
-        print("Reading Leag Area Index from "+input_fn)
-        var = 'LAI'
-        
-        #plt.plot
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        #idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        # divide by cveg, since it only reflects the LAI for the vegetation fraction and not for the entire (satellite) grid cell
-        
-        print('Warning! Dividing by cveg, which is: '+str(class_settings.cveg))
-        tarray = np.array(input_nc.variables['LAI'][:,ilatitude,ilongitude])/class_settings.cveg
-        
-        if np.isnan(tarray[idatetime]):
-            print("interpolating GIMMS cveg nan value")
-            
-            mask = np.isnan(tarray)
-            if np.where(mask)[0].shape[0] < 0.25*mask.shape[0]:
-                tarray[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), tarray[~mask])
-            else:
-                print("Warning. Could not interpolate GIMMS cveg nan value")
-                
-        class_settings.__dict__[var] = tarray[idatetime]
-        
-        input_nc.close()
- 
-    key = "IGBPDIS_ALPHA"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        var = 'alpha'
-        
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc"
-        print("Reading albedo from "+input_fn)
-    
-        input_nc = nc4.Dataset(input_fn,'r')
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][-1]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        
-        landfr = {}
-        for ltype in ['W','B','H','TC']:   
-            landfr[ltype] = input_nc.variables['f'+ltype][0,ilon,ilat]
-        
-        aweights = {'W':0.075,'TC':0.15,'H':0.22,'B':0.30}
-        
-        alpha=0.
-        for ltype in landfr.keys():
-            alpha += landfr[ltype]*aweights[ltype]
-        
-        
-        class_settings.__dict__[var] = alpha
-        input_nc.close()        
-        
-        
-    key = "ERAINT_ST"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly/stl1_'+str(class_settings.datetime.year)+"_3hourly.nc"
-        print("Reading soil temperature from "+input_fn)
-        
-        var = 'Tsoil'
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-        
-        
-        class_settings.__dict__[var] = input_nc.variables['stl1'][idatetime,ilatitude,ilongitude]
-        
-        input_fn = '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly/stl2_'+str(class_settings.datetime.year)+"_3hourly.nc"
-        var = 'T2'
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        idatetime = np.where(np.array(pcd.ncgetdatetime(input_nc))  >= class_settings.datetime)[0][0]
-        
-        ilatitude = np.where(input_nc.variables['latitude'][:] >= class_settings.lat)[0][-1]
-        ilongitude = np.where(input_nc.variables['longitude'][:] >= class_settings.lon)[0][0]
-        
-        
-        class_settings.__dict__[var] = input_nc.variables['stl2'][idatetime,ilatitude,ilongitude]
-        
-        
-        input_nc.close()
-        
-        
-    
-    #inputs.append(cp.deepcopy(class_settings))
-    #var = 'T2'
-    #valold = class_settings.__dict__[var]
-    #
-    #class_settings.__dict__[var] = 305.
-    #class_settings.__dict__['Tsoil'] = 302.
-    #valnew = class_settings.__dict__[var]
-    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-    
-    
-    
-    #inputs.append(cp.deepcopy(class_settings))
-    #
-    #var = 'Lambda'
-    #valold = class_settings.__dict__[var]
-    
-    ## I presume that the skin layer conductivity scales with both LAI and vegetation fraction, which seems ~ valid according to table 10.6 in CLASS-book. 
-    ## I need to ask Chiel.
-    ## I extrapolate from Lambda value of grass with Lambda = 5.9 W m-2 K-1, LAI = 2 and cveg = 0.85
-    #
-    #valnew = 5.9 / 2. / 0.85 * class_settings.__dict__['LAI'] * class_settings.__dict__['cveg'] 
-    #class_settings.__dict__[var] = valnew
-    #labels.append(var+': '+format(valold,"0.2g")+'->'+format(valnew,"0.2g"))
-    
-    
-    
-    key = "GLAS"
-    if ((kwargs == {}) or ((key in kwargs.keys()) and (kwargs[key]))):          
-       
-        input_fn = "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc"
-        print("Reading canopy height for determining roughness length from "+input_fn)
-        var = 'z0m'
-    
-        
-        #plt.plot
-        
-        input_nc = nc4.Dataset(input_fn,'r')
-        
-        ilat = np.where(input_nc.variables['lat'][:] >= class_settings.lat)[0][0]
-        ilon = np.where(input_nc.variables['lon'][:] >= class_settings.lon)[0][0]
-        
-        testval = np.float64(input_nc.variables['Band1'][ilat,ilon])/10.
-        
-        lowerlimit = 0.01
-        if testval < lowerlimit:
-            print('forest canopy height very very small. We take a value of '+str(lowerlimit))
-            class_settings.__dict__[var] = lowerlimit
-        else:
-            class_settings.__dict__[var] = testval
-        
-        class_settings.__dict__['z0h'] =  class_settings.__dict__['z0m']/10.
-        
-        
-        input_nc.close()
-        

From 60ae5ece37a3f681c988552303c1f1bf098795c0 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 09:14:53 +0200
Subject: [PATCH 054/129] modified:   .gitignore modified:  
 class4gl/interface/interface.py deleted:   
 class4gl/interface/interface_eval.py deleted:   
 class4gl/interface/interface_eval_stations.py modified:  
 class4gl/interface/interface_stations.py

---
 .gitignore                                    |   2 +-
 class4gl/interface/interface.py               | 433 ++++++++++--------
 class4gl/interface/interface_eval.py          | 375 ---------------
 class4gl/interface/interface_eval_stations.py | 290 ------------
 class4gl/interface/interface_stations.py      | 343 +++++++-------
 5 files changed, 411 insertions(+), 1032 deletions(-)
 delete mode 100644 class4gl/interface/interface_eval.py
 delete mode 100644 class4gl/interface/interface_eval_stations.py

diff --git a/.gitignore b/.gitignore
index d5a1137..b65667c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -7,7 +7,7 @@ class4gl/__pycache__/*
 .*
 build/
 dist/
-
+trash/
 */__pychache__/
 *.py[cod]
 *$py.class
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index f6d718f..72b970e 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -13,6 +13,10 @@
 parser.add_argument('--experiments')
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
 parser.add_argument('--load_globaldata',default=False)
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--show_control_parameters',default=True)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--figure_filename_2',default=None)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -118,192 +122,249 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-# # the lines below activate TaylorPlots but it is disabled for now
-# fig = plt.figure(figsize=(10,7))   #width,height
-# i = 1                                                                           
-# axes = {}         
-# axes_taylor = {}         
-# 
-# colors = ['r','g','b','m']
-# symbols = ['*','x','+']
-# dias = {}
-# 
-# for varkey in ['h','theta','q']:                                                    
-#     axes[varkey] = fig.add_subplot(2,3,i)                                       
-#     #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
-# 
-#     #print(obs.std())
-#     dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-#     if i == 0:
-#         dias[varkey]._ax.axis["left"].label.set_text(\
-#             "Standard deviation (model) / Standard deviation (observations)")
-#         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-#         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
-#     #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-#     # Q95 = obs.quantile(0.95)
-#     # Q95 = obs.quantile(0.90)
-#     # Add RMS contours, and label them
-#     contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-#     dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
-#     #dia._ax.set_title(season.capitalize())
-# 
-#     dias[varkey].add_grid()
-# 
-# 
-#     #dia.ax.plot(x99,y99,color='k')
-# 
-#     
-#     for ikey,key in enumerate(args.experiments.split(';')):
-#         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-#         # clearsky = (cc < 0.05)
-#         # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-#         # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-#         mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-#         obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-#         x, y = obs.values,mod.values
-#         print(key,len(obs.values))
-# 
-#         STD_OBS = obs.std()
-#         #scores
-#         PR = pearsonr(mod,obs)[0]
-#         RMSE = rmse(obs,mod)                                               
-#         BIAS = np.mean(mod) - np.mean(obs)
-#         STD = mod.std()
-#         
-#         # fit = np.polyfit(x,y,deg=1)
-#         # axes[varkey].plot(x, fit[0] * x + fit[1],\
-#         #                   color=colors[ikey],alpha=0.8,lw=2,\
-#         #                   label=key+", "+\
-#         #                               'R = '+str(round(PR,3))+', '+\
-#         #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-#         #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-#         # axes[varkey].legend(fontsize=5)
-#         
-#         # print(STD)
-#         # print(PR)
-#         dias[varkey].add_sample(STD/STD_OBS, PR,
-#                        marker='o', ms=5, ls='',
-#                        #mfc='k', mec='k', # B&W
-#                        mfc=colors[ikey], mec=colors[ikey], # Colors
-#                        label=key)
-# 
-#     # put ticker position, see
-#     # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
-#     # dia.ax.axis['bottom'].
-#     # dia.ax.axis['left'].
-#     # dia.ax.axis['left'].
-# 
-#     i += 1
-# 
-# i = 0
-# for varkey in ['h','theta','q']:                                                    
-#     ikey = 0
-#     key = list(args.experiments.split(';'))[ikey]
-#     cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-#     clearsky = (cc < 0.05)
-# 
-#     mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-#     obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-# 
-# 
-#     nbins=40       
-#     x, y = obs.values,mod.values
-#     
-#     xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
-#     zi = np.zeros_like(xi)*np.nan       
-#     for ibin in range(nbins):
-#         xmin = x.min() + ibin * (x.max() - x.min())/nbins
-#         xmax = xmin + (x.max() - x.min())/nbins
-#         in_bin = ((x >= xmin) & (x < xmax))
-#         ybin = y[in_bin]
-#         xbin = x[in_bin]
-#         if len(ybin) > 20:
-#             k = kde.gaussian_kde((ybin))
-#             zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
-#     zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
-#     zi_int = zi.cumsum(axis=1) 
-#                  #  label=key+", "+\
-#                  #                    'R = '+str(round(PR[0],3))+', '+\
-#                  #                    'RMSE = '+str(round(RMSE,5))+', '+\
-#                  #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-#     axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
-#             colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
-#     axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
-#             colors=['darkred'],alpha=0.5,)
-# 
-# 
-#     latex = {}
-#     latex['dthetadt'] =  r'$d \theta / dt $'
-#     latex['dqdt'] =      r'$d q / dt $'
-#     latex['dhdt'] =      r'$d h / dt $'
-# 
-#     axes[varkey].set_xlabel('observations')     
-#     axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-# 
-#     PR = pearsonr(mod,obs)[0]
-#     RMSE = rmse(obs,mod)                                               
-#     BIAS = np.mean(mod) - np.mean(obs)
-#     STD = mod.std()
-# 
-#     axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
-#                                   'R = '+str(round(PR,3))+', '+\
-#                                   'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-#                                   'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
-#                          s=0.1,alpha=0.14,color='k')
-#     axes[varkey].legend(fontsize=5)
-#                    
-#     axes[varkey].set_xlabel('observations')     
-#     if i==0:                                    
-#         axes[varkey].set_ylabel('model')                                            
-#     abline(1,0,axis=axes[varkey])
-#     i +=1
-# 
-# 
-# 
-# # legend for different forcing simulations (colors)
-# ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-# leg = []
-# for ikey,key in enumerate(args.experiments.split(';')):
-#     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
-#     leg.append(leg1)
-# ax.axis('off')
-# #leg1 =
-# ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
-# 
-# 
-# # # legend for different stations (symbols)
-# # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-# # leg = []
-# # isymbol = 0
-# # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-# #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
-# #     leg.append(leg1)
-# #     isymbol += 1
-# # 
-# # # symbol for all stations
-# # leg1, = ax.plot([],'ko',markersize=10)
-# # leg.append(leg1)
-# 
-# 
-# # ax.axis('off')
-# # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
-# 
-# 
-# fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
-# 
-# 
-# #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
-# # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
-# # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
-# fig.show()  
-
-
-
-
-
-
-
-
+if args.show_figures:
+    # the lines below activate TaylorPlots but it is disabled for now
+    fig = plt.figure(figsize=(10,7))   #width,height
+    i = 1                                                                           
+    axes = {}         
+    axes_taylor = {}         
+    
+    colors = ['r','g','b','m']
+    symbols = ['*','x','+']
+    dias = {}
+    
+    for varkey in ['h','theta','q']:                                                    
+        axes[varkey] = fig.add_subplot(2,3,i)                                       
+        #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+    
+        #print(obs.std())
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        if i == 0:
+            dias[varkey]._ax.axis["left"].label.set_text(\
+                "Standard deviation (model) / Standard deviation (observations)")
+            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # Q95 = obs.quantile(0.95)
+        # Q95 = obs.quantile(0.90)
+        # Add RMS contours, and label them
+        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        #dia._ax.set_title(season.capitalize())
+    
+        dias[varkey].add_grid()
+    
+    
+        #dia.ax.plot(x99,y99,color='k')
+    
+        
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+            # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+            # clearsky = (cc < 0.05)
+            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            x, y = obs.values,mod.values
+            print(key,len(obs.values))
+    
+            STD_OBS = obs.std()
+            #scores
+            PR = pearsonr(mod,obs)[0]
+            RMSE = rmse(obs,mod)                                               
+            BIAS = np.mean(mod) - np.mean(obs)
+            STD = mod.std()
+            
+            # fit = np.polyfit(x,y,deg=1)
+            # axes[varkey].plot(x, fit[0] * x + fit[1],\
+            #                   color=colors[ikey],alpha=0.8,lw=2,\
+            #                   label=key+", "+\
+            #                               'R = '+str(round(PR,3))+', '+\
+            #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+            #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+            # axes[varkey].legend(fontsize=5)
+            
+            # print(STD)
+            # print(PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,
+                           marker='o', ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=key)
+    
+        # put ticker position, see
+        # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+        # dia.ax.axis['bottom'].
+        # dia.ax.axis['left'].
+        # dia.ax.axis['left'].
+    
+        i += 1
+    
+    i = 0
+    for varkey in ['h','theta','q']:                                                    
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        clearsky = (cc < 0.05)
+    
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+    
+    
+        nbins=40       
+        x, y = obs.values,mod.values
+        
+        xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+        zi = np.zeros_like(xi)*np.nan       
+        for ibin in range(nbins):
+            xmin = x.min() + ibin * (x.max() - x.min())/nbins
+            xmax = xmin + (x.max() - x.min())/nbins
+            in_bin = ((x >= xmin) & (x < xmax))
+            ybin = y[in_bin]
+            xbin = x[in_bin]
+            if len(ybin) > 20:
+                k = kde.gaussian_kde((ybin))
+                zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+        zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+        zi_int = zi.cumsum(axis=1) 
+                     #  label=key+", "+\
+                     #                    'R = '+str(round(PR[0],3))+', '+\
+                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+                colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+                colors=['darkred'],alpha=0.5,)
+    
+    
+        latex = {}
+        latex['dthetadt'] =  r'$d \theta / dt $'
+        latex['dqdt'] =      r'$d q / dt $'
+        latex['dhdt'] =      r'$d h / dt $'
+    
+        axes[varkey].set_xlabel('observations')     
+        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+    
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+    
+        axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+                                      'R = '+str(round(PR,3))+', '+\
+                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+                             s=0.1,alpha=0.14,color='k')
+        axes[varkey].legend(fontsize=5)
+                       
+        axes[varkey].set_xlabel('observations')     
+        if i==0:                                    
+            axes[varkey].set_ylabel('model')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+    
+    
+    
+    # legend for different forcing simulations (colors)
+    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    leg = []
+    for ikey,key in enumerate(args.experiments.strip().split(' ')):
+        leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+        leg.append(leg1)
+    ax.axis('off')
+    #leg1 =
+    ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+    
+    
+    # # legend for different stations (symbols)
+    # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # isymbol = 0
+    # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+    #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+    #     leg.append(leg1)
+    #     isymbol += 1
+    # 
+    # # symbol for all stations
+    # leg1, = ax.plot([],'ko',markersize=10)
+    # leg.append(leg1)
+    
+    
+    # ax.axis('off')
+    # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    
+    
+    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+    
+    
+    #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+    # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+    # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+    
+    if args.figure_filename is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+    fig.show()  
+    
+    
+    if bool(args.show_control_parameters):
+        import seaborn as sns
+        sns.set()
+        fig = pl.figure(figsize=(12,8))
+        i = 1
+        axes = {}
+        data_all = pd.DataFrame()
+        
+        
+        
+        # #for varkey in ['theta','q']:     
+        # EF =\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
+        # EF[EF<0] = np.nan
+        # EF[EF>1] = np.nan
+        
+        # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
+        
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        for varkey in ['h','theta','q']:
+            for input_key in ['wg','cc']:
+                data_all = pd.DataFrame()
+                data = pd.DataFrame()
+                data[varkey] = ""
+                data[varkey] = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+                data["source"] = "soundings"
+                data_all = pd.concat([data_all,data])
+                    
+                data = pd.DataFrame()
+                
+                
+                data[varkey] = ""
+                data[varkey] = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+                data["source"] = "model"
+                data_all = pd.concat([data_all,data])
+                data_input = pd.concat([c4gldata[key].frames['stats']['records_all_stations_ini'],
+                                       c4gldata[key].frames['stats']['records_all_stations_ini']],axis=0)
+                input_key_full = input_key + "["+units[input_key]+"]"
+                data_all[input_key_full] =  pd.cut(x=data_input[input_key].values,bins=10)
+                
+                varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+                data_all = data_all.rename(columns={varkey:varkey_full})
+                
+                qvalmax = data_all[varkey_full].quantile(0.999)
+                qvalmin = data_all[varkey_full].quantile(0.001)
+                data_all = data_all[(data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)]
+                
+                ax = fig.add_subplot(3,2,i)
+                sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+                ax.grid()
+                plt.xticks(rotation=45,ha='right')
+                i +=1
+                plt.legend(loc='lower right')
+        fig.tight_layout()
+        if args.figure_filename_2 is not None:
+            fig.savefig(args.figure_filename,dpi=200); print("Image file written to:", args.figure_filename)
+        fig.show()
 
 
 
diff --git a/class4gl/interface/interface_eval.py b/class4gl/interface/interface_eval.py
deleted file mode 100644
index f72d4fc..0000000
--- a/class4gl/interface/interface_eval.py
+++ /dev/null
@@ -1,375 +0,0 @@
-import numpy as np
-
-import pandas as pd
-import sys
-
-import matplotlib
-matplotlib.use('TkAgg')
-
-import argparse
-parser = argparse.ArgumentParser()
-parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--experiments')
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load_globaldata',default=True)
-parser.add_argument('--show_control_parameters',default=True)
-parser.add_argument('--figure_filename',default=None)
-parser.add_argument('--figure_filename_2',default=None)
-args = parser.parse_args()
-
-print('Adding python library:',args.c4gl_path_lib)
-sys.path.insert(0, args.c4gl_path_lib)
-from interface_multi import c4gl_interface_soundings,get_record_yaml
-from class4gl import class4gl_input, data_global,class4gl,units
-#from sklearn.metrics import mean_squared_error
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-#import seaborn.apionly as sns
-import pylab as pl
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.stats import kde
-from scipy.stats import pearsonr                                                
-from taylorDiagram import TaylorDiagram
-from matplotlib import ticker
-# import importlib
-# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
-
-
-
-
-def abline(slope, intercept,axis):
-    """Plot a line from slope and intercept"""
-    #axis = plt.gca()
-    x_vals = np.array(axis.get_xlim())
-    y_vals = intercept + slope * x_vals
-    axis.plot(x_vals, y_vals, 'k--')
-
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    rmse_temp = (y_actual_temp - y_predicted_temp)
-    rmse_temp = np.mean(rmse_temp*rmse_temp)
-    return np.sqrt(rmse_temp)
-
-
-
-
-
-# EXPS  =\
-# {
-# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-# }
-
-if args.load_globaldata:
-    # iniitialize global data
-    globaldata = data_global()
-    # ...  and load initial data pages
-    globaldata.load_datasets(recalc=0)
-else:
-    globaldata = None
-
-c4gldata = {}
-for key in args.experiments.strip(' ').split(' '):
-    
-    c4gldata[key] = c4gl_interface_soundings( \
-                      args.path_experiments+'/'+key+'/',\
-                      args.path_forcing+'/',\
-                      globaldata,\
-                      refetch_records=False
-                    )
-
-# the lines below activate TaylorPlots but it is disabled for now
-fig = plt.figure(figsize=(10,7))   #width,height
-i = 1                                                                           
-axes = {}         
-axes_taylor = {}         
-
-colors = ['r','g','b','m']
-symbols = ['*','x','+']
-dias = {}
-
-for varkey in ['h','theta','q']:                                                    
-    axes[varkey] = fig.add_subplot(2,3,i)                                       
-    #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
-
-    #print(obs.std())
-    dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-    if i == 0:
-        dias[varkey]._ax.axis["left"].label.set_text(\
-            "Standard deviation (model) / Standard deviation (observations)")
-        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
-    #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-    # Q95 = obs.quantile(0.95)
-    # Q95 = obs.quantile(0.90)
-    # Add RMS contours, and label them
-    contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-    dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
-    #dia._ax.set_title(season.capitalize())
-
-    dias[varkey].add_grid()
-
-
-    #dia.ax.plot(x99,y99,color='k')
-
-    
-    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-        # clearsky = (cc < 0.05)
-        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-        x, y = obs.values,mod.values
-        print(key,len(obs.values))
-
-        STD_OBS = obs.std()
-        #scores
-        PR = pearsonr(mod,obs)[0]
-        RMSE = rmse(obs,mod)                                               
-        BIAS = np.mean(mod) - np.mean(obs)
-        STD = mod.std()
-        
-        # fit = np.polyfit(x,y,deg=1)
-        # axes[varkey].plot(x, fit[0] * x + fit[1],\
-        #                   color=colors[ikey],alpha=0.8,lw=2,\
-        #                   label=key+", "+\
-        #                               'R = '+str(round(PR,3))+', '+\
-        #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-        #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-        # axes[varkey].legend(fontsize=5)
-        
-        # print(STD)
-        # print(PR)
-        dias[varkey].add_sample(STD/STD_OBS, PR,
-                       marker='o', ms=5, ls='',
-                       #mfc='k', mec='k', # B&W
-                       mfc=colors[ikey], mec=colors[ikey], # Colors
-                       label=key)
-
-    # put ticker position, see
-    # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
-    # dia.ax.axis['bottom'].
-    # dia.ax.axis['left'].
-    # dia.ax.axis['left'].
-
-    i += 1
-
-i = 0
-for varkey in ['h','theta','q']:                                                    
-    ikey = 0
-    key = list(args.experiments.strip().split(' '))[ikey]
-    cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-    clearsky = (cc < 0.05)
-
-    mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-    obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-
-
-    nbins=40       
-    x, y = obs.values,mod.values
-    
-    xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
-    zi = np.zeros_like(xi)*np.nan       
-    for ibin in range(nbins):
-        xmin = x.min() + ibin * (x.max() - x.min())/nbins
-        xmax = xmin + (x.max() - x.min())/nbins
-        in_bin = ((x >= xmin) & (x < xmax))
-        ybin = y[in_bin]
-        xbin = x[in_bin]
-        if len(ybin) > 20:
-            k = kde.gaussian_kde((ybin))
-            zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
-    zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
-    zi_int = zi.cumsum(axis=1) 
-                 #  label=key+", "+\
-                 #                    'R = '+str(round(PR[0],3))+', '+\
-                 #                    'RMSE = '+str(round(RMSE,5))+', '+\
-                 #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-    axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
-            colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
-    axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
-            colors=['darkred'],alpha=0.5,)
-
-
-    latex = {}
-    latex['dthetadt'] =  r'$d \theta / dt $'
-    latex['dqdt'] =      r'$d q / dt $'
-    latex['dhdt'] =      r'$d h / dt $'
-
-    axes[varkey].set_xlabel('observations')     
-    axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-
-    PR = pearsonr(mod,obs)[0]
-    RMSE = rmse(obs,mod)                                               
-    BIAS = np.mean(mod) - np.mean(obs)
-    STD = mod.std()
-
-    axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
-                                  'R = '+str(round(PR,3))+', '+\
-                                  'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-                                  'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
-                         s=0.1,alpha=0.14,color='k')
-    axes[varkey].legend(fontsize=5)
-                   
-    axes[varkey].set_xlabel('observations')     
-    if i==0:                                    
-        axes[varkey].set_ylabel('model')                                            
-    abline(1,0,axis=axes[varkey])
-    i +=1
-
-
-
-# legend for different forcing simulations (colors)
-ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-leg = []
-for ikey,key in enumerate(args.experiments.strip().split(' ')):
-    leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
-    leg.append(leg1)
-ax.axis('off')
-#leg1 =
-ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
-
-
-# # legend for different stations (symbols)
-# ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-# leg = []
-# isymbol = 0
-# for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-#     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
-#     leg.append(leg1)
-#     isymbol += 1
-# 
-# # symbol for all stations
-# leg1, = ax.plot([],'ko',markersize=10)
-# leg.append(leg1)
-
-
-# ax.axis('off')
-# ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
-
-
-fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
-
-
-#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
-# figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
-# fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
-
-if args.figure_filename is not None:
-    fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
-fig.show()  
-
-
-if bool(args.show_control_parameters):
-    import seaborn as sns
-    sns.set()
-    fig = pl.figure(figsize=(12,8))
-    i = 1
-    axes = {}
-    data_all = pd.DataFrame()
-    
-    
-    
-    # #for varkey in ['theta','q']:     
-    # EF =\
-    #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
-    #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
-    # EF[EF<0] = np.nan
-    # EF[EF>1] = np.nan
-    
-    # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
-    
-    ikey = 0
-    key = list(args.experiments.strip().split(' '))[ikey]
-    for varkey in ['h','theta','q']:
-        for input_key in ['wg','cc']:
-            data_all = pd.DataFrame()
-            data = pd.DataFrame()
-            data[varkey] = ""
-            data[varkey] = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-            data["source"] = "soundings"
-            data_all = pd.concat([data_all,data])
-                
-            data = pd.DataFrame()
-            
-            
-            data[varkey] = ""
-            data[varkey] = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-            data["source"] = "model"
-            data_all = pd.concat([data_all,data])
-            data_input = pd.concat([c4gldata[key].frames['stats']['records_all_stations_ini'],
-                                   c4gldata[key].frames['stats']['records_all_stations_ini']],axis=0)
-            input_key_full = input_key + "["+units[input_key]+"]"
-            data_all[input_key_full] =  pd.cut(x=data_input[input_key].values,bins=10)
-            
-            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
-            data_all = data_all.rename(columns={varkey:varkey_full})
-            
-            qvalmax = data_all[varkey_full].quantile(0.999)
-            qvalmin = data_all[varkey_full].quantile(0.001)
-            data_all = data_all[(data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)]
-            
-            ax = fig.add_subplot(3,2,i)
-            sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
-            ax.grid()
-            plt.xticks(rotation=45,ha='right')
-            i +=1
-            plt.legend(loc='lower right')
-    fig.tight_layout()
-    if args.figure_filename_2 is not None:
-        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:", args.figure_filename)
-    fig.show()
-
-
-
-
-
-
-
-
-
-
diff --git a/class4gl/interface/interface_eval_stations.py b/class4gl/interface/interface_eval_stations.py
deleted file mode 100644
index 49deebc..0000000
--- a/class4gl/interface/interface_eval_stations.py
+++ /dev/null
@@ -1,290 +0,0 @@
-import numpy as np
-
-import pandas as pd
-import sys
-
-import matplotlib
-matplotlib.use('TkAgg')
-
-import argparse
-parser = argparse.ArgumentParser()
-parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--experiments')
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load_globaldata',default=True)
-parser.add_argument('--figure_filename',default=None)
-args = parser.parse_args()
-
-print('Adding python library:',args.c4gl_path_lib)
-sys.path.insert(0, args.c4gl_path_lib)
-from interface_multi import c4gl_interface_soundings,get_record_yaml
-from class4gl import class4gl_input, data_global,class4gl,units
-#from sklearn.metrics import mean_squared_error
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-#import seaborn.apionly as sns
-import pylab as pl
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.stats import kde
-from scipy.stats import pearsonr                                                
-from taylorDiagram import TaylorDiagram
-from matplotlib import ticker
-# import importlib
-# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
-
-
-
-
-
-latex = {}
-latex['dthetadt'] =  r'$d \theta / dt $'
-latex['dqdt'] =      r'$d q / dt $'
-latex['dhdt'] =      r'$d h / dt $'
-
-def abline(slope, intercept,axis):
-    """Plot a line from slope and intercept"""
-    #axis = plt.gca()
-    x_vals = np.array(axis.get_xlim())
-    y_vals = intercept + slope * x_vals
-    axis.plot(x_vals, y_vals, 'k--')
-
-def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
-    """ calculated root mean squared error 
-        
-    
-        INPUT:
-            y_actual: reference dataset
-            y_predicted: predicting dataset
-            z_actual: coordinate values of reference dataset
-            z_predicted: coordinate values of the predicting dataset
-            
-            filternan_actual: throw away reference values that have nans
-    """
-    
-    y_actual_temp = np.array(y_actual)
-    y_predicted_temp = np.array(y_predicted)
-    
-    if z_actual is not None:
-        z_actual_temp = np.array(z_actual)
-    else: 
-        z_actual_temp = None
-        
-    
-    if filternan_actual:
-        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
-        if z_actual_temp is not None:
-            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
-    
-    if ((z_actual_temp is not None) or (z_predicted is not None)):    
-        if (z_actual_temp is None) or (z_predicted is None):
-            raise ValueError('Input z_actual and z_predicted need \
-                              to be specified simultaneously.')
-        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
-    
-    else:
-        # this catches the situation that y_predicted is a single value (eg., 
-        # which is the case for evaluating eg., mixed-layer estimates)
-        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
-        
-    rmse_temp = (y_actual_temp - y_predicted_temp)
-    rmse_temp = np.mean(rmse_temp*rmse_temp)
-    return np.sqrt(rmse_temp)
-
-
-
-
-
-# EXPS  =\
-# {
-# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-# }
-
-if args.load_globaldata:
-    # iniitialize global data
-    globaldata = data_global()
-    # ...  and load initial data pages
-    globaldata.load_datasets(recalc=0)
-else:
-    globaldata = None
-
-c4gldata = {}
-for key in args.experiments.strip(' ').split(' '):
-    
-    c4gldata[key] = c4gl_interface_soundings( \
-                      args.path_experiments+'/'+key+'/',\
-                      args.path_forcing+'/',\
-                      globaldata,\
-                      refetch_records=False
-                    )
-
-fig = plt.figure(figsize=(10,7))   #width,height
-i = 1                                                                           
-axes = {}         
-axes_taylor = {}         
-
-colors = ['r','g','b','m']
-symbols = ['*','x','+']
-dias = {}
-
-for varkey in ['h','theta','q']:                                                    
-    axes[varkey] = fig.add_subplot(2,3,i)                                       
-    #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
-
-    #print(obs.std())
-    obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-    STD_OBS = obs.std()
-    dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-    if i == 2:
-        dias[varkey]._ax.axis["left"].label.set_text(\
-            "Standard deviation (model) / Standard deviation (observations)")
-        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
-    #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-    # Q95 = obs.quantile(0.95)
-    # Q95 = obs.quantile(0.90)
-    # Add RMS contours, and label them
-    contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-    dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
-    #dia._ax.set_title(season.capitalize())
-
-    dias[varkey].add_grid()
-
-
-    #dia.ax.plot(x99,y99,color='k')
-
-    
-    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-        x, y = obs.values,mod.values
-        print(key,len(obs.values))
-
-        #scores
-        PR = pearsonr(mod,obs)[0]
-        RMSE = rmse(obs,mod)                                               
-        BIAS = np.mean(mod) - np.mean(obs)
-        STD = mod.std()
-        
-        fit = np.polyfit(x,y,deg=1)
-        axes[varkey].plot(x, fit[0] * x + fit[1],\
-                          color=colors[ikey],alpha=0.8,lw=2,\
-                          label=key+", "+\
-                                      'R = '+str(round(PR,3))+', '+\
-                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-        axes[varkey].legend(fontsize=5)
-        
-        # print(STD)
-        # print(PR)
-        dias[varkey].add_sample(STD/STD_OBS, PR,
-                       marker='o', ms=5, ls='',
-                       #mfc='k', mec='k', # B&W
-                       mfc=colors[ikey], mec=colors[ikey], # Colors
-                       label=key)
-
-    # put ticker position, see
-    # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
-    # dia.ax.axis['bottom'].
-    # dia.ax.axis['left'].
-    # dia.ax.axis['left'].
-
-    i += 1
-
-i = 0
-for varkey in ['h','theta','q']:                                                    
-    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-        isymbol = 0
-        for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-            indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
-            station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
-            station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
-
-            axes[varkey].scatter(station_obs,station_mod,marker=symbols[isymbol],color=colors[ikey])
-                     #  label=key+", "+\
-                     #                    'R = '+str(round(PR[0],3))+', '+\
-                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
-                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-
-
-
-        # # pl.scatter(obs,mod,label=key+", "+\
-        # #                              'R = '+str(round(PR[0],3))+', '+\
-        # #                              'RMSE = '+str(round(RMSE,5))+', '+\
-        # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-            
-            dias[varkey].add_sample(station_mod.std()/station_obs.std(),
-                           pearsonr(station_mod,station_obs)[0],
-                           marker=symbols[isymbol], ms=5, ls='',
-                           #mfc='k', mec='k', # B&W
-                           mfc=colors[ikey], mec=colors[ikey], # Colors
-                           label=key)
-            isymbol += 1
-
-
-        axes[varkey].set_xlabel('observations')     
-        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-    if i==0:                                    
-        axes[varkey].set_ylabel('model')                                            
-    abline(1,0,axis=axes[varkey])
-    i +=1
-
-
-
-# legend for different forcing simulations (colors)
-ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-leg = []
-for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-    leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
-    leg.append(leg1)
-ax.axis('off')
-#leg1 =
-ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
-
-
-# legend for different stations (symbols)
-ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-leg = []
-isymbol = 0
-for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-    leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
-    leg.append(leg1)
-    isymbol += 1
-
-# symbol for all stations
-leg1, = ax.plot([],'ko',markersize=10)
-leg.append(leg1)
-
-
-ax.axis('off')
-ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
-
-
-fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
-
-
-#pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
-#figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/iops_eval_report.png'
-
-if args.figure_filename is not None:
-    fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
-fig.show()  
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index f6d718f..9e6b82a 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -12,7 +12,9 @@
 parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--experiments')
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--load_globaldata',default=False)
+parser.add_argument('--load_globaldata',default=False) # load the data needed for the interface
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--figure_filename',default=None)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -36,6 +38,12 @@
 
 
 
+
+latex = {}
+latex['dthetadt'] =  r'$d \theta / dt $'
+latex['dqdt'] =      r'$d q / dt $'
+latex['dhdt'] =      r'$d h / dt $'
+
 def abline(slope, intercept,axis):
     """Plot a line from slope and intercept"""
     #axis = plt.gca()
@@ -100,7 +108,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 # # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 # }
 
-if bool(args.load_globaldata):
+if args.load_globaldata:
     # iniitialize global data
     globaldata = data_global()
     # ...  and load initial data pages
@@ -118,184 +126,159 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-# # the lines below activate TaylorPlots but it is disabled for now
-# fig = plt.figure(figsize=(10,7))   #width,height
-# i = 1                                                                           
-# axes = {}         
-# axes_taylor = {}         
-# 
-# colors = ['r','g','b','m']
-# symbols = ['*','x','+']
-# dias = {}
-# 
-# for varkey in ['h','theta','q']:                                                    
-#     axes[varkey] = fig.add_subplot(2,3,i)                                       
-#     #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
-# 
-#     #print(obs.std())
-#     dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-#     if i == 0:
-#         dias[varkey]._ax.axis["left"].label.set_text(\
-#             "Standard deviation (model) / Standard deviation (observations)")
-#         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-#         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
-#     #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-#     # Q95 = obs.quantile(0.95)
-#     # Q95 = obs.quantile(0.90)
-#     # Add RMS contours, and label them
-#     contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-#     dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
-#     #dia._ax.set_title(season.capitalize())
-# 
-#     dias[varkey].add_grid()
-# 
-# 
-#     #dia.ax.plot(x99,y99,color='k')
-# 
-#     
-#     for ikey,key in enumerate(args.experiments.split(';')):
-#         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-#         # clearsky = (cc < 0.05)
-#         # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-#         # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-#         mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-#         obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-#         x, y = obs.values,mod.values
-#         print(key,len(obs.values))
-# 
-#         STD_OBS = obs.std()
-#         #scores
-#         PR = pearsonr(mod,obs)[0]
-#         RMSE = rmse(obs,mod)                                               
-#         BIAS = np.mean(mod) - np.mean(obs)
-#         STD = mod.std()
-#         
-#         # fit = np.polyfit(x,y,deg=1)
-#         # axes[varkey].plot(x, fit[0] * x + fit[1],\
-#         #                   color=colors[ikey],alpha=0.8,lw=2,\
-#         #                   label=key+", "+\
-#         #                               'R = '+str(round(PR,3))+', '+\
-#         #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-#         #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-#         # axes[varkey].legend(fontsize=5)
-#         
-#         # print(STD)
-#         # print(PR)
-#         dias[varkey].add_sample(STD/STD_OBS, PR,
-#                        marker='o', ms=5, ls='',
-#                        #mfc='k', mec='k', # B&W
-#                        mfc=colors[ikey], mec=colors[ikey], # Colors
-#                        label=key)
-# 
-#     # put ticker position, see
-#     # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
-#     # dia.ax.axis['bottom'].
-#     # dia.ax.axis['left'].
-#     # dia.ax.axis['left'].
-# 
-#     i += 1
-# 
-# i = 0
-# for varkey in ['h','theta','q']:                                                    
-#     ikey = 0
-#     key = list(args.experiments.split(';'))[ikey]
-#     cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-#     clearsky = (cc < 0.05)
-# 
-#     mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-#     obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-# 
-# 
-#     nbins=40       
-#     x, y = obs.values,mod.values
-#     
-#     xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
-#     zi = np.zeros_like(xi)*np.nan       
-#     for ibin in range(nbins):
-#         xmin = x.min() + ibin * (x.max() - x.min())/nbins
-#         xmax = xmin + (x.max() - x.min())/nbins
-#         in_bin = ((x >= xmin) & (x < xmax))
-#         ybin = y[in_bin]
-#         xbin = x[in_bin]
-#         if len(ybin) > 20:
-#             k = kde.gaussian_kde((ybin))
-#             zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
-#     zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
-#     zi_int = zi.cumsum(axis=1) 
-#                  #  label=key+", "+\
-#                  #                    'R = '+str(round(PR[0],3))+', '+\
-#                  #                    'RMSE = '+str(round(RMSE,5))+', '+\
-#                  #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-#     axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
-#             colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
-#     axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
-#             colors=['darkred'],alpha=0.5,)
-# 
-# 
-#     latex = {}
-#     latex['dthetadt'] =  r'$d \theta / dt $'
-#     latex['dqdt'] =      r'$d q / dt $'
-#     latex['dhdt'] =      r'$d h / dt $'
-# 
-#     axes[varkey].set_xlabel('observations')     
-#     axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-# 
-#     PR = pearsonr(mod,obs)[0]
-#     RMSE = rmse(obs,mod)                                               
-#     BIAS = np.mean(mod) - np.mean(obs)
-#     STD = mod.std()
-# 
-#     axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
-#                                   'R = '+str(round(PR,3))+', '+\
-#                                   'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-#                                   'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
-#                          s=0.1,alpha=0.14,color='k')
-#     axes[varkey].legend(fontsize=5)
-#                    
-#     axes[varkey].set_xlabel('observations')     
-#     if i==0:                                    
-#         axes[varkey].set_ylabel('model')                                            
-#     abline(1,0,axis=axes[varkey])
-#     i +=1
-# 
-# 
-# 
-# # legend for different forcing simulations (colors)
-# ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-# leg = []
-# for ikey,key in enumerate(args.experiments.split(';')):
-#     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
-#     leg.append(leg1)
-# ax.axis('off')
-# #leg1 =
-# ax.legend(leg,list(args.experiments.split(';')),loc=2,fontsize=10)
-# 
-# 
-# # # legend for different stations (symbols)
-# # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-# # leg = []
-# # isymbol = 0
-# # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-# #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
-# #     leg.append(leg1)
-# #     isymbol += 1
-# # 
-# # # symbol for all stations
-# # leg1, = ax.plot([],'ko',markersize=10)
-# # leg.append(leg1)
-# 
-# 
-# # ax.axis('off')
-# # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
-# 
-# 
-# fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
-# 
-# 
-# #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
-# # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
-# # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
-# fig.show()  
+if bool(args.make_figures):
+    fig = plt.figure(figsize=(10,7))   #width,height
+    i = 1                                                                           
+    axes = {}         
+    axes_taylor = {}         
+    
+    colors = ['r','g','b','m']
+    symbols = ['*','x','+']
+    dias = {}
+    
+    for varkey in ['h','theta','q']:                                                    
+        axes[varkey] = fig.add_subplot(2,3,i)                                       
+        #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+    
+        #print(obs.std())
+        obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        STD_OBS = obs.std()
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        if i == 2:
+            dias[varkey]._ax.axis["left"].label.set_text(\
+                "Standard deviation (model) / Standard deviation (observations)")
+            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # Q95 = obs.quantile(0.95)
+        # Q95 = obs.quantile(0.90)
+        # Add RMS contours, and label them
+        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        #dia._ax.set_title(season.capitalize())
+    
+        dias[varkey].add_grid()
+    
+    
+        #dia.ax.plot(x99,y99,color='k')
+    
+        
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            x, y = obs.values,mod.values
+            print(key,len(obs.values))
+    
+            #scores
+            PR = pearsonr(mod,obs)[0]
+            RMSE = rmse(obs,mod)                                               
+            BIAS = np.mean(mod) - np.mean(obs)
+            STD = mod.std()
+            
+            fit = np.polyfit(x,y,deg=1)
+            axes[varkey].plot(x, fit[0] * x + fit[1],\
+                              color=colors[ikey],alpha=0.8,lw=2,\
+                              label=key+", "+\
+                                          'R = '+str(round(PR,3))+', '+\
+                                          'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                          'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+            axes[varkey].legend(fontsize=5)
+            
+            # print(STD)
+            # print(PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,
+                           marker='o', ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=key)
+    
+        # put ticker position, see
+        # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+        # dia.ax.axis['bottom'].
+        # dia.ax.axis['left'].
+        # dia.ax.axis['left'].
+    
+        i += 1
+    
+    i = 0
+    for varkey in ['h','theta','q']:                                                    
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+            isymbol = 0
+            for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+                indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
+                station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
+                station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
+    
+                axes[varkey].scatter(station_obs,station_mod,marker=symbols[isymbol],color=colors[ikey])
+                         #  label=key+", "+\
+                         #                    'R = '+str(round(PR[0],3))+', '+\
+                         #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                         #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+    
+    
+    
+            # # pl.scatter(obs,mod,label=key+", "+\
+            # #                              'R = '+str(round(PR[0],3))+', '+\
+            # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+            # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+                
+                dias[varkey].add_sample(station_mod.std()/station_obs.std(),
+                               pearsonr(station_mod,station_obs)[0],
+                               marker=symbols[isymbol], ms=5, ls='',
+                               #mfc='k', mec='k', # B&W
+                               mfc=colors[ikey], mec=colors[ikey], # Colors
+                               label=key)
+                isymbol += 1
+    
+    
+            axes[varkey].set_xlabel('observations')     
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+        if i==0:                                    
+            axes[varkey].set_ylabel('model')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+    
+    
+    
+    # legend for different forcing simulations (colors)
+    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    leg = []
+    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
+        leg.append(leg1)
+    ax.axis('off')
+    #leg1 =
+    ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
+    
+    
+    # legend for different stations (symbols)
+    ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    leg = []
+    isymbol = 0
+    for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+        leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+        leg.append(leg1)
+        isymbol += 1
+    
+    # symbol for all stations
+    leg1, = ax.plot([],'ko',markersize=10)
+    leg.append(leg1)
+    
+    
+    ax.axis('off')
+    ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    
+    
+    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+    
+    
+    #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+    #figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/iops_eval_report.png'
+    
+    if args.figure_filename is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+    fig.show()  
+
 
 
 

From 3e7b724243c15e38d3a6eb4e516d0ce9dc7be678 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 09:27:25 +0200
Subject: [PATCH 055/129] 	modified:   class4gl/interface/interface.py

---
 class4gl/interface/interface.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 72b970e..4d851ce 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -122,7 +122,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-if args.show_figures:
+if args.make_figures:
     # the lines below activate TaylorPlots but it is disabled for now
     fig = plt.figure(figsize=(10,7))   #width,height
     i = 1                                                                           

From b4b76cce841d95a7f318bcab0c0ec990359d0eda Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 10:06:47 +0200
Subject: [PATCH 056/129] xies in figures

---
 class4gl/interface/interface.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 4d851ce..0685a46 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -231,10 +231,13 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                      #                    'R = '+str(round(PR[0],3))+', '+\
                      #                    'RMSE = '+str(round(RMSE,5))+', '+\
                      #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.5,0.86] ,
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.5,0.86] ,
                 colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
-        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.14,0.86] ,
+        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.84] ,
                 colors=['darkred'],alpha=0.5,)
+
+        axes[varkey].set_xlim((xi.min(),xi.max())
+        axes[varkey].set_ylim((yi.min(),yi.max())
     
     
         latex = {}
@@ -256,7 +259,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                       'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
                              s=0.1,alpha=0.14,color='k')
         axes[varkey].legend(fontsize=5)
-                       
+        
+
+
+
         axes[varkey].set_xlabel('observations')     
         if i==0:                                    
             axes[varkey].set_ylabel('model')                                            
@@ -363,7 +369,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 plt.legend(loc='lower right')
         fig.tight_layout()
         if args.figure_filename_2 is not None:
-            fig.savefig(args.figure_filename,dpi=200); print("Image file written to:", args.figure_filename)
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file
+                                                               written to:",
+                                                               args.figure_filename_2)
         fig.show()
 
 

From b4202d8a27d89376a7f603eaa869f8ec12664d41 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 10:09:56 +0200
Subject: [PATCH 057/129] figure fixes

---
 class4gl/interface/interface.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 0685a46..4ccd1e4 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -237,7 +237,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 colors=['darkred'],alpha=0.5,)
 
         axes[varkey].set_xlim((xi.min(),xi.max())
-        axes[varkey].set_ylim((yi.min(),yi.max())
+        axes[varkey].set_ylim((xi.min(),xi.max())
     
     
         latex = {}

From f74ec72ab00a7cc05f89fde44ae71301ae1d7492 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 10:10:50 +0200
Subject: [PATCH 058/129] figure fixes

---
 class4gl/interface/interface.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 4ccd1e4..0a8a009 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -236,8 +236,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.84] ,
                 colors=['darkred'],alpha=0.5,)
 
-        axes[varkey].set_xlim((xi.min(),xi.max())
-        axes[varkey].set_ylim((xi.min(),xi.max())
+        axes[varkey].set_xlim((xi.min(),xi.max()))
+        axes[varkey].set_ylim((xi.min(),xi.max()))
     
     
         latex = {}

From 91d362d451b4bb96fdb495168dfbb4971a62f61a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 10:11:50 +0200
Subject: [PATCH 059/129] figure fixes

---
 class4gl/interface/interface.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 0a8a009..3d76c13 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -369,9 +369,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 plt.legend(loc='lower right')
         fig.tight_layout()
         if args.figure_filename_2 is not None:
-            fig.savefig(args.figure_filename_2,dpi=200); print("Image file
-                                                               written to:",
-                                                               args.figure_filename_2)
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
         fig.show()
 
 

From b24bc772a40587b75d74b43ef9b1cf24d688d28d Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 10:22:16 +0200
Subject: [PATCH 060/129] figure fixes

---
 class4gl/interface/interface.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 3d76c13..e481bbe 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -231,13 +231,13 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                      #                    'R = '+str(round(PR[0],3))+', '+\
                      #                    'RMSE = '+str(round(RMSE,5))+', '+\
                      #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.5,0.86] ,
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.5,0.84] ,
                 colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
         axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.84] ,
                 colors=['darkred'],alpha=0.5,)
-
-        axes[varkey].set_xlim((xi.min(),xi.max()))
-        axes[varkey].set_ylim((xi.min(),xi.max()))
+        nanxi = xi[zi != np.nan]
+        axes[varkey].set_xlim((nanxi.min(),nanxi.max()))
+        axes[varkey].set_ylim((nanxi.min(),nanxi.max()))
     
     
         latex = {}

From 70e9d665d1eafd87bf127ddc2544eb07601b0bbc Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 11:10:33 +0200
Subject: [PATCH 061/129] modified:   .gitignore

---
 .gitignore | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index b65667c..84f7975 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,20 +1,17 @@
 *.o, 
 *.pyc
 *.class 
-class4gl/__pycache__/
 class4gl/__pycache__/*
 *.log
 .*
-build/
-dist/
-trash/
+build/*
+dist/*
+trash/*
 */__pychache__/
 *.py[cod]
 *$py.class
 .Python
-build/
 develop-eggs/
-dist/
 downloads/
 eggs/
 .eggs/

From 95466045a2ef98e97107a38074b4810393dae7b7 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 30 Aug 2018 17:19:59 +0200
Subject: [PATCH 062/129] figure fixes

---
 class4gl/interface/interface.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index e481bbe..5f55626 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -238,6 +238,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         nanxi = xi[zi != np.nan]
         axes[varkey].set_xlim((nanxi.min(),nanxi.max()))
         axes[varkey].set_ylim((nanxi.min(),nanxi.max()))
+        print(varkey,(nanxi.min(),nanxi.max()))
     
     
         latex = {}

From 51fbba95c8606b6c5d0cea93d42fc738d6289734 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 1 Sep 2018 02:22:16 +0200
Subject: [PATCH 063/129] nicely-organized boxplots

---
 class4gl/interface/interface.py               | 209 +++++++++++++++---
 class4gl/simulations/c4gl_sim.o643275-5       |  28 ---
 ...tions_wilt.py => simulations_wwilt_wfc.py} |  37 ++--
 3 files changed, 202 insertions(+), 72 deletions(-)
 delete mode 100644 class4gl/simulations/c4gl_sim.o643275-5
 rename class4gl/simulations/{simulations_wilt.py => simulations_wwilt_wfc.py} (92%)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 5f55626..cc0febf 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -1,3 +1,5 @@
+'''
+
 import numpy as np
 
 import pandas as pd
@@ -122,7 +124,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
+'''
 if args.make_figures:
+    '''
     # the lines below activate TaylorPlots but it is disabled for now
     fig = plt.figure(figsize=(10,7))   #width,height
     i = 1                                                                           
@@ -311,15 +315,39 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     if args.figure_filename is not None:
         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
     fig.show()  
-    
-    
+   ''' 
     if bool(args.show_control_parameters):
+
         import seaborn as sns
-        sns.set()
+
+        pkmn_type_colors = [
+                                            '#A0A0A0',  # Poison
+                                            '#78C850',  # Grass
+                                            '#F08030',  # Fire
+                                            '#6890F0',  # Water
+                                            '#F08030',  # Fire
+                                            '#C03028',  # Fighting
+                                            '#F85888',  # Psychic
+                                            '#A8B820',  # Bug
+                                            '#A8A878',  # Normal
+                                            '#F8D030',  # Electric
+                                            '#E0C068',  # Ground
+                                            '#EE99AC',  # Fairy
+                                            '#B8A038',  # Rock
+                                            '#705898',  # Ghost
+                                            '#98D8D8',  # Ice
+                                            '#7038F8',  # Dragon
+                                           ]
+
+
+
+        sns.set_style('whitegrid')
+        #sns.set()
         fig = pl.figure(figsize=(12,8))
         i = 1
         axes = {}
         data_all = pd.DataFrame()
+        data_input = pd.DataFrame()
         
         
         
@@ -334,41 +362,166 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         
         ikey = 0
         key = list(args.experiments.strip().split(' '))[ikey]
+        data_all = pd.DataFrame()
+
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        tempdatamodstats["source"] = "soundings"
+        tempdatamodstats["source_index"] = "soundings"
+
+        ini_ref = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+        tempdataini_this = pd.DataFrame(ini_ref.copy())
+
+        tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+        tempdatamodstats['STNID']= tempdataini_this.STNID
+        tempdatamodstats['source']= "soundings"
+        tempdatamodstats['source_index']= "soundings"
+        tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+        print('hello')
+
+        tempdataini = pd.DataFrame(ini_ref)
+        tempdataini["source"] = "soundings"
+        tempdataini["source_index"] = "soundings"
+        tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+        print('hello2')
+
+
+        data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+        data_input = pd.concat([data_input,tempdataini],axis=0)
+        print(data_input.shape)
+        print(data_all.shape)
+
+            
+        for key in list(args.experiments.strip().split(' ')):
+
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+            tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+            tempdatamodstats['STNID']= tempdataini_this.STNID
+            tempdatamodstats['source']= key
+            tempdatamodstats['source_index']= key
+            tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+            print('hello')
+
+
+            tempdataini = pd.DataFrame(ini_ref.copy())
+            tempdataini["source"] = key 
+            tempdataini["source_index"] = key
+            tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+    
+
+            print('hello2')
+            index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
+            print('hello3')
+
+            tempdataini = tempdataini.loc[index_intersect]
+            print('hello4')
+            tempdatamodstats = tempdatamodstats.loc[index_intersect]
+            print('hello5')
+
+
+            # data[varkey] = tempdatamodstats['d'+varkey+'dt']
+            data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+            data_input = pd.concat([data_input, tempdataini],axis=0)
+            print(data_input.shape)
+            print(data_all.shape)
+
+        data_input.cc = data_input.cc.clip(0.,+np.inf)
+
+        for varkey in ['h','theta','q']:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+            data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            print(data_input.shape)
+            print(data_all.shape)
+        print('hello6')
+        print(data_all.columns)
+        print('hello7')
         for varkey in ['h','theta','q']:
             for input_key in ['wg','cc']:
-                data_all = pd.DataFrame()
-                data = pd.DataFrame()
-                data[varkey] = ""
-                data[varkey] = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-                data["source"] = "soundings"
-                data_all = pd.concat([data_all,data])
-                    
-                data = pd.DataFrame()
-                
-                
-                data[varkey] = ""
-                data[varkey] = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-                data["source"] = "model"
-                data_all = pd.concat([data_all,data])
-                data_input = pd.concat([c4gldata[key].frames['stats']['records_all_stations_ini'],
-                                       c4gldata[key].frames['stats']['records_all_stations_ini']],axis=0)
-                input_key_full = input_key + "["+units[input_key]+"]"
-                data_all[input_key_full] =  pd.cut(x=data_input[input_key].values,bins=10)
-                
                 varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
-                data_all = data_all.rename(columns={varkey:varkey_full})
+
+                print('hello8')
+                print(data_input.shape)
+                print(data_all.shape)
+                input_key_full = input_key + "["+units[input_key]+"]"
+                data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2)
+                data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2,)
+                print('hello9')
+                print(data_input.shape)
+                print(data_all.shape)
                 
                 qvalmax = data_all[varkey_full].quantile(0.999)
                 qvalmin = data_all[varkey_full].quantile(0.001)
-                data_all = data_all[(data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)]
+                select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
+                print('hello11')
+                data_all = data_all[select_data]
+                print('hello12')
+                data_input = data_input[select_data.values]
+                print('hello13')
+                print(data_input.shape)
+                print(data_all.shape)
+                print('hello10')
                 
+                sns.set(style="ticks", palette="pastel")
                 ax = fig.add_subplot(3,2,i)
-                sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
-                ax.grid()
-                plt.xticks(rotation=45,ha='right')
+                #sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+                
+                #ax.set_title(input_key_full)
+                sb = sns.boxplot(x=input_key_full, y=varkey_full, hue="source",
+                                 palette=pkmn_type_colors,
+                                # palette=["m", "g",'r','b'],
+                                 linewidth=1.2, data=data_all,sym='')
+                if i ==1:
+                     plt.legend(loc='upper right',fontsize=7.)
+                else:
+                     ax.get_legend().set_visible(False)
+                #     plt.legend('off')
+                if i >= 5:
+                    ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=45.,ha='right')
+                else:
+                    ax.set_xticklabels([])
+                    ax.set_xlabel('')
+
+                if np.mod(i,2) == 0:
+                    ax.set_yticklabels([])
+                    ax.set_ylabel('')
+
+                for j,artist in enumerate(ax.artists):
+                    if np.mod(j,4) !=0:
+                        # Set the linecolor on the artist to the facecolor, and set the facecolor to None
+                        print(j,artist)
+                        col = artist.get_facecolor()
+                        print(j,artist)
+                        artist.set_edgecolor(col)
+                        print(j,artist)
+                        artist.set_facecolor('None')
+                
+                        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
+                        # Loop over them here, and use the same colour as above
+                        
+                        for k in range(j*5,j*5+5):
+                            line = ax.lines[k]
+                            line.set_color(col)
+                            line.set_mfc(col)
+                            line.set_mec(col)
+                
+                # Also fix the legend
+                j = 0
+                for legpatch in ax.get_legend().get_patches():
+                    if j > 0:
+
+                        col = legpatch.get_facecolor()
+                        legpatch.set_edgecolor(col)
+                        legpatch.set_facecolor('None')
+                    j +=1
+
+
+
+
+                #ax.grid()
+                #sns.despine(offset=10, trim=True)
                 i +=1
-                plt.legend(loc='lower right')
         fig.tight_layout()
+        fig.subplots_adjust( bottom=0.18,left=0.09,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
         if args.figure_filename_2 is not None:
             fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
         fig.show()
diff --git a/class4gl/simulations/c4gl_sim.o643275-5 b/class4gl/simulations/c4gl_sim.o643275-5
deleted file mode 100644
index df8103f..0000000
--- a/class4gl/simulations/c4gl_sim.o643275-5
+++ /dev/null
@@ -1,28 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=SM5 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL --split_by=50 --station_id=74560 --subset_forcing=morning
-getting stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (5)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 5
-Fetching initial/forcing records
-Fetching afternoon records for determining the simulation runtimes
-hello
-396
-396
-aligning morning and afternoon records
-Traceback (most recent call last):
-  File "/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py", line 184, in 
-    exp = EXP_DEFS[expname]
-KeyError: 'SM5'
diff --git a/class4gl/simulations/simulations_wilt.py b/class4gl/simulations/simulations_wwilt_wfc.py
similarity index 92%
rename from class4gl/simulations/simulations_wilt.py
rename to class4gl/simulations/simulations_wwilt_wfc.py
index 8da29e4..6f0afd3 100644
--- a/class4gl/simulations/simulations_wilt.py
+++ b/class4gl/simulations/simulations_wwilt_wfc.py
@@ -57,11 +57,12 @@
 
 EXP_DEFS  =\
 {
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'WILT':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_WILT':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_FC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
 
@@ -69,7 +70,6 @@
 # SET = args.dataset
 
 
-
 print("getting stations")
 # these are all the stations that are found in the input dataset
 all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
@@ -182,7 +182,6 @@
     records_afternoon.index = records_morning.index
 
 experiments = args.experiments.strip(' ').split(' ')
-
 for expname in experiments:
     exp = EXP_DEFS[expname]
     path_exp = args.path_experiments+'/'+expname+'/'
@@ -235,24 +234,30 @@
                                              c4gli_morning.pars.datetime_daylight).total_seconds())
                     else:
                         runtime = int(args.runtime)
+
             
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
-                    
-                    if expname == 'WILT':
-                       c4gli_morning.update(source=expname, \
-                                            pars={'wg':c4gli_morning.pars.wwilt,\
-                                                  'w2':c4gli_morning.pars.wwilt},
-                                           )
+                    if expname == 'GLOBAL_NOAC_WILT':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'wg':c4gli_morning.pars.wwilt,\
+                                              'w2':c4gli_morning.pars.wwilt}\
+                                            )
+                    if expname == 'GLOBAL_NOAC_FC':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'wg':c4gli_morning.pars.wfc,\
+                                              'w2':c4gli_morning.pars.wfc}\
+                                            )
+
                     c4gl = class4gl(c4gli_morning)
 
                     if args.error_handling == 'dump_always':
                         try:
                             c4gl.run()
-                            print('run succesfull')
+                            print('run successful')
                         except:
-                            print('run not succesfull')
+                            print('run not successful')
                         onerun = True
 
                         c4gli_morning.dump(file_ini)
@@ -319,7 +324,7 @@
     #     with \
     #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
     #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
     #         for (STNID,index),record_ini in records_iterator(records_ini):
     #             c4gli_ini = get_record_yaml(file_station_ini, 
     #                                         record_ini.index_start, 

From ded0a79fd562044b3ebf6de93ee0f1adee39c3a6 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 1 Sep 2018 02:30:26 +0200
Subject: [PATCH 064/129] nicely-organized boxplots

---
 class4gl/interface/interface.py | 74 ++++++++++++++++-----------------
 1 file changed, 36 insertions(+), 38 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index cc0febf..af989c0 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -1,4 +1,3 @@
-'''
 
 import numpy as np
 
@@ -124,9 +123,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-'''
 if args.make_figures:
-    '''
     # the lines below activate TaylorPlots but it is disabled for now
     fig = plt.figure(figsize=(10,7))   #width,height
     i = 1                                                                           
@@ -315,7 +312,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     if args.figure_filename is not None:
         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
     fig.show()  
-   ''' 
+
     if bool(args.show_control_parameters):
 
         import seaborn as sns
@@ -376,19 +373,19 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         tempdatamodstats['source']= "soundings"
         tempdatamodstats['source_index']= "soundings"
         tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
-        print('hello')
+        #print('hello')
 
         tempdataini = pd.DataFrame(ini_ref)
         tempdataini["source"] = "soundings"
         tempdataini["source_index"] = "soundings"
         tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
-        print('hello2')
+        #print('hello2')
 
 
         data_all = pd.concat([data_all,tempdatamodstats],axis=0)
         data_input = pd.concat([data_input,tempdataini],axis=0)
-        print(data_input.shape)
-        print(data_all.shape)
+        #print(data_input.shape)
+        #print(data_all.shape)
 
             
         for key in list(args.experiments.strip().split(' ')):
@@ -400,7 +397,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             tempdatamodstats['source']= key
             tempdatamodstats['source_index']= key
             tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
-            print('hello')
+            #print('hello')
 
 
             tempdataini = pd.DataFrame(ini_ref.copy())
@@ -409,57 +406,58 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
     
 
-            print('hello2')
+            #print('hello2')
             index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
-            print('hello3')
+            #print('hello3')
 
             tempdataini = tempdataini.loc[index_intersect]
-            print('hello4')
+            #print('hello4')
             tempdatamodstats = tempdatamodstats.loc[index_intersect]
-            print('hello5')
+            #print('hello5')
 
 
             # data[varkey] = tempdatamodstats['d'+varkey+'dt']
             data_all = pd.concat([data_all,tempdatamodstats],axis=0)
             data_input = pd.concat([data_input, tempdataini],axis=0)
-            print(data_input.shape)
-            print(data_all.shape)
+            #print(data_input.shape)
+            #print(data_all.shape)
 
         data_input.cc = data_input.cc.clip(0.,+np.inf)
 
         for varkey in ['h','theta','q']:
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
-            print(data_input.shape)
-            print(data_all.shape)
-        print('hello6')
-        print(data_all.columns)
-        print('hello7')
+            #print(data_input.shape)
+            #print(data_all.shape)
+        #print('hello6')
+        #print(data_all.columns)
+        #print('hello7')
         for varkey in ['h','theta','q']:
-            for input_key in ['wg','cc']:
+            input_keys =['wg','cc']
+            for input_key in input_keys:
                 varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
 
-                print('hello8')
-                print(data_input.shape)
-                print(data_all.shape)
+                #print('hello8')
+                #print(data_input.shape)
+                #print(data_all.shape)
                 input_key_full = input_key + "["+units[input_key]+"]"
                 data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2)
                 data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2,)
-                print('hello9')
-                print(data_input.shape)
-                print(data_all.shape)
+                #print('hello9')
+                #print(data_input.shape)
+                #print(data_all.shape)
                 
                 qvalmax = data_all[varkey_full].quantile(0.999)
                 qvalmin = data_all[varkey_full].quantile(0.001)
                 select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
-                print('hello11')
+                #print('hello11')
                 data_all = data_all[select_data]
-                print('hello12')
+                #print('hello12')
                 data_input = data_input[select_data.values]
-                print('hello13')
-                print(data_input.shape)
-                print(data_all.shape)
-                print('hello10')
+                #print('hello13')
+                #print(data_input.shape)
+                #print(data_all.shape)
+                #print('hello10')
                 
                 sns.set(style="ticks", palette="pastel")
                 ax = fig.add_subplot(3,2,i)
@@ -481,18 +479,18 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                     ax.set_xticklabels([])
                     ax.set_xlabel('')
 
-                if np.mod(i,2) == 0:
+                if np.mod(i,len(input_keys)) == 0:
                     ax.set_yticklabels([])
                     ax.set_ylabel('')
 
                 for j,artist in enumerate(ax.artists):
-                    if np.mod(j,4) !=0:
+                    if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
                         # Set the linecolor on the artist to the facecolor, and set the facecolor to None
-                        print(j,artist)
+                        #print(j,artist)
                         col = artist.get_facecolor()
-                        print(j,artist)
+                        #print(j,artist)
                         artist.set_edgecolor(col)
-                        print(j,artist)
+                        #print(j,artist)
                         artist.set_facecolor('None')
                 
                         # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)

From cfc535dea502eee3e62fb4ce4102a1cf236509ac Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 4 Sep 2018 13:23:57 +0200
Subject: [PATCH 065/129]  batch_update.py and update_yaml.py for updating
 datasets, new interface figures, temporary fix concerning sign of advection,
 lots more ...

---
 class4gl/class4gl.py                          |  15 +-
 class4gl/data_global.py                       |   1 +
 class4gl/interface/interface_koeppen.py       | 530 ++++++++++++++++++
 class4gl/model.py                             |  12 +-
 class4gl/simulations/batch_simulations.py     |   6 +
 class4gl/simulations/batch_update.py          | 154 +++++
 class4gl/simulations/simulations.py           |   9 +-
 class4gl/simulations/simulations_iter.py      |   1 +
 class4gl/simulations/simulations_smchange2.py |   1 +
 class4gl/simulations/simulations_wwilt_wfc.py |   1 +
 class4gl/simulations/update_yaml.py           | 331 +++++++++++
 class4gl/simulations/update_yaml_old.py       | 277 +++++++++
 12 files changed, 1323 insertions(+), 15 deletions(-)
 create mode 100644 class4gl/interface/interface_koeppen.py
 create mode 100644 class4gl/simulations/batch_update.py
 create mode 100644 class4gl/simulations/update_yaml.py
 create mode 100644 class4gl/simulations/update_yaml_old.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 51d58e1..7fe3767 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -860,10 +860,6 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
         # by default, we get all dataset keys
         keys = list(globaldata.datasets.keys())
 
-        # We add LAI manually, because it is not listed in the datasets and
-        #they its retreival is hard coded below based on LAIpixel and cveg
-        if ('LAIpixel' in keys) and ('cveg' in keys):
-            keys.append('LAI')
 
         # # In case there is surface pressure, we also calculate the half-level
         # # and full-level pressure fields
@@ -873,9 +869,11 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
 
         # If specified, we only take the keys that are in only_keys
         if only_keys is not None:
-            for key in keys:
+            cycle_keys = list(keys)
+            for key in cycle_keys:
                 if key not in only_keys:
                     keys.remove(key)
+
                 
         # If specified, we take out keys that are in exclude keys
         if exclude_keys is not None:
@@ -883,9 +881,16 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                 if key in exclude_keys:
                     keys.remove(key)
 
+        # We add LAI manually, because it is not listed in the datasets and
+        #they its retreival is hard coded below based on LAIpixel and cveg
+        if ('LAIpixel' in keys) and ('cveg' in keys):
+            keys.append('LAI')
+
         # we set everything to nan first in the pars section (non-profile parameters
         # without lev argument), so that we can check afterwards whether the
         # data is well-fetched or not.
+
+
         for key in keys:
             if not ((key in globaldata.datasets) and \
                 (globaldata.datasets[key].page is not None) and \
diff --git a/class4gl/data_global.py b/class4gl/data_global.py
index 63081f1..73f0dda 100644
--- a/class4gl/data_global.py
+++ b/class4gl/data_global.py
@@ -216,6 +216,7 @@ def browse_page(self,rewind=2,**args):
 
 class data_global(object):
     def __init__(self,sources= {
+        'KOEPPEN:KGC'   : '/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc',
         # # old gleam
         # 'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMsurf_*_GLEAM_v3.1a.nc:SMsurf',
         # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
new file mode 100644
index 0000000..6af9a7c
--- /dev/null
+++ b/class4gl/interface/interface_koeppen.py
@@ -0,0 +1,530 @@
+'''
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False)
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--show_control_parameters',default=True)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--figure_filename_2',default=None)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+import xarray as xr
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if bool(args.load_globaldata):
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing+'/',\
+                      globaldata,\
+                      refetch_records=False
+                    )
+'''
+if args.make_figures:
+    """
+    # the lines below activate TaylorPlots but it is disabled for now
+    fig = plt.figure(figsize=(10,7))   #width,height
+    i = 1                                                                           
+    axes = {}         
+    axes_taylor = {}         
+    
+    colors = ['r','g','b','m']
+    symbols = ['*','x','+']
+    dias = {}
+    
+    for varkey in ['h','theta','q']:                                                    
+        axes[varkey] = fig.add_subplot(2,3,i)                                       
+        #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+    
+        #print(obs.std())
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        if i == 0:
+            dias[varkey]._ax.axis["left"].label.set_text(\
+                "Standard deviation (model) / Standard deviation (observations)")
+            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # Q95 = obs.quantile(0.95)
+        # Q95 = obs.quantile(0.90)
+        # Add RMS contours, and label them
+        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        #dia._ax.set_title(season.capitalize())
+    
+        dias[varkey].add_grid()
+    
+    
+        #dia.ax.plot(x99,y99,color='k')
+    
+        
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+            # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+            # clearsky = (cc < 0.05)
+            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            x, y = obs.values,mod.values
+            print(key,len(obs.values))
+    
+            STD_OBS = obs.std()
+            #scores
+            PR = pearsonr(mod,obs)[0]
+            RMSE = rmse(obs,mod)                                               
+            BIAS = np.mean(mod) - np.mean(obs)
+            STD = mod.std()
+            
+            # fit = np.polyfit(x,y,deg=1)
+            # axes[varkey].plot(x, fit[0] * x + fit[1],\
+            #                   color=colors[ikey],alpha=0.8,lw=2,\
+            #                   label=key+", "+\
+            #                               'R = '+str(round(PR,3))+', '+\
+            #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+            #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+            # axes[varkey].legend(fontsize=5)
+            
+            # print(STD)
+            # print(PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,
+                           marker='o', ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=key)
+    
+        # put ticker position, see
+        # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+        # dia.ax.axis['bottom'].
+        # dia.ax.axis['left'].
+        # dia.ax.axis['left'].
+    
+        i += 1
+    
+    i = 0
+    for varkey in ['h','theta','q']:                                                    
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        clearsky = (cc < 0.05)
+    
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+    
+    
+        nbins=40       
+        x, y = obs.values,mod.values
+        
+        xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+        zi = np.zeros_like(xi)*np.nan       
+        for ibin in range(nbins):
+            xmin = x.min() + ibin * (x.max() - x.min())/nbins
+            xmax = xmin + (x.max() - x.min())/nbins
+            in_bin = ((x >= xmin) & (x < xmax))
+            ybin = y[in_bin]
+            xbin = x[in_bin]
+            if len(ybin) > 20:
+                k = kde.gaussian_kde((ybin))
+                zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+        zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+        zi_int = zi.cumsum(axis=1) 
+                     #  label=key+", "+\
+                     #                    'R = '+str(round(PR[0],3))+', '+\
+                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.5,0.84] ,
+                colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.84] ,
+                colors=['darkred'],alpha=0.5,)
+        nanxi = xi[zi != np.nan]
+        axes[varkey].set_xlim((nanxi.min(),nanxi.max()))
+        axes[varkey].set_ylim((nanxi.min(),nanxi.max()))
+        print(varkey,(nanxi.min(),nanxi.max()))
+    
+    
+        latex = {}
+        latex['dthetadt'] =  r'$d \theta / dt $'
+        latex['dqdt'] =      r'$d q / dt $'
+        latex['dhdt'] =      r'$d h / dt $'
+    
+        axes[varkey].set_xlabel('observations')     
+        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+    
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+    
+        axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
+                                      'R = '+str(round(PR,3))+', '+\
+                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+                             s=0.1,alpha=0.14,color='k')
+        axes[varkey].legend(fontsize=5)
+        
+
+
+
+        axes[varkey].set_xlabel('observations')     
+        if i==0:                                    
+            axes[varkey].set_ylabel('model')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+    
+    
+    
+    # legend for different forcing simulations (colors)
+    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    leg = []
+    for ikey,key in enumerate(args.experiments.strip().split(' ')):
+        leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+        leg.append(leg1)
+    ax.axis('off')
+    #leg1 =
+    ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+    
+    
+    # # legend for different stations (symbols)
+    # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # isymbol = 0
+    # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+    #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+    #     leg.append(leg1)
+    #     isymbol += 1
+    # 
+    # # symbol for all stations
+    # leg1, = ax.plot([],'ko',markersize=10)
+    # leg.append(leg1)
+    7
+    
+    # ax.axis('off')
+    # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    
+    
+    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+    
+    
+    #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+    # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+    # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+    
+    if args.figure_filename is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+    fig.show()  
+"""
+    if bool(args.show_control_parameters):
+
+        import seaborn as sns
+
+        pkmn_type_colors = [
+                                            '#A0A0A0',  # Poison
+                                            '#78C850',  # Grass
+                                            '#F08030',  # Fire
+                                            '#6890F0',  # Water
+                                            '#F08030',  # Fire
+                                            '#C03028',  # Fighting
+                                            '#F85888',  # Psychic
+                                            '#A8B820',  # Bug
+                                            '#A8A878',  # Normal
+                                            '#F8D030',  # Electric
+                                            '#E0C068',  # Ground
+                                            '#EE99AC',  # Fairy
+                                            '#B8A038',  # Rock
+                                            '#705898',  # Ghost
+                                            '#98D8D8',  # Ice
+                                            '#7038F8',  # Dragon
+                                           ]
+
+
+
+        sns.set_style('whitegrid')
+        #sns.set()
+        fig = pl.figure(figsize=(12,8))
+        i = 1
+        axes = {}
+        data_all = pd.DataFrame()
+        data_input = pd.DataFrame()
+        
+        
+        
+        # #for varkey in ['theta','q']:     
+        # EF =\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
+        # EF[EF<0] = np.nan
+        # EF[EF>1] = np.nan
+        
+        # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
+        
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        data_all = pd.DataFrame()
+
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        tempdatamodstats["source"] = "soundings"
+        tempdatamodstats["source_index"] = "soundings"
+
+        ini_ref = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+        tempdataini_this = pd.DataFrame(ini_ref.copy())
+
+        tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+        tempdatamodstats['STNID']= tempdataini_this.STNID
+        tempdatamodstats['source']= "soundings"
+        tempdatamodstats['source_index']= "soundings"
+        tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+        #print('hello')
+
+        tempdataini = pd.DataFrame(ini_ref)
+        tempdataini["source"] = "soundings"
+        tempdataini["source_index"] = "soundings"
+        tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+        #print('hello2')
+
+
+        data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+        data_input = pd.concat([data_input,tempdataini],axis=0)
+        #print(data_input.shape)
+        #print(data_all.shape)
+
+            
+        for key in list(args.experiments.strip().split(' ')):
+
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+            tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+            tempdatamodstats['STNID']= tempdataini_this.STNID
+            tempdatamodstats['source']= key
+            tempdatamodstats['source_index']= key
+            tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+            #print('hello')
+
+
+            tempdataini = pd.DataFrame(ini_ref.copy())
+            tempdataini["source"] = key 
+            tempdataini["source_index"] = key
+            tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+    
+
+            #print('hello2')
+            index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
+            #print('hello3')
+
+            tempdataini = tempdataini.loc[index_intersect]
+            #print('hello4')
+            tempdatamodstats = tempdatamodstats.loc[index_intersect]
+            #print('hello5')
+
+
+            # data[varkey] = tempdatamodstats['d'+varkey+'dt']
+            data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+            data_input = pd.concat([data_input, tempdataini],axis=0)
+            #print(data_input.shape)
+            #print(data_all.shape)
+
+        data_input.cc = data_input.cc.clip(0.,+np.inf)
+
+        for varkey in ['h','theta','q']:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+            data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            #print(data_input.shape)
+            #print(data_all.shape)
+        xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
+        lookuptable = pd.Series(xrkoeppen['KGCID'])
+        data_all['KGCname'] = data_input['KGC'].map(lookuptable)
+        #print('hello6')
+        #print(data_all.columns)
+        #print('hello7')
+        for varkey in ['h','theta','q']:
+            #input_keys =['wg','cc']
+            #for input_key in input_keys:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+
+            #print('hello8')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            #input_key_full = input_key + "["+units[input_key]+"]"
+            #print('hello9')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            qvalmax = data_all[varkey_full].quantile(0.999)
+            qvalmin = data_all[varkey_full].quantile(0.001)
+            select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
+            #print('hello11')
+            data_all = data_all[select_data]
+            #print('hello12')
+            data_input = data_input[select_data.values]
+            #print('hello13')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            #print('hello10')
+            
+
+            sns.set(style="ticks", palette="pastel")
+            ax = fig.add_subplot(3,1,i)
+            #sns.violinplot(x='KGC',y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+            
+            #ax.set_title(input_key_full)
+            sb = sns.boxplot(x='KGCname', y=varkey_full, hue="source",
+                             palette=pkmn_type_colors,
+                            # palette=["m", "g",'r','b'],
+                             linewidth=1.2, data=data_all,sym='')
+            if i ==1:
+                 plt.legend(loc='upper right',fontsize=7.)
+            else:
+                 ax.get_legend().set_visible(False)
+            #     plt.legend('off')
+            if i >= 2:
+                ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=45.,ha='right')
+            else:
+                ax.set_xticklabels([])
+                ax.set_xlabel('')
+
+            # ax.set_yticklabels([])
+            # ax.set_ylabel('')
+
+            for j,artist in enumerate(ax.artists):
+                if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
+                    # Set the linecolor on the artist to the facecolor, and set the facecolor to None
+                    #print(j,artist)
+                    col = artist.get_facecolor()
+                    #print(j,artist)
+                    artist.set_edgecolor(col)
+                    #print(j,artist)
+                    artist.set_facecolor('None')
+            
+                    # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
+                    # Loop over them here, and use the same colour as above
+                    
+                    for k in range(j*5,j*5+5):
+                        line = ax.lines[k]
+                        line.set_color(col)
+                        line.set_mfc(col)
+                        line.set_mec(col)
+            
+            # Also fix the legend
+            j = 0
+            for legpatch in ax.get_legend().get_patches():
+                if j > 0:
+
+                    col = legpatch.get_facecolor()
+                    legpatch.set_edgecolor(col)
+                    legpatch.set_facecolor('None')
+                j +=1
+
+
+
+
+            #ax.grid()
+            #sns.despine(offset=10, trim=True)
+            i +=1
+        fig.tight_layout()
+        fig.subplots_adjust( bottom=0.18,left=0.09,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
+        if args.figure_filename_2 is not None:
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
+        fig.show()
+
+
+
diff --git a/class4gl/model.py b/class4gl/model.py
index e6eeb07..471d218 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -965,15 +965,15 @@ def run_mixed_layer(self):
         self.dthetatend = l_entrainment*dthetatend_pre + \
                         (1.-l_entrainment)*0.
         self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h - self.advtheta)
         self.htend = l_entrainment*htend_pre + \
                      (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
         #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
         #stop
 
 
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h - self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h - self.advCO2
 
 
         # self.qtend = l_entrainment*qtend_pre + \
@@ -1010,8 +1010,8 @@ def run_mixed_layer(self):
      
         # assume u + du = ug, so ug - u = du
         if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h - self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h - self.advv
   
             self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
             self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
@@ -1076,7 +1076,7 @@ def integrate_mixed_layer(self):
 
             # take into account advection for the whole profile
                 
-                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
+                self.air_ap[var] = self.air_ap[var] - self.dtcur * self.air_ap['adv'+var]
 
             var = 'z'
             #print(self.air_ap[var])
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index c5bb7c9..9b6398e 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -37,6 +37,11 @@
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
 parser.add_argument('--path_forcing') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_experiments') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+
+
+#arguments only used for update_yaml.py
+parser.add_argument('--path_dataset') 
+parser.add_argument('--global_keys') 
 args = parser.parse_args()
 
 sys.path.insert(0, args.c4gl_path_lib)
@@ -110,6 +115,7 @@
     if args.cleanup_experiments:
         os.system("rm -R "+args.path_experiments+'/'+EXP)
 
+    # C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
     command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
                 str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
     # propagate arguments towards the job script
diff --git a/class4gl/simulations/batch_update.py b/class4gl/simulations/batch_update.py
new file mode 100644
index 0000000..d9ff78a
--- /dev/null
+++ b/class4gl/simulations/batch_update.py
@@ -0,0 +1,154 @@
+# -*- coding: utf-8 -*-
+
+"""
+Usage:
+python batch_update.py --exec $CLASS4GL/simulations/update_yaml_old.py
+--path_experiments $VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC/ --path_forcing
+$VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC_BACKUP_20180904/ --c4gl_path_lib
+$CLASS4GL --split_by 50 --global_keys "KGC" --subset_forcing ini --experiments
+"GLOBAL_NOAC"
+"""
+
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+parser = argparse.ArgumentParser()
+#if __name__ == '__main__':
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--pbs_string',default=' -l walltime=:2:0:0')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling')
+parser.add_argument('--subset_forcing',default='morning') 
+                                        # this tells which yaml subset
+                                        # to initialize with.
+                                        # Most common options are
+                                        # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime')
+# delete folders of experiments before running them
+parser.add_argument('--cleanup_experiments',default=False)
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=50)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--path_forcing') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+
+
+#arguments only used for update_yaml.py
+parser.add_argument('--path_dataset') 
+parser.add_argument('--global_keys') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+# path_forcingSET = args.path_forcing+'/'+SET+'/'
+
+print("getting all stations from --path_forcing")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting stations by --station_id")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table [--first_station_row,--last_station_row]")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,\
+                                         refetch_records=False,\
+                                        )
+
+print('splitting batch in --split_by='+args.split_by+' jobs.')
+totalchunks = 0
+for istation,current_station in all_stations_select.iterrows():
+    records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+    chunks_current_station = len(records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
+    totalchunks +=chunks_current_station
+
+print('total chunks (= size of array-job) per experiment: ' + str(totalchunks))
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+
+print(args.experiments.strip().split(" "))
+
+for EXP in args.experiments.strip().split(" "):
+    if args.cleanup_experiments:
+        os.system("rm -R "+args.path_experiments+'/'+EXP)
+
+    #C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+    command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+                str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
+    # propagate arguments towards the job script
+    for argkey in args.__dict__.keys():
+        if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
+            # default values are specified in the simulation script, so
+            # excluded here
+            (args.__dict__[argkey] is not None)
+           ):
+                command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+
+    print('Submitting array job for experiment '+EXP+': '+command)
+    os.system(command)
+
+
+    #os.system(command)
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 49eb282..bced8d3 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -13,6 +13,7 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
 parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 parser.add_argument('--first_station_row')
@@ -57,10 +58,10 @@
 
 EXP_DEFS  =\
 {
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
 
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 5dfbaff..438db32 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -39,6 +39,7 @@
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
+    #parser.add_argument('--timestamp')
     parser.add_argument('--global-chunk')
     parser.add_argument('--first-station')
     parser.add_argument('--last-station')
diff --git a/class4gl/simulations/simulations_smchange2.py b/class4gl/simulations/simulations_smchange2.py
index 99c38a9..d79a46f 100644
--- a/class4gl/simulations/simulations_smchange2.py
+++ b/class4gl/simulations/simulations_smchange2.py
@@ -13,6 +13,7 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
 parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 parser.add_argument('--first_station_row')
diff --git a/class4gl/simulations/simulations_wwilt_wfc.py b/class4gl/simulations/simulations_wwilt_wfc.py
index 6f0afd3..1e5450c 100644
--- a/class4gl/simulations/simulations_wwilt_wfc.py
+++ b/class4gl/simulations/simulations_wwilt_wfc.py
@@ -13,6 +13,7 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
 parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 parser.add_argument('--first_station_row')
diff --git a/class4gl/simulations/update_yaml.py b/class4gl/simulations/update_yaml.py
new file mode 100644
index 0000000..1681e88
--- /dev/null
+++ b/class4gl/simulations/update_yaml.py
@@ -0,0 +1,331 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--global_keys') 
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_morning = get_records(run_stations,\
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
+                              refetch_records=False,
+                              )
+
+# # note that if runtime is an integer number, we don't need to get the afternoon
+# # profiles. 
+# if args.runtime == 'from_afternoon_profile':
+#     print('Fetching afternoon records for determining the simulation runtimes')
+#     records_afternoon = get_records(run_stations,\
+#                                     args.path_forcing,\
+#                                     subset='afternoon',
+#                                     refetch_records=False,
+#                                     )
+#     
+#     # print(records_morning.index)
+#     # print(records_afternoon.index)
+#     # align afternoon records with the noon records, and set same index
+#     print('hello')
+#     print(len(records_afternoon))
+#     print(len(records_morning))
+# 
+#     print("aligning morning and afternoon records")
+#     records_morning['dates'] = records_morning.ldatetime.dt.date
+#     records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+#     records_afternoon.set_index(['STNID','dates'],inplace=True)
+#     ini_index_dates = records_morning.set_index(['STNID','dates']).index
+#     records_afternoon = records_afternoon.loc[ini_index_dates]
+#     records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            fn_forcing = \
+                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+
+            #file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            file_ini = open(fn_ini,'w')
+
+            #iexp = 0
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+            isim = 0
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    print('starting '+str(isim+1)+' out of '+\
+                      str(len(records_morning_station_chunk) )+\
+                      ' (station total: ',str(len(records_morning_station)),')')  
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    if args.runtime == 'from_afternoon_profile':
+                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                        c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                          record_afternoon.index_start, 
+                                                          record_afternoon.index_end,
+                                                        mode='ini')
+                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())
+                    else:
+                        runtime = int(args.runtime)
+
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        runtime})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                        except:
+                            print('run not succesfull')
+                        onerun = True
+
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_success':
+                        try:
+                            c4gl.run()
+                            print('run succesfull')
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                      #timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+                    isim += 1
+
+
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/class4gl/simulations/update_yaml_old.py b/class4gl/simulations/update_yaml_old.py
new file mode 100644
index 0000000..18b3071
--- /dev/null
+++ b/class4gl/simulations/update_yaml_old.py
@@ -0,0 +1,277 @@
+# -*- coding: utf-8 -*-
+
+""" 
+Purpose:
+    update variables in class4gl yaml files, eg., when you need new categorical
+    values in the table.
+
+
+"""
+
+
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import dateutil.parser
+
+import argparse
+
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--path_experiments')
+parser.add_argument('--experiments')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--mode',default='ini') # this tells which yaml subset
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to update in the yaml
+                                                      # dataset.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+parser.add_argument('--split_by',default=-1)# station soundings are split
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--global_keys') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+os.system('mkdir -p '+args.path_experiments)
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            #chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+
+            chunks_current_station = len(all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
+            print('chunks_current_station',chunks_current_station)
+
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk =all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique()[int(args.global_chunk_number) - totalchunks ]
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching current records')
+records_forcing = get_records(run_stations,\
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
+                              refetch_records=False,
+                              )
+
+# if args.timestamp is None:
+#     backupdir = args.path_forcing+'/'+dt.datetime.now().isoformat()+'/'
+# else: 
+#     backupdir = args.path_forcing+'/'+args.timestamp+'/'
+# print('creating backup dir: '+backupdir)
+# os.system('mkdir -p "'+backupdir+'"')
+
+
+
+
+
+for istation,current_station in run_stations.iterrows():
+    records_forcing_station = records_forcing.query('STNID == ' +\
+                                                    str(current_station.name))
+
+    records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+                                                    str(current_station.name)+\
+                                                   '& chunk == '+str(run_station_chunk))
+    print('lenrecords_forcing_station: ',len(records_forcing_station))
+    print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
+    print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
+    
+    # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
+    #     print("warning: outside of profile number range for station "+\
+    #           str(current_station)+". Skipping chunk number for this station.")
+    if len(records_forcing_station_chunk) == 0:
+        print("warning: outside of profile number range for station "+\
+              str(current_station)+". Skipping chunk number for this station.")
+    else:
+        # normal case
+        if ((int(args.split_by) > 0) or \
+            (os.path.isfile(args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                 str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'))):
+            fn_forcing = \
+                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            file_forcing = \
+                open(fn_forcing,'r')
+            fn_experiment = args.path_experiments+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            file_experiment = \
+                open(fn_experiment,'w')
+            fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+        else:
+            print("\
+Warning. We are choosing chunk 0 without specifying it in filename.    \
+ No-chunk naming will be removed in the future."\
+                 )
+
+            fn_forcing = \
+                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            file_forcing = \
+                open(fn_forcing,'r')
+            fn_experiment = args.path_experiments+'/'+format(current_station.name,'05d')+'_'+\
+                     args.subset_forcing+'.yaml'
+            file_experiment = \
+                open(fn_experiment,'w')
+            fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
+                     args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          args.subset_forcing+'.pkl'
+
+        onerun = False
+        print('starting station chunk number: '\
+              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+        #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+        #                                                 str(current_station.name)+\
+        #                                                '& chunk == '+str(run_station_chunk))
+        isim = 0
+        for (STNID,chunk,index),record_forcing in records_forcing_station_chunk.iterrows():
+                print('starting '+str(isim+1)+' out of '+\
+                  str(len(records_forcing_station_chunk) )+\
+                  ' (station total: ',str(len(records_forcing_station)),')')  
+            
+        
+                c4gli_forcing = get_record_yaml(file_forcing, 
+                                                record_forcing.index_start, 
+                                                record_forcing.index_end,
+                                                mode=args.mode)
+                
+                #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
+                
+                if args.global_keys is not None:
+                    print(args.global_keys.strip(' ').split(' '))
+                    c4gli_forcing.get_global_input(
+                        globaldata, 
+                        only_keys=args.global_keys.strip(' ').split(' ')
+                    )
+        
+
+
+                c4gli_forcing.dump(file_experiment)
+                    
+                    
+                onerun = True
+                isim += 1
+
+
+        file_forcing.close()
+        file_experiment.close()
+
+        if onerun:
+            # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
+            # if os.path.isfile(fn_forcing_pkl):
+            #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
+            # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
+                                                       args.path_experiments,\
+                                                       getchunk = int(run_station_chunk),\
+                                                       subset=args.subset_forcing,
+                                                       refetch_records=True,
+                                                       )
+

From dbf3f270a34c8b7b861f070b18426bdabf16a20d Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 4 Sep 2018 21:29:33 +0200
Subject: [PATCH 066/129] correction in advection terms

---
 class4gl/model.py                        | 2 +-
 class4gl/simulations/simulations.py      | 8 ++++++++
 class4gl/simulations/simulations_iter.py | 4 ++++
 3 files changed, 13 insertions(+), 1 deletion(-)

diff --git a/class4gl/model.py b/class4gl/model.py
index 471d218..6a9d2c3 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -926,7 +926,7 @@ def run_mixed_layer(self):
         htend_pre       = self.we + self.ws + self.wf - self.M
         
         #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h - self.advtheta
         
  
         #print('thetatend_pre',thetatend_pre)
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index bced8d3..e10c1da 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -58,10 +58,18 @@
 
 EXP_DEFS  =\
 {
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
 
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 438db32..cdc8923 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -29,6 +29,10 @@
 
 EXP_DEFS  =\
 {
+  'GLOBAL_ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},

From 659ca0f63dca9ab5909e24103d758cccf8871897 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Fri, 7 Sep 2018 17:49:35 +0200
Subject: [PATCH 067/129] era-interim input

---
 class4gl/class4gl.py                     | 262 ++++++++++++++-
 class4gl/interface/interface.py          |   2 +-
 class4gl/interface/interface_koeppen.py  | 155 +++++++--
 class4gl/interface/interface_stations.py |  10 +-
 class4gl/interface/taylorDiagram.py      |   9 +-
 class4gl/interface_functions.py          |   2 +-
 class4gl/interface_multi.py              | 411 ++++++++++++-----------
 class4gl/setup/setup_era.py              | 270 +++++++++++++++
 class4gl/simulations/simulations.py      |  36 +-
 9 files changed, 913 insertions(+), 244 deletions(-)
 create mode 100644 class4gl/setup/setup_era.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 7fe3767..9b5c8b9 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -378,6 +378,7 @@ def update(self,source,**kwargs):
         #print(source,kwargs)
 
         for key,data in kwargs.items():
+            #print('update',key,data)
 
             #print(key)
             # if the key is not in class4gl_input object, then just add it. In
@@ -414,24 +415,37 @@ def update(self,source,**kwargs):
                     
 
             elif type(self.__dict__[key]) == model_input:
-                # if the data type is a model_input, then we update its internal
+                # if the data type is a model_input (pars), then we update its internal
                 # dictionary of parameters
                 if type(data) == model_input:
                     self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
                                                    **data.__dict__}
                     datakeys = list(data.__dict__.keys())
                 elif type(data) == dict:
-                    self.__dict__[key].__dict__ = {**self.__dict__[key].__dict__, \
-                                                   **data}
                     datakeys = list(data.keys())
+                    datavalues = list(data.values())
+                    for idatavalue,datavalue in enumerate(datavalues):
+
+                        # convert numpy to native python value types, so that
+                        # we get clean output in the yaml file
+                        if type(datavalue).__module__ == 'numpy':
+                            datavalues[idatavalue] = datavalue.item()
+
+                    self.__dict__[key].__dict__ = \
+                            {**self.__dict__[key].__dict__, \
+                            **dict(zip(datakeys, datavalues))}
                 else:
                     raise TypeError('input key '+key+' is not of the same type\
                                     as the one in the class4gl_object')
 
+
+
             elif type(self.__dict__[key]) == dict:
                 # if the data type is a dictionary, we update the
                 # dictionary 
+               # print('before update', self.__dict__[key] , data)
                 self.__dict__[key] = {self.__dict__[key] , data}
+               # print('after update',self.__dict__[key] )
                 datakeys = list(data.keys())
 
 
@@ -576,7 +590,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         # # this is an alternative pipe/numpy method
         # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
         valid_indices = air_balloon.index[is_valid].values
-        print(valid_indices)
+        #print(valid_indices)
 
         dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
 
@@ -860,6 +874,7 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
         # by default, we get all dataset keys
         keys = list(globaldata.datasets.keys())
 
+        #print('keys orig', keys)
 
         # # In case there is surface pressure, we also calculate the half-level
         # # and full-level pressure fields
@@ -874,6 +889,7 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                 if key not in only_keys:
                     keys.remove(key)
 
+        #print('keys 1', keys)
                 
         # If specified, we take out keys that are in exclude keys
         if exclude_keys is not None:
@@ -901,14 +917,15 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
             #else:
             #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
 
-        self.logger.debug('getting keys "'+', '.join(keys)+'\
-                          from global data')
+        #print('keys 2', keys)
 
         for key in keys:
             # If we find it, then we obtain the variables
+            #print('key 0', key)
             if ((key in globaldata.datasets) and \
                 (globaldata.datasets[key].page is not None)):
 
+                #print('key 1', key)
                 # check first whether the dataset has a height coordinate (3d space)
                 if 'lev' in globaldata.datasets[key].page[key].dims:
 
@@ -926,12 +943,35 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                         
                         # if we have a time dimension, then we look up the required timesteps during the class simulation
                         if 'time' in list(globaldata.datasets[key].page[key].dims):
-                            itimes = ((globaldata.datasets[key].page.time >= \
-                                       classdatetime) & (globaldata.datasets[key].page.time < classdatetime_stop))
+
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+                            
+                            idatetime = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+                            if key not in ['t','u','v','q']:
+                                if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                    idatetime += 1
+                            
+                            DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime_stop))
+                            idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+                            #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+                            if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetime_stop)):
+                                idatetimeend -= 1
+                            idatetime = np.min((idatetime,idatetimeend))
+                            #for gleam, we take the previous day values
+
+                            # in case of soil temperature, we take the exact
+                            # timing (which is the morning)
+                            if key in ['t','u','v','q']:
+                                idatetimeend = idatetime
+                            
+                            itimes = range(idatetime,idatetimeend+1)
+                            #print(key,'itimes',itimes)
+
 
                             # In case we didn't find any correct time, we take the
                             # closest one.
-                            if np.sum(itimes) == 0.:
+                            if len(itimes) == 0:
 
 
                                 classdatetimemean = \
@@ -969,8 +1009,6 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                 else:
                     # this procedure is for reading the ground fields (2d space). 
                     # Actually, the code should be simplified to a similar fasion as the 3d procedure above and tested again.
-
-    
                     if 'time' in list(globaldata.datasets[key].page[key].dims):
     
                        # first, we browse to the correct file
@@ -1029,11 +1067,12 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                             
                             idatetime = np.where((DIST) == np.min(DIST))[0][0]
                             #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
-                                idatetime += 1
+                            if key not in ['Tsoil','T2']:
+                                if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+                                    idatetime += 1
                             
                             classdatetimeend = np.datetime64(\
-                                                             self.pars.datetime +\
+                                                             self.pars.datetime_daylight +\
                                                              dt.timedelta(seconds=self.pars.runtime)\
                                                             ) 
                             DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetimeend))
@@ -1224,6 +1263,23 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
             # self.update(source='globaldata',\
             #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
 
+
+    def query_source(self,var):
+        """ 
+        purpose:
+            this procedure returns the name of the data source for a certain
+            variable
+        
+        input:
+            var: this should be in the format "section:variable", eg.,
+            "pars:h", or "air_ac:theta"
+
+        """
+
+        for source,vars_in_source in self.sources.items():
+            if var in vars_in_source:
+                return source
+
     def check_source(self,source,check_only_sections=None):
         """ this procedure checks whether data of a specified source is valid.
 
@@ -1316,6 +1372,184 @@ def check_source_globaldata(self):
 
         return source_globaldata_ok
 
+    def mixed_layer_fit(self,air_ap,source,mode):
+        """ 
+            Purpose: 
+
+
+        """
+
+
+        # Raise an error in case the input stream is not the correct object
+        # if type(wy_strm) is not wyoming:
+        #    raise TypeError('Not a wyoming type input stream')
+
+        # Let's tell the class_input object that it is a Wyoming fit type
+        self.air_ap_type = source+'_fit'
+        # ... and which mode of fitting we apply
+        self.air_ap_mode = mode
+
+
+        # Therefore, determine the sounding that are valid for 'any' column 
+        is_valid = ~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)
+        #is_valid = (air_ap.z >= 0)
+        # # this is an alternative pipe/numpy method
+        # (~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)).pipe(np.where)[0]
+        valid_indices = air_ap.index[is_valid].values
+        #print(valid_indices)
+
+
+        hvalues = {}
+        if len(valid_indices) > 0:
+            #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
+            hvalues['h_b'] ,hvalues['h_u'],hvalues['h_l']  = blh(air_ap.z,air_ap.thetav,np.sqrt(air_ap.u**2. + air_ap.u**2.))
+            
+            hvalues['h_b']  = np.max((hvalues['h_b'] ,10.))
+            hvalues['h_u']  = np.max((hvalues['h_u'] ,10.)) #upper limit of mixed layer height
+            hvalues['h_l']  = np.max((hvalues['h_l'] ,10.)) #low limit of mixed layer height
+            hvalues['h_e']  = np.abs( hvalues['h_u']  - hvalues['h_l'] ) # error of mixed-layer height
+            
+            # the final mixed-layer height that will be used by class. We round it
+            # to 1 decimal so that we get a clean yaml output format
+            hvalues['h']  = np.round(hvalues['h_'+mode],1)
+        else:
+            hvalues['h_u']  =np.nan
+            hvalues['h_l']  =np.nan
+            hvalues['h_e']  =np.nan
+            hvalues['h']    =np.nan
+        self.update(source='fit_from_'+source,pars=hvalues)
+
+        if np.isnan(self.pars.h ):
+            self.pars.Ps  = nan
+
+        mlvalues = {}
+        if ~np.isnan(self.pars.h ):
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_ap.z < self.pars.h)
+            valid_indices_below_h =  air_ap.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_ap[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_ap.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_ap.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_ap)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            mlvalues['theta'] = ml_mean.theta
+            mlvalues['q']     = ml_mean.q
+            mlvalues['u']     = ml_mean.u 
+            mlvalues['v']     = ml_mean.v 
+        else:
+            mlvalues['theta']  = np.nan
+            mlvalues['q']  = np.nan
+            mlvalues['u']  = np.nan
+            mlvalues['v']  = np.nan
+            
+
+        self.update(source='fit_from_'+source,pars=mlvalues)
+
+
+        # First 3 data points of the mixed-layer fit. We create a empty head
+        # first
+        air_ap_head = air_ap[0:0] #pd.DataFrame(columns = air_ap.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_ap[air_ap.z > self.pars.h ]
+        
+        #calculate mixed-layer jump ( this should be larger than 0.1)
+        
+        air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+        #air_ap_head['HGHT'] = air_ap_head['z'] \
+        #                        + \
+        #                        np.round(dpars[ 'Station elevation'],1)
+        
+        # make a row object for defining the jump
+        jump = air_ap_head.iloc[0] * np.nan
+            
+        if air_ap_tail.shape[0] > 1:
+
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (self.pars.h - air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = self.pars.theta 
+        z_low =     self.pars.h 
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                (z_mean > (z_low+10.)) and \
+                (theta_mean > (theta_low+0.2) ) and \
+                (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+
+
+
+
+
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        #print(air_ap['PRES'].iloc[0])
+
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+
+        air_ap['p'].iloc[0] =self.pars.Ps  
+        air_ap['p'].iloc[1] =(self.pars.Ps - rho * g * self.pars.h )
+        air_ap['p'].iloc[2] =(self.pars.Ps - rho * g * self.pars.h -0.1)
+
+        self.update(source='fit_from_'+source,air_ap=air_ap)
+
+
 
 class c4gli_iterator():
     """ this iterator allows to loop through an entire yaml file and load class4gl_input sequentially 
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index af989c0..1af9e4c 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -118,7 +118,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     c4gldata[key] = c4gl_interface_soundings( \
                       args.path_experiments+'/'+key+'/',\
-                      args.path_forcing+'/',\
+                      args.path_forcing,\
                       globaldata,\
                       refetch_records=False
                     )
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index 6af9a7c..cbe89f2 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -123,25 +123,132 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False
                     )
+
 '''
+key = args.experiments.strip(' ').split(' ')[0]
+xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
+koeppenlookuptable = pd.DataFrame()
+koeppenlookuptable['KGCID'] = pd.Series(xrkoeppen['KGCID'])
+c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
+    c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
+
+koeppenlookuptable['amount'] = ""
+for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+    print(ikoeppen,':',koeppen)
+    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen.KGCID)
+    print(np.sum(kgc_select))
+    koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
+
+koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
+koeppenlookuptable = koeppenlookuptable[:9]
+koeppenlookuptable = koeppenlookuptable.sort_index()
+
+
+kgccolors = {
+    'Dfa':['navy','white'],
+    'Cfb':['green','white']       ,
+    'BSk':['tan','black']      ,
+    'Csb':['lightgreen','black'] ,     
+    'Cfa':['darkgreen','white']  ,    
+    'BWh':['orange','black']      ,
+    'Aw' :['pink','black'],
+    'Dwc':['rebeccapurple','white'] ,    
+    'Dfb':['darkviolet','white']    , 
+}
+kgcnames = {
+    'Dfa':'snow - fully humid - hot summer',
+    'Cfb':'green'       ,
+    'BSk':''      ,
+    'Csb':''      ,
+    'Cfa':'darkgreen' ,     
+    'BWh':''      ,
+    'Aw' :''     ,
+    'Dwc':''     ,
+    'Dfb':''     ,
+    #'Dfa':'',
+}
+
+
+koeppenlookuptable['color'] = ""
+koeppenlookuptable['textcolor'] = ""
+koeppenlookuptable['name'] = ""
+for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+    print(ikoeppen)
+    print(koeppen.KGCID)
+    print(kgccolors[koeppen.KGCID])
+    koeppenlookuptable['color'].loc[ikoeppen] = kgccolors[koeppen.KGCID][0]
+    koeppenlookuptable['textcolor'].loc[ikoeppen] = kgccolors[koeppen.KGCID][1]
+    koeppenlookuptable['name'].loc[ikoeppen] = kgcnames[koeppen.KGCID]
+
+
 if args.make_figures:
-    """
     # the lines below activate TaylorPlots but it is disabled for now
     fig = plt.figure(figsize=(10,7))   #width,height
     i = 1                                                                           
     axes = {}         
     axes_taylor = {}         
     
-    colors = ['r','g','b','m']
+    colors = ['r','g','b','m','y','purple','orange','sienna','navy']
     symbols = ['*','x','+']
     dias = {}
-    
+
+
+
+    i = 1
     for varkey in ['h','theta','q']:                                                    
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
         axes[varkey] = fig.add_subplot(2,3,i)                                       
+
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
+            icolor = 0
+            for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+                print(ikoeppen,':',koeppen)
+                kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen.KGCID)
+                
+                koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
+                koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
+    
+                #axes[varkey].scatter(koeppen_obs,koeppen_mod,marker=symbols[ikoeppen],color=colors[ikey])
+                         #  label=key+", "+\
+                         #                    'R = '+str(round(PR[0],3))+', '+\
+                         #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                         #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+    
+    
+    
+            # # pl.scatter(obs,mod,label=key+", "+\
+            # #                              'R = '+str(round(PR[0],3))+', '+\
+            # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+            # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+                
+                print('hellobla')
+                print(koeppen.KGCID)
+                print(koeppen.color)
+                dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                               pearsonr(koeppen_mod,koeppen_obs)[0],
+                               annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                               bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                               )
+                icolor += 1
+    
+            latex = {}
+            latex['dthetadt'] =  r'$d \theta / dt $'
+            latex['dqdt'] =      r'$d q / dt $'
+            latex['dhdt'] =      r'$d h / dt $'
+    
+            axes[varkey].set_xlabel('observations')     
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+        if i==1:                                    
+            axes[varkey].set_ylabel('model')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+
+    
+    i = 0
+    for varkey in ['h','theta','q']:                                                    
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
         #print(obs.std())
-        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
         if i == 0:
             dias[varkey]._ax.axis["left"].label.set_text(\
                 "Standard deviation (model) / Standard deviation (observations)")
@@ -161,7 +268,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #dia.ax.plot(x99,y99,color='k')
     
         
-        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        #for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
             # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
             # clearsky = (cc < 0.05)
             # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
@@ -189,11 +297,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             
             # print(STD)
             # print(PR)
-            dias[varkey].add_sample(STD/STD_OBS, PR,
-                           marker='o', ms=5, ls='',
-                           #mfc='k', mec='k', # B&W
-                           mfc=colors[ikey], mec=colors[ikey], # Colors
-                           label=key)
+            dias[varkey].add_sample(STD/STD_OBS, PR,\
+                               annotate='All', zorder=100,color='black',weight='bold',fontsize=5.,\
+                                    bbox={'edgecolor':'black','boxstyle':'circle','fc':'lightgrey','alpha':0.6}\
+                            )
     
         # put ticker position, see
         # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
@@ -202,6 +309,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # dia.ax.axis['left'].
     
         i += 1
+
     
     i = 0
     for varkey in ['h','theta','q']:                                                    
@@ -275,15 +383,15 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     
     
-    # legend for different forcing simulations (colors)
-    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-    leg = []
-    for ikey,key in enumerate(args.experiments.strip().split(' ')):
-        leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
-        leg.append(leg1)
-    ax.axis('off')
-    #leg1 =
-    ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+    # # legend for different forcing simulations (colors)
+    # ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # for ikey,key in enumerate(args.experiments.strip().split(' ')):
+    #     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+    #     leg.append(leg1)
+    # ax.axis('off')
+    # #leg1 =
+    # ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
     
     
     # # legend for different stations (symbols)
@@ -314,7 +422,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     if args.figure_filename is not None:
         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
     fig.show()  
-"""
+
     if bool(args.show_control_parameters):
 
         import seaborn as sns
@@ -429,11 +537,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         for varkey in ['h','theta','q']:
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            data_all['KGCname'] = data_input['KGCname']
             #print(data_input.shape)
             #print(data_all.shape)
-        xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
-        lookuptable = pd.Series(xrkoeppen['KGCID'])
-        data_all['KGCname'] = data_input['KGC'].map(lookuptable)
+        # xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
+        # lookuptable = pd.Series(xrkoeppen['KGCID'])
+        # data_all['KGCname'] = data_input['KGC'].map(lookuptable)
         #print('hello6')
         #print(data_all.columns)
         #print('hello7')
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index 9e6b82a..c7ac908 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -203,13 +203,13 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     i = 0
     for varkey in ['h','theta','q']:                                                    
         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-            isymbol = 0
+            istation = 0
             for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
                 indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
                 station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
                 station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
     
-                axes[varkey].scatter(station_obs,station_mod,marker=symbols[isymbol],color=colors[ikey])
+                axes[varkey].scatter(station_obs,station_mod,marker=symbols[istation],color=colors[ikey])
                          #  label=key+", "+\
                          #                    'R = '+str(round(PR[0],3))+', '+\
                          #                    'RMSE = '+str(round(RMSE,5))+', '+\
@@ -223,12 +223,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
                 
                 dias[varkey].add_sample(station_mod.std()/station_obs.std(),
-                               pearsonr(station_mod,station_obs)[0],
-                               marker=symbols[isymbol], ms=5, ls='',
+                               pearsonr(station_mod,station_obs)[0],annotate=symbols[istation],
+                               marker=symbols[istation], ms=5, ls='',
                                #mfc='k', mec='k', # B&W
                                mfc=colors[ikey], mec=colors[ikey], # Colors
                                label=key)
-                isymbol += 1
+                istation += 1
     
     
             axes[varkey].set_xlabel('observations')     
diff --git a/class4gl/interface/taylorDiagram.py b/class4gl/interface/taylorDiagram.py
index 9c51b48..d9e9e26 100644
--- a/class4gl/interface/taylorDiagram.py
+++ b/class4gl/interface/taylorDiagram.py
@@ -106,14 +106,19 @@ def __init__(self, refstd,
         # Collect sample points for latter use (e.g. legend)
         self.samplePoints = [l]
 
-    def add_sample(self, stddev, corrcoef, *args, **kwargs):
+    def add_sample(self, stddev, corrcoef, annotate=None,*args, **kwargs):
         """
         Add sample (*stddev*, *corrcoeff*) to the Taylor
         diagram. *args* and *kwargs* are directly propagated to the
         `Figure.plot` command.
         """
+        print(annotate)
 
-        l, = self.ax.plot(NP.arccos(corrcoef), stddev,
+        if annotate is not None:
+            l = self.ax.annotate(annotate,xy=(NP.arccos(corrcoef), stddev),\
+                              *args, **kwargs)  # (theta, radius)
+        else:
+            l, = self.ax.plot(NP.arccos(corrcoef), stddev,
                           *args, **kwargs)  # (theta, radius)
         self.samplePoints.append(l)
 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 43f0be6..0da9a00 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -178,7 +178,7 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
 
 
 class stations(object):
-    def __init__(self,path,suffix='ini',refetch_stations=False):
+    def __init__(self,path,suffix='ini',refetch_stations=True):
 
         self.path = path
 
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 8672499..6e7cd2f 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -51,7 +51,7 @@
 os.system('module load Ruby')
 
 class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
         """ creates an interactive interface for analysing class4gl experiments
 
         INPUT:
@@ -117,48 +117,50 @@ def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetc
                                            refetch_records=refetch_records
                                            )
 
-        # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_obs_afternoon'] =\
-                        get_records(self.frames['stats']['stations'].table,\
-                                           self.path_obs,\
-                                           subset='afternoon',\
-                                           refetch_records=refetch_records
-                                           )
+        if self.path_obs is not None:
+            # get its records and load it into the stats frame
+            self.frames['stats']['records_all_stations_obs_afternoon'] =\
+                            get_records(self.frames['stats']['stations'].table,\
+                                               self.path_obs,\
+                                               subset='afternoon',\
+                                               refetch_records=refetch_records
+                                               )
 
         self.frames['stats']['records_all_stations_mod'].index = \
             self.frames['stats']['records_all_stations_ini'].index 
 
         self.frames['stats']['records_all_stations_ini']['dates'] = \
-            self.frames['stats']['records_all_stations_ini'].ldatetime.dt.date
+            self.frames['stats']['records_all_stations_ini']['ldatetime'].dt.date
 
-        self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].ldatetime.dt.date
+        if self.path_obs is not None:
+            self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
+                self.frames['stats']['records_all_stations_obs_afternoon']['ldatetime'].dt.date
 
-        self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+            self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
 
 
-        ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
+            ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
 
-        self.frames['stats']['records_all_stations_obs_afternoon'] = \
-            self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+            self.frames['stats']['records_all_stations_obs_afternoon'] = \
+                self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
 
-        self.frames['stats']['records_all_stations_obs_afternoon'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
+            self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+                self.frames['stats']['records_all_stations_ini'].index 
 
-        self.frames['stats']['viewkeys'] = ['h','theta','q']
-        print('Calculating table statistics')
-        self.frames['stats']['records_all_stations_mod_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_mod'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
-        self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_obs_afternoon'],\
-                           self.frames['stats']['records_all_stations_ini'],\
-                           self.frames['stats']['viewkeys']\
-                          )
+            self.frames['stats']['viewkeys'] = ['h','theta','q']
+            print('Calculating table statistics')
+            self.frames['stats']['records_all_stations_mod_stats'] = \
+                    tendencies(self.frames['stats']['records_all_stations_mod'],\
+                               self.frames['stats']['records_all_stations_obs_afternoon'],\
+                               self.frames['stats']['records_all_stations_ini'],\
+                               self.frames['stats']['viewkeys']\
+                              )
+            self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                    tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                               self.frames['stats']['records_all_stations_obs_afternoon'],\
+                               self.frames['stats']['records_all_stations_ini'],\
+                               self.frames['stats']['viewkeys']\
+                              )
 
         self.frames['stats']['inputkeys'] = inputkeys
         
@@ -204,21 +206,25 @@ def __init__(self,path_exp,path_obs,globaldata=None,refetch_records=False,refetc
         #               obs = self.frames['stats']['records_all_stations_ini'], \
         #               columns = self.frames['stats']['viewkeys'], \
         #              )
-        indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+
         
-        print('filtering pathological data')
-        # some observational sounding still seem problematic, which needs to be
-        # investigated. In the meantime, we filter them
-        valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
-
-        # we filter ALL data frames!!!
-        for key in self.frames['stats'].keys():
-            if (type(self.frames['stats'][key]) == pd.DataFrame) and \
-               (self.frames['stats'][key].index.names == indextype):
-                self.frames['stats'][key] = self.frames['stats'][key][valid]
-        print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+
+        if self.path_obs is not None:
+            print('filtering pathological data')
+            indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+            # some observational sounding still seem problematic, which needs to be
+            # investigated. In the meantime, we filter them
+
+            if self.path_obs is not None:
+                valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
+                        ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                        ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+
+                for key in self.frames['stats'].keys():
+                    if (type(self.frames['stats'][key]) == pd.DataFrame) and \
+                       (self.frames['stats'][key].index.names == indextype):
+                        self.frames['stats'][key] = self.frames['stats'][key][valid]
+                print(str(len(valid) - np.sum(valid))+' soundings are filtered')
 
         self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
 
@@ -300,7 +306,10 @@ def update_station(self):
 
         # create the value table of the records of the current station
         tab_suffixes = \
-                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                ['_mod','_ini','_ini_pct']
+        if self.path_obs is not None:
+            tab_suffixes=tab_suffixes+['_obs_afternoon','_mod_stats','_obs_afternoon_stats']
+
         for tab_suffix in tab_suffixes:
             self.frames['stats']['records_current_station'+tab_suffix] = \
                 self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
@@ -330,8 +339,9 @@ def update_station(self):
             open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
         if 'current_station_file_afternoon' in self.frames['profiles'].keys():
             self.frames['profiles']['current_station_file_afternoon'].close()
-        self.frames['profiles']['current_station_file_afternoon'] = \
-            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+        if self.path_obs is not None:
+            self.frames['profiles']['current_station_file_afternoon'] = \
+                open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
 
         # for the profiles we make a distinct record iterator, so that the
         # stats iterator can move independently
@@ -379,22 +389,23 @@ def update_record(self):
                   (self.frames['profiles']['STNID'] , \
                   self.frames['profiles']['current_record_chunk'],\
                   self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon'] =  \
-            self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
-                  (self.frames['profiles']['STNID'] , \
-                  self.frames['profiles']['current_record_chunk'] , \
-                  self.frames['profiles']['current_record_index'])]
-
-        self.frames['profiles']['current_record_mod_stats'] = \
-                self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
-                    self.frames['profiles']['STNID'], \
-                    self.frames['profiles']['current_record_chunk'], \
-                    self.frames['profiles']['current_record_index'])]
-        self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
-                    self.frames['profiles']['STNID'],\
-                    self.frames['profiles']['current_record_chunk'],\
-                    self.frames['profiles']['current_record_index'])]
+        if self.path_obs is not None:
+            self.frames['profiles']['current_record_obs_afternoon'] =  \
+                self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+                      (self.frames['profiles']['STNID'] , \
+                      self.frames['profiles']['current_record_chunk'] , \
+                      self.frames['profiles']['current_record_index'])]
+
+            self.frames['profiles']['current_record_mod_stats'] = \
+                    self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+                        self.frames['profiles']['STNID'], \
+                        self.frames['profiles']['current_record_chunk'], \
+                        self.frames['profiles']['current_record_index'])]
+            self.frames['profiles']['current_record_obs_afternoon_stats'] = \
+                    self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+                        self.frames['profiles']['STNID'],\
+                        self.frames['profiles']['current_record_chunk'],\
+                        self.frames['profiles']['current_record_index'])]
         self.frames['profiles']['current_record_ini_pct'] = \
                 self.frames['profiles']['records_all_stations_ini_pct'].loc[(\
                     self.frames['profiles']['STNID'],\
@@ -430,17 +441,18 @@ def update_record(self):
                record_ini.index_end,
                 mode='ini')
 
-        record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
-                       (self.frames['stats']['STNID'] , \
-                        self.frames['stats']['current_record_chunk'] , \
-                        self.frames['stats']['current_record_index'])]
+        if self.path_obs is not None:
+            record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+                           (self.frames['stats']['STNID'] , \
+                            self.frames['stats']['current_record_chunk'] , \
+                            self.frames['stats']['current_record_index'])]
 
-        self.frames['profiles']['record_yaml_obs_afternoon'] = \
-           get_record_yaml(
-               self.frames['profiles']['current_station_file_afternoon'], \
-               record_afternoon.index_start,
-               record_afternoon.index_end,
-                mode='ini')
+            self.frames['profiles']['record_yaml_obs_afternoon'] = \
+               get_record_yaml(
+                   self.frames['profiles']['current_station_file_afternoon'], \
+                   record_afternoon.index_start,
+                   record_afternoon.index_end,
+                    mode='ini')
 
 
         key = self.frames['worldmap']['inputkey']
@@ -481,20 +493,22 @@ def plot(self):
         btns = {} #buttons
 
         # frames, which sets attributes for a group of axes, buttens, 
-        for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
-            label = 'stats_'+str(key)
-            axes[label] = fig.add_subplot(\
-                            len(self.frames['stats']['viewkeys']),\
-                            5,\
-                            5*ikey+1,label=label)
-            # Actually, the axes should be a part of the frame!
-            #self.frames['stats']['axes'] = axes[
+        if self.path_obs is not None:
+
+            for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+                label = 'stats_'+str(key)
+                axes[label] = fig.add_subplot(\
+                                len(self.frames['stats']['viewkeys']),\
+                                5,\
+                                5*ikey+1,label=label)
+                # Actually, the axes should be a part of the frame!
+                #self.frames['stats']['axes'] = axes[
 
-            # pointer to the axes' point data
-            axes[label].data = {}
+                # pointer to the axes' point data
+                axes[label].data = {}
 
-            # pointer to the axes' color fields
-            axes[label].fields = {}
+                # pointer to the axes' color fields
+                axes[label].fields = {}
 
 
         fig.tight_layout()
@@ -906,7 +920,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
             # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
 
-        if (only is None) or ('stats' in only) or ('stats_lightupdate' in only):
+        if (self.path_obs is not None) and \
+           ((only is None) or ('stats' in only) or ('stats_lightupdate' in only)):
 
             statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
             store_xlim = {}
@@ -1192,23 +1207,21 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
             #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
             hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                          ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
-            #print('r13')
-            # 
+                           self.frames['profiles']['record_yaml_mod'].out.h[-1]])
+            if self.path_obs is not None:
+                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_obs_afternoon'].pars.h])
 
 
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
+                                    < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+                zco = range(zidxmax)
 
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
+                axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                                 label="obs "+\
+                                 self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
             #print('r14')
             zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values
                                 < 2.*hmax))[0][-1])+2
@@ -1223,29 +1236,30 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
 
             #print('r15')
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
-                                < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
+            if self.path_obs is not None:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+                                    < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+                zco = range(zidxmax)
 
-                          
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
+                              
+                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                                 label="obs "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
 
-            #print('r16')
+                #print('r16')
 
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+                zco = range(zidxmax)
 
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
+                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                                 label="fit "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
 
             #print('r17')
             print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
@@ -1260,7 +1274,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                 axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
                                  self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
                                  label="mod "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 (self.frames['profiles']['record_yaml_ini'].pars.ldatetime
+                                 +dt.timedelta(seconds=self.frames['profiles']['record_yaml_ini'].pars.runtime)).strftime("%H:%M")\
                                  +'LT')
 
             #print('r18')
@@ -1272,7 +1287,11 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             axes[label].clear()
 
             tbox['datetime'].set_text(\
-                self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M")) #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
+                 (self.frames['profiles']['record_yaml_ini'].pars.datetime_daylight+\
+                  dt.timedelta(seconds=self.frames['profiles']['record_yaml_ini'].pars.runtime)).strftime("%Y/%m/%d %H:%M")
+                )
+            
+            #+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M")+ "UTC")
             # 
 
             #print('r19')
@@ -1280,27 +1299,26 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
             # 
             if valid_mod:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+                hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
+                               self.frames['profiles']['record_yaml_mod'].out.h[-1]])
             else:
-                hmax = np.max([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_obs_afternoon'].pars.h,
-                              ])#self.morning_sounding.c4gl.out.h[-1],self.evening_sounding.fit.PARAMS.T.h.value])
+                hmax = self.frames['profiles']['record_yaml_ini'].pars.h
+
+            if self.path_obs is not None:
+                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_obs_afternoon'].pars.h])
             # 
             #print('r20')
 
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
-            zco = range(zidxmax)
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_ini'].air_balloon.z.values)))
+                zco = range(zidxmax)
 
-            axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
-            #print('r21')
+                axes[label].plot(self.frames['profiles']['record_yaml_ini'].air_balloon.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_ini'].air_balloon.z.values[zco],"b*", \
+                                 label="obs "+\
+                                 self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
+                #print('r21')
 
 
             zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_ap.z.values < 2.*hmax))[0][-1])+2
@@ -1313,27 +1331,28 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                              self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
                              +'LT')
 
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
-            zco = range(zidxmax)
+            if self.path_obs is not None:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+                zco = range(zidxmax)
 
 
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
-                             label="obs "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
+                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                                 label="obs "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
 
-            zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-            zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
-            zco = range(zidxmax)
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+                zco = range(zidxmax)
 
-            #print('r23')
-            axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                             self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
-                             label="fit "+\
-                             self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
-                             +'LT')
+                #print('r23')
+                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                                 label="fit "+\
+                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 +'LT')
 
             #print('r24')
             if valid_mod:
@@ -1550,10 +1569,11 @@ def on_pick(self,event):
                         self.frames['profiles']['current_station_file_mod'].close()
                     self.frames['profiles']['current_station_file_mod'] = \
                         open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_afternoon'].close()
-                    self.frames['profiles']['current_station_file_afternoon'] = \
-                        open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+                    if self.path_obs is not None:
+                        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                            self.frames['profiles']['current_station_file_afternoon'].close()
+                        self.frames['profiles']['current_station_file_afternoon'] = \
+                            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
 
                     # go to hovered record of current station
                     self.frames['profiles']['records_iterator'] = \
@@ -1642,22 +1662,22 @@ def on_plot_hover(self,event):
                         # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
                         # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
                         
-
-                        if label[:5] == 'stats':
-                            records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                            (self.frames['stats']['STNID'] ,
-                             self.frames['stats']['current_record_chunk'], 
-                             self.frames['stats']['current_record_index']) = \
-                                records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
-                        # elif label[:5] == 'stats':
-                        #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                        #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
-                        #     records_datetimes = self.frames['stats']['records_all_stations_ini']
-                        #     (self.frames['stats']['STNID'] ,
-                        #      self.frames['stats']['current_record_chunk'], 
-                        #      self.frames['stats']['current_record_index']) = \
-                        #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                        if self.path_obs is not None:
+                            if label[:5] == 'stats':
+                                records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                                records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                                (self.frames['stats']['STNID'] ,
+                                 self.frames['stats']['current_record_chunk'], 
+                                 self.frames['stats']['current_record_index']) = \
+                                    records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                            # elif label[:5] == 'stats':
+                            #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
+                            #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            #     records_datetimes = self.frames['stats']['records_all_stations_ini']
+                            #     (self.frames['stats']['STNID'] ,
+                            #      self.frames['stats']['current_record_chunk'], 
+                            #      self.frames['stats']['current_record_index']) = \
+                            #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
 
 
                         self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
@@ -1699,7 +1719,11 @@ def on_plot_hover(self,event):
 
 
                         tab_suffixes = \
-                                ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                                ['_mod','_ini','_ini_pct']
+                        if self.path_obs is not None:
+                            tab_suffixes += \
+                                ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                            
                         for tab_suffix in tab_suffixes:
                             self.frames['stats']['records_current_station'+tab_suffix] = \
                                 self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
@@ -1735,7 +1759,10 @@ def on_plot_hover(self,event):
 
                         #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
                         tab_suffixes = \
-                                ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                                ['_ini','_ini_pct']
+                        if self.path_obs is not None:
+                            tab_suffixes += \
+                                ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
                         for tab_suffix in tab_suffixes:
                             #print(tab_suffix)
                             #print(self.frames['stats']['records_current_station'+tab_suffix])
@@ -1833,9 +1860,12 @@ def on_plot_hover(self,event):
                                      == \
                                      self.frames['stats']['current_station'].name)
 
-
                             tab_suffixes = \
-                                    ['_mod','_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                                    ['_mod','_ini','_ini_pct']
+                            if self.path_obs is not None:
+                                tab_suffixes += \
+                                    ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+
                             for tab_suffix in tab_suffixes:
                                 self.frames['stats']['records_current_station'+tab_suffix] = \
                                     self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
@@ -1858,13 +1888,12 @@ def on_plot_hover(self,event):
                             self.frames['stats']['current_record_mod'] = \
                                 self.frames['stats']['records_iterator'].__next__()
                         
-
-
-
-                            #print('h10')
-                            # cash the current record
                             tab_suffixes = \
-                                    ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                                    ['_ini','_ini_pct']
+                            if self.path_obs is not None:
+                                tab_suffixes += \
+                                    ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+
                             for tab_suffix in tab_suffixes:
                                 self.frames['stats']['current_record'+tab_suffix] =  \
                                     self.frames['stats']['records_current_station'+tab_suffix].loc[\
@@ -1959,18 +1988,18 @@ def on_plot_hover(self,event):
                          == \
                          self.frames['stats']['current_station'].name)
 
-
+                
                 tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
+                        ['_ini','_ini_pct']
+                if self.path_obs is not None:
+                    tab_suffixes += \
+                        ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+
                 for tab_suffix in tab_suffixes:
                     self.frames['stats']['records_current_station'+tab_suffix] = \
                         self.frames['stats']['records_all_stations'+tab_suffix].iloc[self.frames['stats']['records_current_station_index']]
 
-                
 
-                # cash the records of the current stations
-                tab_suffixes = \
-                        ['_ini','_obs_afternoon','_mod_stats','_obs_afternoon_stats','_ini_pct']
                 for tab_suffix in tab_suffixes:
                     self.frames['stats']['current_record'+tab_suffix] =  \
                         self.frames['stats']['records_current_station'+tab_suffix].loc[\
diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
new file mode 100644
index 0000000..7f259ad
--- /dev/null
+++ b/class4gl/setup/setup_era.py
@@ -0,0 +1,270 @@
+
+
+# -*- coding: utf-8 -*-
+
+import logging
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import importlib
+spam_loader = importlib.find_loader('Pysolar')
+found = spam_loader is not None
+if found:
+    import Pysolar
+    import Pysolar.util.GetSunriseSunset
+else:
+    import pysolar as Pysolar
+    GetSunriseSunset =  Pysolar.util.get_sunrise_sunset
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_YYYYMMDD',default="19810101")
+parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--latitude') # run a specific station id
+parser.add_argument('--longitude') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+parser.add_argument('--subset_experiments',default='ini') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+EXP_DEFS  =\
+{
+  'ERA-INTERIM_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+# iniitialize global data
+# ===============================
+print("Initializing global data")
+# ===============================
+globaldata = data_global()
+globaldata.sources = {**globaldata.sources,**{
+    
+        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_19830609-19830808_6hourly.nc",
+        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830609-19830808_6hourly.nc",
+        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_19830609-19830808_6hourly.nc",
+        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_19830609-19830808_6hourly.nc",
+    
+#        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
+ #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
+ #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",
+ #       "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v*_6hourly.nc",
+        }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=True)
+
+
+# # ===============================
+# print("Selecting station by ID")
+# # ===============================
+# stations_iter = stations_iterator(all_stations)
+# STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+# all_stations_select = pd.DataFrame([run_station])
+# print(run_station)
+
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if (args.latitude is not None) or (args.longitude is not None):
+    print('custom coordinates not implemented yet, please ask developer.')
+elif args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+#     print("making a custom station according to the coordinates")
+# 
+#     STNID = 43.23
+else:
+     print("Selecting stations from a row range in the table")
+     all_stations_select = pd.DataFrame(all_stations.table)
+     if args.last_station_row is not None:
+         all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+     if args.first_station_row is not None:
+         all_stations_select = all_station_select.iloc[int(args.first_station):]
+
+
+
+
+
+
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+
+dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d",)
+dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d",)
+# ===============================
+print("Creating daily timeseries from", dtfirst," to ", dtlast)
+# ===============================
+DTS = [dtfirst + dt.timedelta(days=iday) for iday in \
+       range(int((dtlast + dt.timedelta(days=1) -
+                  dtfirst).total_seconds()/3600./24.))]
+
+if args.split_by != -1:
+    totalchunks = len(all_stations_select)*len(DTS)/int(args.split_by)
+else:
+    totalchunks = len(all_stations_select)
+
+
+if args.global_chunk_number is not None:
+    run_station_chunk = np.mod(int(args.global_chunk_number),len(DTS)/int(args.split_by))
+else:
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+
+DTS_chunk = DTS[(int(run_station_chunk)*int(args.split_by)):\
+                 (int(run_station_chunk)+1)*int(args.split_by)]
+
+# for the current implementation we only consider one station. Let's upgrade it
+# later for more stations.
+run_station_chunk = int(args.global_chunk_number)
+
+# ===============================
+print('start looping over chunk')
+# ===============================
+
+os.system('mkdir -p '+args.path_experiments)
+
+
+fn_ini = args.path_experiments+'/'+format(run_station.name,'05d')+'_'+\
+        str(int(run_station_chunk))+'_'+args.subset_experiments+'.yaml'
+file_ini = open(fn_ini,'w');print('Writing to: ',fn_ini)
+
+for iDT,DT in enumerate(DTS_chunk):
+    print(iDT,DT)
+    c4gli = class4gl_input(debug_level=logging.INFO)
+    c4gli.update(source='STNID'+format(STNID,'05d'),\
+                 pars=dict(latitude = float(run_station.latitude), \
+                           longitude = float(run_station.longitude),\
+                           STNID=int(STNID)))
+
+    lSunrise, lSunset = GetSunriseSunset(c4gli.pars.latitude,0.,DT)
+
+    #start simulation at sunrise and stop at one hour before sunset
+    runtime = (lSunset - lSunrise).total_seconds() - 3600.*1.
+    ldatetime = lSunrise
+    datetime = ldatetime - dt.timedelta(hours=c4gli.pars.longitude/360.*24.)
+    datetime_daylight = datetime
+    c4gli.update(source='timeseries',   \
+                 pars=dict(\
+                           lSunrise = lSunrise, \
+                           lSunset = lSunset, \
+                           datetime = datetime, \
+                           ldatetime = ldatetime, \
+                           ldatetime_daylight = ldatetime, \
+                           datetime_daylight = datetime, \
+                           doy = datetime.timetuple().tm_yday,\
+                           runtime = runtime,\
+                          ))
+
+    c4gli.get_global_input(globaldata)
+
+    c4gli.update(source='era-interim',pars={'Ps' : c4gli.pars.sp})
+
+    cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+    Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+    Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+    R = (Rd*(1.-c4gli.air_ac.q) + Rv*c4gli.air_ac.q)
+    rho = c4gli.air_ac.p/R/c4gli.air_ac.t
+    dz = c4gli.air_ac.delpdgrav/rho
+    z = [dz.iloc[-1]/2.]
+    for idz in list(reversed(range(0,len(dz)-1,1))):
+        z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
+    z = list(reversed(z))
+
+    theta = c4gli.air_ac.t * \
+               (c4gli.pars.sp/(c4gli.air_ac.p))**(R/cp)
+    thetav   = theta*(1. + 0.61 * c4gli.air_ac.q)
+
+    
+    c4gli.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
+                                                           'theta':list(theta),
+                                                           'thetav':list(thetav),
+                                                          }))
+    air_ap_input = c4gli.air_ac[::-1].reset_index().drop('index',axis=1)
+    air_ap_mode = 'b'
+    air_ap_input_source = c4gli.query_source('air_ac:theta')
+
+
+    c4gli.mixed_layer_fit(air_ap=air_ap_input,
+                         source=air_ap_input_source,
+                         mode=air_ap_mode)
+
+
+    c4gli.dump(file_ini)
+
+file_ini.close()
+all_records_morning = get_records(pd.DataFrame([run_station]),\
+                              args.path_experiments,\
+                              getchunk = int(run_station_chunk),\
+                              subset=args.subset_experiments,
+                              refetch_records=True,
+                              )
+
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index e10c1da..c9e31ff 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -58,6 +58,7 @@
 
 EXP_DEFS  =\
 {
+  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
@@ -76,14 +77,22 @@
 # #SET = 'GLOBAL'
 # SET = args.dataset
 
+# ========================
+print("getting a list of stations")
+# ========================
 
-print("getting stations")
 # these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=True)
 
+# ====================================
 print('defining all_stations_select')
+# ====================================
+
 # these are all the stations that are supposed to run by the whole batch (all
 # chunks). We narrow it down according to the station(s) specified.
+
+
+
 if args.station_id is not None:
     print("Selecting station by ID")
     stations_iter = stations_iterator(all_stations)
@@ -99,6 +108,7 @@
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
+print(all_stations_select)
 print("getting all records of the whole batch")
 all_records_morning_select = get_records(all_stations_select,\
                                          args.path_forcing,\
@@ -181,8 +191,8 @@
     print(len(records_morning))
 
     print("aligning morning and afternoon records")
-    records_morning['dates'] = records_morning.ldatetime.dt.date
-    records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
     records_afternoon.set_index(['STNID','dates'],inplace=True)
     ini_index_dates = records_morning.set_index(['STNID','dates']).index
     records_afternoon = records_afternoon.loc[ini_index_dates]
@@ -200,8 +210,17 @@
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
+            else:
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
+
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -239,6 +258,8 @@
                                                         mode='ini')
                         runtime = int((c4gli_afternoon.pars.datetime_daylight - 
                                              c4gli_morning.pars.datetime_daylight).total_seconds())
+                    elif args.runtime == 'from_input':
+                        runtime = c4gli_morning.pars.runtime
                     else:
                         runtime = int(args.runtime)
 
@@ -287,7 +308,8 @@
             file_ini.close()
             file_mod.close()
             file_morning.close()
-            file_afternoon.close()
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon.close()
     
             if onerun:
                 records_ini = get_records(pd.DataFrame([current_station]),\

From 1aebcb674065f751416626da8c6e1f6bbd4a36ba Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 8 Sep 2018 09:32:04 +0200
Subject: [PATCH 068/129] fix permission problbem when getting station list

---
 class4gl/setup/setup_era.py         | 2 +-
 class4gl/simulations/simulations.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 7f259ad..9d5a6f0 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -108,7 +108,7 @@
 # ===============================
 print("getting a list of stations")
 # ===============================
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=True)
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
 
 # # ===============================
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index c9e31ff..114d23a 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -82,7 +82,7 @@
 # ========================
 
 # these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=True)
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
 # ====================================
 print('defining all_stations_select')

From 10531915ec9389c1d5e776a98f57ef39fd57482a Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 12 Sep 2018 15:08:56 +0200
Subject: [PATCH 069/129] make setup_global work

---
 class4gl/class4gl.py               |  29 +-
 class4gl/data_air.py               | 482 -----------------------------
 class4gl/data_soundings.py         | 453 +++++++++++++++++++++++++++
 class4gl/setup/batch_setup_era.pbs |  30 ++
 class4gl/setup/batch_setup_era.py  | 188 +++++++++++
 class4gl/setup/setup_era.py        |   6 -
 class4gl/setup/setup_global.py     |  84 +++--
 7 files changed, 745 insertions(+), 527 deletions(-)
 delete mode 100644 class4gl/data_air.py
 create mode 100644 class4gl/data_soundings.py
 create mode 100644 class4gl/setup/batch_setup_era.pbs
 create mode 100644 class4gl/setup/batch_setup_era.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 9b5c8b9..805ac35 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -41,8 +41,16 @@
 found = spam_loader is not None
 if found:
     import Pysolar
+    import Pysolar.util as Pysolarutil
+    GetSunriseSunset = Pysolarutil.GetSunriseSunset
+    GetAzimuth = Pysolarutil.GetAzimuth
+    GetAltitude = Pysolarutil.GetAltitude
 else:
     import pysolar as Pysolar
+    Pysolarutil = Pysolar.util
+    GetSunriseSunset = Pysolarutil.get_sunrise_sunset
+    GetAzimuth = Pysolar.solar.get_azimuth
+    GetAltitude = Pysolar.solar.get_altitude
 import yaml
 import logging
 import warnings
@@ -621,9 +629,6 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         if np.isnan(dpars['h']):
             dpars['Ps'] = np.nan
 
-
-
-
         if ~np.isnan(dpars['h']):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
@@ -652,9 +657,6 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
             dpars['u'] = np.nan
             dpars['v'] = np.nan
             
-
-
-
         # First 3 data points of the mixed-layer fit. We create a empty head
         # first
         air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
@@ -761,22 +763,25 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
                             dt.timedelta(minutes=int(dpars['longitude']/360.*24.*60.))
         dpars['doy'] = dpars['datetime'].timetuple().tm_yday
         dpars['SolarAltitude'] = \
-                                Pysolar.GetAltitude(\
+                                GetAltitude(\
                                     dpars['latitude'],\
                                     dpars['longitude'],\
                                     dpars['datetime']\
                                 )
-        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+        dpars['SolarAzimuth'] =  GetAzimuth(\
                                     dpars['latitude'],\
                                     dpars['longitude'],\
                                     dpars['datetime']\
                                 )
         dpars['lSunrise'], dpars['lSunset'] \
-        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+        =  GetSunriseSunset(dpars['latitude'],
                                          0.,
-                                         dpars['ldatetime'],0.)
-        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
-        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+                                         dpars['ldatetime'])
+        #print(dpars['lSunrise'])
+        dpars['lSunrise'] = dpars['lSunrise']
+        dpars['lSunset'] = dpars['lSunset']
+        # dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        # dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
         # This is the nearest datetime when the sun is up (for class)
         dpars['ldatetime_daylight'] = \
                                 np.min(\
diff --git a/class4gl/data_air.py b/class4gl/data_air.py
deleted file mode 100644
index 3860319..0000000
--- a/class4gl/data_air.py
+++ /dev/null
@@ -1,482 +0,0 @@
-import numpy as np
-
-from bs4 import BeautifulSoup
-import pandas as pd
-import datetime as dt
-#import pylab as pl
-import io
-import os
-import calendar
-
-import importlib
-spam_loader = importlib.find_loader('Pysolar')
-found = spam_loader is not None
-if found:
-    import Pysolar
-else:
-    import pysolar as Pysolar
-import Pysolar.util as Pysolarutil
-
-
-
-
-
-#from urllib import request
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / WSPD**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHd = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHd = np.min([BLHd,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHd = np.min([BLHd,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHd = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHd
-
-def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
-    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
-    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
-
-
-#from os import listdir
-#from os.path import isfile #,join
-import glob
-
-
-class wyoming(object):
-    def __init__(self):
-       self.status = 'init'
-       self.found = False
-       self.DT = None
-       self.current = None
-       #self.mode = 'b'
-       self.profile_type = 'wyoming'  
-       self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
-       self.PATH = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-         
-    def set_STNM(self,STNM):
-        self.__init__()
-        self.STNM = STNM
-        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
-        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
-        self.current = None
-        self.found = False
-        self.FILES.sort()
-        
-    def find_first(self,year=None,get_atm=False):
-        self.found = False    
-                
-        # check first file/year or specified year
-        if year == None:
-            self.iFN = 0
-            self.FN = self.FILES[self.iFN]
-        else:
-            self.FN = os.path.realpath(self.PATH+'/'+str(year)+'/SOUNDINGS_'+str(year)+'_'+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-        self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-        self.current = self.sounding_series.find('h2')
-        keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
-        
-        # go through other files and find first sounding when year is not specified
-        self.iFN=self.iFN+1
-        while keepsearching:
-            self.FN = self.FILES[self.iFN]
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            self.iFN=self.iFN+1
-            keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
-        self.found = (self.current is not None)
-
-        self.status = 'fetch'
-        if self.found:
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        
-        if self.found and get_atm:
-            self.get_values_air_input()
-        
-    
-    def find(self,DT,get_atm=False):
-        
-        self.found = False
-        keepsearching = True
-        #print(DT)
-        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
-        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
-            self.DT = DT
-            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
-            self.iFN = self.FILES.index(self.FN)
-            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-            self.current = self.sounding_series.find('h2')
-            
-        keepsearching = (self.current is not None)
-        while keepsearching:
-            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            if DTcurrent == DT:
-                self.found = True
-                keepsearching = False
-                if get_atm:
-                    self.get_values_air_input()
-                    self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-            elif DTcurrent > DT:
-                keepsearching = False
-                self.current = None
-            else:
-                self.current = self.current.find_next('h2')
-                if self.current is None:
-                    keepsearching = False
-        self.found = (self.current is not None)
-        self.status = 'fetch'
-
-    def find_next(self,get_atm=False):
-        self.found = False
-        self.DT = None
-        if self.current is None:
-            self.find_first()
-        else:                
-            self.current = self.current.find_next('h2')
-            self.found = (self.current is not None)
-            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
-            while keepsearching:
-                self.iFN=self.iFN+1
-                self.FN = self.FILES[self.iFN]
-                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
-                self.current = self.sounding_series.find('h2')
-                
-                self.found = (self.current is not None)
-                keepsearching = ((self.current is None) and (self.iFN < len(self.FILES)))
-        if self.found:        
-            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
-        if self.found and get_atm:
-            self.get_values_air_input()
-       
-
-
-    def get_values_air_input(self,latitude=None,longitude=None):
-
-        # for iDT,DT in enumerate(DTS):
-        
-            #websource = urllib.request.urlopen(webpage)
-        #soup = BeautifulSoup(open(webpage), "html.parser")
-        
-       
-        #workaround for ...last line has 
 which results in stringlike first column
-        string = self.current.find_next('pre').text
-        string = string.split('\n')[:-1]
-        string =  '\n'.join(string)
-        columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        ONE_COLUMN = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns).iloc[5:-1]
-        #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
-        
-        #string =  soup.pre.next_sibling.next_sibling
-        
-        string = self.current.find_next('pre').find_next('pre').text
-
-        PARAMS = pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T
-        #PARAMS.insert(0,'date',DT)
-
-        PARAMS.insert(0,'datetime', dt.datetime.strptime(str(PARAMS['Observation time'][0]),"%y%m%d/%H%M"))
-        PARAMS.insert(0,'STNID',int(PARAMS['Station number']))
-        
-        THTV = np.array(ONE_COLUMN.THTV,dtype='float')
-        #THTA = np.array(ONE_COLUMN.THTA,dtype='float')
-        HGHT = np.array(ONE_COLUMN.HGHT,dtype='float')
-        HAGL = HGHT - np.float(PARAMS['Station elevation'])
-        ONE_COLUMN.insert(0,'HAGL',HAGL)
-
-        
-        
-        
-        MIXR = np.array(ONE_COLUMN.MIXR,dtype='float')
-        QABS = (MIXR/1000.)/(MIXR/1000.+1.)
-        ONE_COLUMN.insert(0,'QABS',QABS)
-        
-        WSPD =0.51444 * np.array(ONE_COLUMN.SKNT,dtype='float')
-
-        #mixed layer potential temperature
-        #THTVM = np.float(VALUE['Mean mixed layer potential temperature'].iloc[0])
-
-        #THTV_0 = THTA[np.where(~np.isnan(THTA))[0][0]]
-        #RiBV = 9.81/THTV_0 * ( THTV - THTV_0) * HGHT / WSPD**2
-        #RiBA = 9.81/THTA_0 * ( THTA - THTA_0) * HGHT / WSPD**2
-
-        BLHV,BLHVu,BLHVd = blh(HAGL,THTV,WSPD)
-        BLHV = np.max((BLHV,10.))
-        BLHVu = np.max((BLHVu,10.))
-        BLHVd = np.max((BLHVd,10.))
-        #BLHA,BLHAu,BLHAd = BLH(HGHT,THTA,WSPD)
-
-        #security values for mixed-layer jump values dthetav, dtheta and dq
-        
-        # fit new profiles taking the above-estimated mixed-layer height
-        ONE_COLUMNNEW = []
-        for BLH in [BLHV,BLHVu,BLHVd]:
-            ONE_COLUMNNEW.append(pd.DataFrame())
-            
-            HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
-            ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
-            
-            listHAGLNEW = list(HAGLNEW)
-            for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
-                
-                # get index of lowest valid observation. This seems to vary
-                idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
-                if len(idxvalid) > 0:
-                    #print('idxvalid',idxvalid)
-                    if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
-                        meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
-                    else:
-                        meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
-                else:
-                    meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
-                    #print(col,meanabl)
-               
-                
-                # if col == 'PRES':
-                #     meanabl =  
-            
-                new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
-                #THTVM = np.nanmean(THTV[HAGL <= BLH])
-                #print("new_pro_h",new_pro_h)
-                # calculate jump ath the top of the mixed layer
-                if col in ['THTA','THTV',]:
-                    #for moisture
-                    #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
-                    #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
-                    if len(listHAGLNEW) > 4:
-                        #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
-                        dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
-                        dtheta = np.max((0.1,dtheta_pre))
-                        #meanabl = meanabl - (dtheta - dtheta_pre)
-                        #print('dtheta_pre',dtheta_pre)
-                        #print('dtheta',dtheta)
-                        #print('meanabl',meanabl)
-                        #stop
-                        
-                    else:
-                        dtheta = np.nan
-                else:
-                    if len(listHAGLNEW) > 4:
-                        #for moisture (it can have both negative and positive slope)
-                        dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
-                    else:
-                        dtheta = np.nan
-                #print('dtheta',dtheta)
-                
-                new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
-            
-                
-                ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
-                
-            #QABSM = np.nanmean(QABS[HAGL <= BLH])
-            #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
-            #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
-            
-        # we just make a copy of the fields, so that it can be read correctly by CLASS 
-        for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
-            dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
-            dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
-            dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
-            
-            angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
-            spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
-        
-            dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
-            dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
-
-
-        # assign fields adopted by CLASS
-        if self.mode == 'o': #original 
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'b':
-            PARAMS.insert(0,'h',   np.float(BLHV))
-        elif self.mode == 'u':
-            PARAMS.insert(0,'h',   BLHVu)
-        elif self.mode == 'd':
-            PARAMS.insert(0,'h',   BLHVd)
-        else:
-            PARAMS.insert(0,'h',   BLHV)
-            
-
-        try:
-            PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
-            PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
-        except:
-            print("could not convert latitude coordinate")
-            PARAMS.insert(0,'latitude', np.nan)
-            PARAMS.insert(0,'lat', np.nan)
-        try:
-            PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
-            # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
-            PARAMS.insert(0,'lon', 0.)
-        except:
-            print("could not convert longitude coordinate")
-            PARAMS.insert(0,'longitude', np.nan)
-            PARAMS.insert(0,'lon', 0.)
-
-        if latitude is not None:
-            print('overwriting latitude with specified value')
-            PARAMS['latitude'] = np.float(latitude)
-            PARAMS['lat'] = np.float(latitude)
-        if longitude is not None:
-            print('overwriting longitude with specified value')
-            PARAMS['longitude'] = np.float(longitude)
-        try:
-            #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
-            PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
-            PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolarutil.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            # This is the nearest datetime when sun is up (for class)
-            PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
-            # apply the same time shift for UTC datetime
-            PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
-            
-        except:
-            print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
-            PARAMS['ldatetime'] = dt.datetime(1900,1,1)
-            PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
-            PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolarutil.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
-            PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
-            PARAMS['datetime_daylight'] =PARAMS['datetime'].value
-
-        
-
-        PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
-        # as we are forcing lon equal to zero this is is expressed in local suntime
-        PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
-
-           
-        ONE_COLUMNb = ONE_COLUMNNEW[0]
-        ONE_COLUMNu = ONE_COLUMNNEW[1]
-        ONE_COLUMNd = ONE_COLUMNNEW[2]
-        
-
-        THTVM = np.nanmean(THTV[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
-        
-        QABSM = np.nanmean(QABS[HAGL <= BLHV])
-        PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHV',BLHV)
-        PARAMS.insert(len(PARAMS.columns),'BLHVu',BLHVu)
-        PARAMS.insert(len(PARAMS.columns),'BLHVd',BLHVd)  
-
-        BLHVe = abs(BLHV - BLHVu)
-        BLHVe = max(BLHVe,abs(BLHV - BLHVd))
-
-        #PARAMS.insert(0,'dq',0.)
-        
-        PARAMS.insert(len(PARAMS.columns),'BLHVe',BLHVe)  
-        PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
-        #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
-        #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
-        
-        if self.mode == 'o': #original 
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
-        elif self.mode == 'b': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNb
-            BLCOLUMN = ONE_COLUMNb
-        elif self.mode == 'u': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNu
-            BLCOLUMN = ONE_COLUMNu
-        elif self.mode == 'd': # best BLH
-            USE_ONECOLUMN = ONE_COLUMNd
-            BLCOLUMN = ONE_COLUMNd
-        else:
-            USE_ONECOLUMN = ONE_COLUMN
-            BLCOLUMN = ONE_COLUMNb
-
-        lt6000 = (BLCOLUMN['HAGL'] < 6000.)
-        lt2500 = (BLCOLUMN['HAGL'] < 2500. + BLHV)
-        # print(BLCOLUMN['HAGL'][lt6000])
-        # print(BLCOLUMN['HAGL'][lt2500])
-        # 
-        # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
-
-        #print(BLCOLUMN['HAGL'][lt2500])
-        PARAMS.insert(0,'OK',
-                      ((BLHVe < 200.) and 
-                       ( len(np.where(lt6000)[0]) > 5) and
-                       (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
-                       (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
-                       (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
-                       (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
-                       (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
-                       (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
-                      )
-                     )
-
-        PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
-        PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
-        PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
-        PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
-        PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
-        PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
-        
-        
-        PARAMS = PARAMS.T
-
-        
-        self.PARAMS = PARAMS
-        self.ONE_COLUMN = USE_ONECOLUMN
-        # if self.mode == 'o': #original 
-        #     self.ONE_COLUMN = ONE_COLUMN
-        # elif self.mode == 'b': # best BLH
-        #     self.ONE_COLUMN = ONE_COLUMNb
-        # elif self.mode == 'u':# upper BLH
-        #     self.ONE_COLUMN = ONE_COLUMNu
-        # elif self.mode == 'd': # lower BLH
-        #     self.ONE_COLUMN=ONE_COLUMNd
-        # else:
-        #     self.ONE_COLUMN = ONE_COLUMN
-
diff --git a/class4gl/data_soundings.py b/class4gl/data_soundings.py
new file mode 100644
index 0000000..8e438b4
--- /dev/null
+++ b/class4gl/data_soundings.py
@@ -0,0 +1,453 @@
+import numpy as np
+
+from bs4 import BeautifulSoup
+import pandas as pd
+import datetime as dt
+#import pylab as pl
+import io
+import os
+import calendar
+import warnings
+
+
+
+def dtrange(STARTTIME,ENDTIME,TIMEJUMP=dt.timedelta(hours=24)):
+    STEPS = int((ENDTIME - STARTTIME).total_seconds()/TIMEJUMP.total_seconds())
+    return [STARTTIME + TIMEJUMP*i for i in range(0,STEPS)]
+
+
+#from os import listdir
+#from os.path import isfile #,join
+import glob
+
+
+class wyoming(object):
+    def __init__(self, PATH="/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/",STNM=None):
+
+        self.profile_type = 'wyoming'  
+        self.MONTHS = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
+        self.PATH = PATH
+        self.reset()
+        if STNM is not None:
+            self.set_STNM(STNM) 
+        else:
+            warnings.warn('warning. No station is set yet. Please use class function set_STNM to set station number')
+
+    def reset(self):
+
+        self.status = 'init'
+        self.found = False
+        self.DT = None
+        self.current = None
+        #self.mode = 'b'
+
+         
+    def set_STNM(self,STNM):
+        self.reset()
+        self.STNM = STNM
+        self.FILES = glob.glob(self.PATH+'/????/SOUNDINGS_????_'+format(STNM,'05d')+".html")
+        self.FILES = [os.path.realpath(FILE) for FILE in self.FILES]
+        self.current = None
+        self.found = False
+        self.FILES.sort()
+        
+    def find_first(self,year=None,get_atm=False):
+        self.found = False    
+        self.current = None
+                
+        # check first file/year or specified year
+        if year == None:
+            self.iFN = 0
+            self.FN = self.FILES[self.iFN]
+        else:
+            # this counter cycles through all the years. We avoid an endless
+            # loop. We don't check more years than the amount of self.FILES
+            iyear = 0
+            while ((not self.found) and (iyear <  100)):
+
+                FN = os.path.realpath(self.PATH \
+                                      +'/' \
+                                      +str(year+iyear) \
+                                      +'/SOUNDINGS_' \
+                                      +str(year+iyear) \
+                                      +'_' \
+                                      +format(self.STNM,'05d') \
+                                      +".html")
+                if FN in self.FILES:
+                    self.iFN = self.FILES.index(FN)
+                    self.found = True
+                    self.FN = FN
+                else:
+                    self.iFN = -1
+                    self.FN = None
+
+                iyear += 1
+                
+        if self.found:
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            keepsearching = (self.current is None) #if we don't want later years, add here: "and (year is None)"
+            
+            # go through other files and find first sounding when year is not specified
+            self.iFN=self.iFN+1
+            while keepsearching:
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                self.iFN=self.iFN+1
+                keepsearching = (self.current is None) and (self.iFN < len(self.FILES))
+            self.found = (self.current is not None)
+
+        self.status = 'fetch'
+        if self.found:
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        
+        # if self.found and get_atm:
+        #     self.get_values_air_input()
+        
+    
+    def find(self,DT,get_atm=False):
+        
+        self.found = False
+        keepsearching = True
+        #print(DT)
+        # we open a new file only when it's needed. Otherwise we just scroll to the right sounding.  
+        if not ((self.current is not None) and (DT >= self.DT) and (self.DT.year == DT.year)):
+            self.DT = DT
+            self.FN = os.path.realpath(self.PATH+"/"+self.DT.strftime("%Y")+"/SOUNDINGS_"+self.DT.strftime("%Y")+"_"+format(self.STNM,'05d')+".html")
+            self.iFN = self.FILES.index(self.FN)
+            self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+            self.current = self.sounding_series.find('h2')
+            
+        keepsearching = (self.current is not None)
+        while keepsearching:
+            DTcurrent = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            if DTcurrent == DT:
+                self.found = True
+                keepsearching = False
+                # if get_atm:
+                #     self.get_values_air_input()
+                self.DT = DTcurrent #dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+            elif DTcurrent > DT:
+                keepsearching = False
+                self.current = None
+            else:
+                self.current = self.current.find_next('h2')
+                if self.current is None:
+                    keepsearching = False
+        self.found = (self.current is not None)
+        self.status = 'fetch'
+
+    def find_next(self,get_atm=False):
+        self.found = False
+        self.DT = None
+        if self.current is None:
+            self.find_first()
+        else:                
+            self.current = self.current.find_next('h2')
+            self.found = (self.current is not None)
+            keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+            while keepsearching:
+                self.iFN=self.iFN+1
+                self.FN = self.FILES[self.iFN]
+                self.sounding_series = BeautifulSoup(open(self.FN), "html.parser")
+                self.current = self.sounding_series.find('h2')
+                
+                self.found = (self.current is not None)
+                keepsearching = ((self.current is None) and ((self.iFN+1) < len(self.FILES)))
+        if self.found:        
+            self.DT = dt.datetime(int(self.current.text[-4:]),self.MONTHS.index(self.current.text[-8:-5])+1,int(self.current.text[-11:-9]),int(self.current.text[-15:-13]))
+        # if self.found and get_atm:
+        #     self.get_values_air_input()
+       
+
+
+
+# # should be placed under class4gl!!!
+# class sounding(object):
+#     #def statistics:
+#     # returns a list of sounding statistics as a dict
+    
+
+#def get_sounding_wyoming(self,wy_strm,latitude=None,longitude=None):
+# input:
+#   wy_strm: wyoming stream
+
+# for iDT,DT in enumerate(DTS):
+
+    #websource = urllib.request.urlopen(webpage)
+#soup = BeautifulSoup(open(webpage), "html.parser")
+
+# __init__(self):
+
+#    sounding_keys
+# list of variables that we get from global ground data
+
+#workaround for ...last line has 
 which results in stringlike first column
+
+
+
+
+
+# dtheta_pre = air
+# 
+# dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+# 
+# 
+# dtheta = np.max((0.1,dtheta_pre))
+# 
+# 
+# 
+# 
+# self.air_ap.
+
+
+
+
+# if len(valid_indices) > 0:
+#     #print('valid_indices',valid_indices)
+#     if len(np.where(HAGL[valid_indices[0]:] < BLH)[0]) >= 3:
+#         meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(valid_indices[0]+1):],dtype=np.float))
+#     else:
+#         meanabl = np.nanmean(ONE_COLUMN[col][valid_indices[0]:(valid_indices[0]+1)],dtype=np.float)                    
+# else:
+#     meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+# #
+
+
+
+
+
+
+
+# # fit new profiles taking the above-estimated mixed-layer height
+# ONE_COLUMNNEW = []
+# for BLH in [self.h,self.h_u,self.h_d]:
+#     ONE_COLUMNNEW.append(pd.DataFrame())
+#     
+#     HAGLNEW = np.array([2.,BLH,BLH]+list(HAGL[HAGL > BLH]),dtype=np.float)
+#     ONE_COLUMNNEW[-1].insert(0,'HAGL',HAGLNEW)
+#     
+#     listHAGLNEW = list(HAGLNEW)
+#     for icol,col in enumerate(['THTA','THTV','QABS','SKNT','DRCT','PRES']):
+#         
+#         # get index of lowest valid observation. This seems to vary
+#         idxvalid = np.where((np.array(HAGL) >= 0) & (~pd.isnull(np.array(ONE_COLUMN[col],dtype=np.float) )))[0]
+#         if len(idxvalid) > 0:
+#             #print('idxvalid',idxvalid)
+#             if len(np.where(HAGL[idxvalid[0]:] < BLH)[0]) >= 3:
+#                 meanabl = np.nanmean(np.array(ONE_COLUMN[col][HAGL < BLH][(idxvalid[0]+1):],dtype=np.float))
+#             else:
+#                 meanabl = np.nanmean(ONE_COLUMN[col][idxvalid[0]:(idxvalid[0]+1)],dtype=np.float)                    
+#         else:
+#             meanabl = np.nanmean(ONE_COLUMN[col][0:1],dtype=np.float)
+#             #print(col,meanabl)
+#        
+#         
+#         # if col == 'PRES':
+#         #     meanabl =  
+#     
+#         new_pro_h = list(np.array(ONE_COLUMN[col][HAGL > BLH],dtype=np.float))
+#         #THTVM = np.nanmean(THTV[HAGL <= BLH])
+#         #print("new_pro_h",new_pro_h)
+#         # calculate jump ath the top of the mixed layer
+#         if col in ['THTA','THTV',]:
+#             #for moisture
+#             #print('hello:',(new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]))
+#             #print('hello:',new_pro_h[1] , new_pro_h[0],listHAGLNEW[4] , listHAGLNEW[3],BLH,listHAGLNEW[3])
+#             if len(listHAGLNEW) > 4:
+#                 #print(type(new_pro_h[1]),type(new_pro_h[0]),type(listHAGLNEW[4]),type(listHAGLNEW[3]),type(BLH),type(meanabl))
+#                 dtheta_pre = (new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl 
+#                 dtheta = np.max((0.1,dtheta_pre))
+#                 #meanabl = meanabl - (dtheta - dtheta_pre)
+#                 #print('dtheta_pre',dtheta_pre)
+#                 #print('dtheta',dtheta)
+#                 #print('meanabl',meanabl)
+#                 #stop
+#                 
+#             else:
+#                 dtheta = np.nan
+#         else:
+#             if len(listHAGLNEW) > 4:
+#                 #for moisture (it can have both negative and positive slope)
+#                 dtheta = ((new_pro_h[1] - new_pro_h[0])/(listHAGLNEW[4] - listHAGLNEW[3])*(BLH-listHAGLNEW[3]) + new_pro_h[0] - meanabl ) 
+#             else:
+#                 dtheta = np.nan
+#         #print('dtheta',dtheta)
+#         
+#         new_pro = np.array([meanabl,meanabl,meanabl+dtheta]+new_pro_h,dtype=np.float)
+#     
+#         
+#         ONE_COLUMNNEW[-1].insert(len(ONE_COLUMNNEW[-1].columns),col,new_pro)
+#         
+#     #QABSM = np.nanmean(QABS[HAGL <= BLH])
+#     #QABSNEW = np.array([QABSM,QABSM]+list(QABS[HAGL > BLH]))
+#     #ONE_COLUMNNEW.append(pd.DataFrame(zip(HAGLNEW,THTVNEW,QABSNEW),columns=('HAGL','THTV','QABS')))
+#     
+# # we just make a copy of the fields, so that it can be read correctly by CLASS 
+# for dataonecolumn in ONE_COLUMNNEW+[ONE_COLUMN]:
+#     dataonecolumn.insert(len(dataonecolumn.columns),'p_pro',np.array(dataonecolumn.PRES,dtype=np.float)*100.)
+#     dataonecolumn.insert(len(dataonecolumn.columns),'z_pro',np.array(dataonecolumn.HAGL,dtype=np.float))
+#     dataonecolumn.insert(len(dataonecolumn.columns),'theta_pro',np.array(dataonecolumn.THTA,dtype=np.float))
+#     dataonecolumn.insert(len(dataonecolumn.columns),'thetav_pro',np.array(dataonecolumn.THTV,dtype=np.float))
+#     dataonecolumn.insert(len(dataonecolumn.columns),'q_pro',np.array(dataonecolumn.QABS,dtype=np.float))
+#     
+#     angle_x = (90.-np.array(dataonecolumn.DRCT,dtype=np.float))/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+#     spd = 0.51444* np.array(dataonecolumn.SKNT,dtype=np.float)
+# 
+#     dataonecolumn.insert(len(dataonecolumn.columns),'u_pro',spd * np.sin(angle_x))
+#     dataonecolumn.insert(len(dataonecolumn.columns),'v_pro',spd * np.cos(angle_x))
+# 
+# 
+# # assign fields adopted by CLASS
+# if self.mode == 'o': #original 
+#     PARAMS.insert(0,'h',   np.float(self.h))
+# elif self.mode == 'b':
+#     PARAMS.insert(0,'h',   np.float(self.h))
+# elif self.mode == 'u':
+#     PARAMS.insert(0,'h',   self.h_u)
+# elif self.mode == 'd':
+#     PARAMS.insert(0,'h',   self.h_d)
+# else:
+#     PARAMS.insert(0,'h',   self.h)
+#     
+# 
+# try:
+#     PARAMS.insert(0,'lat', np.float(PARAMS['Station latitude'][0]))
+#     PARAMS.insert(0,'latitude', np.float(PARAMS['Station latitude'][0]))
+# except:
+#     print("could not convert latitude coordinate")
+#     PARAMS.insert(0,'latitude', np.nan)
+#     PARAMS.insert(0,'lat', np.nan)
+# try:
+#     PARAMS.insert(0,'longitude', np.float(PARAMS['Station longitude'][0]))
+#     # we set the actual input parameter value of lon to zero as we are working in local time (as if we were in Greenwhich) 
+#     PARAMS.insert(0,'lon', 0.)
+# except:
+#     print("could not convert longitude coordinate")
+#     PARAMS.insert(0,'longitude', np.nan)
+#     PARAMS.insert(0,'lon', 0.)
+# 
+# if latitude is not None:
+#     print('overwriting latitude with specified value')
+#     PARAMS['latitude'] = np.float(latitude)
+#     PARAMS['lat'] = np.float(latitude)
+# if longitude is not None:
+#     print('overwriting longitude with specified value')
+#     PARAMS['longitude'] = np.float(longitude)
+# try:
+#     #this is the local suntime datetime from which we calculate the hour of the day (assuming we would be in greenwhich hence taking lon=0)
+#     PARAMS['ldatetime'] = PARAMS.datetime.value + dt.timedelta(hours=PARAMS.longitude.value/360.*24.) 
+#     PARAMS['SolarAltitude'] = Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+#     PARAMS['SolarAzimuth'] = Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.longitude.value,PARAMS.datetime.value)
+#     PARAMS['lSunrise'], PARAMS['lSunset'] = Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+#     # This is the nearest datetime when sun is up (for class)
+#     PARAMS['ldatetime_daylight'] = np.min(np.max(PARAMS['ldatetime'].value ,PARAMS['lSunrise'].value),PARAMS['lSunset'].value) 
+#     # apply the same time shift for UTC datetime
+#     PARAMS['datetime_daylight'] = PARAMS.datetime.value  + (PARAMS.ldatetime_daylight.value  - PARAMS.ldatetime.value)
+#     
+# except:
+#     print("could not get local times for profile, perhaps because of wrong longitude or latitude in the profile description")
+#     PARAMS['ldatetime'] = dt.datetime(1900,1,1)
+#     PARAMS['SolarAltitude'] = np.nan #Pysolar.GetAltitude(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+#     PARAMS['SolarAzimuth'] = np.nan #Pysolar.GetAzimuth(PARAMS.lat.value,PARAMS.lon.value,PARAMS.datetime.value)
+#     PARAMS['lSunrise'], PARAMS['lSunset'] = dt.datetime(1900,1,1), dt.datetime(1900,1,1) #Pysolar.util.GetSunriseSunset(PARAMS.lat.value,0.,PARAMS.datetime.value,0.)
+#     PARAMS['ldatetime_daylight'] =PARAMS['ldatetime'].value
+#     PARAMS['datetime_daylight'] =PARAMS['datetime'].value
+# 
+# 
+# 
+# PARAMS.insert(0,'day', PARAMS['ldatetime'][0].day)
+# # as we are forcing lon equal to zero this is is expressed in local suntime
+# PARAMS.insert(0,'tstart', PARAMS['ldatetime_daylight'][0].hour + PARAMS['ldatetime_daylight'][0].minute/60. + PARAMS['ldatetime_daylight'][0].second/3600.)
+# 
+#    
+# ONE_COLUMNb = ONE_COLUMNNEW[0]
+# ONE_COLUMNu = ONE_COLUMNNEW[1]
+# ONE_COLUMNd = ONE_COLUMNNEW[2]
+# 
+# 
+# THTVM = np.nanmean(THTV[HAGL <= self.h])
+# PARAMS.insert(len(PARAMS.columns),'THTVM',THTVM)
+# 
+# QABSM = np.nanmean(QABS[HAGL <= self.h])
+# PARAMS.insert(len(PARAMS.columns),'QABSM',QABSM)
+# 
+# PARAMS.insert(len(PARAMS.columns),'self.h',self.h)
+# PARAMS.insert(len(PARAMS.columns),'self.h_u',self.h_u)
+# PARAMS.insert(len(PARAMS.columns),'self.h_d',self.h_d)  
+# 
+# self.he = abs(self.h - self.h_u)
+# self.he = max(self.he,abs(self.h - self.h_d))
+# 
+# #PARAMS.insert(0,'dq',0.)
+# 
+# PARAMS.insert(len(PARAMS.columns),'self.he',self.he)  
+# PARAMS.insert(0,'Ps',np.array(ONE_COLUMN.PRES,dtype='float')[0]*100.)
+# #PARAMS.insert(len(PARAMS.columns),'STNM',STNM)
+# #PARAMS.insert(len(PARAMS.columns),'PATH',webpage)
+# 
+# if self.mode == 'o': #original 
+#     USE_ONECOLUMN = ONE_COLUMN
+#     BLCOLUMN = ONE_COLUMNb # this var is used for investigating whether the original profile is of sufficient quality to be used for analysis or class model input.
+# elif self.mode == 'b': # best BLH
+#     USE_ONECOLUMN = ONE_COLUMNb
+#     BLCOLUMN = ONE_COLUMNb
+# elif self.mode == 'u': # best BLH
+#     USE_ONECOLUMN = ONE_COLUMNu
+#     BLCOLUMN = ONE_COLUMNu
+# elif self.mode == 'd': # best BLH
+#     USE_ONECOLUMN = ONE_COLUMNd
+#     BLCOLUMN = ONE_COLUMNd
+# else:
+#     USE_ONECOLUMN = ONE_COLUMN
+#     BLCOLUMN = ONE_COLUMNb
+# 
+# lt6000 = (BLCOLUMN['HAGL'] < 6000.)
+# lt2500 = (BLCOLUMN['HAGL'] < 2500. + self.h)
+# # print(BLCOLUMN['HAGL'][lt6000])
+# # print(BLCOLUMN['HAGL'][lt2500])
+# # 
+# # print(len(np.where(lt2500)[0]) > 9.) # distance between two points (lower than 2500m) should be smaller than 400 meters
+# 
+# print(BLCOLUMN['HAGL'][lt2500])
+# PARAMS.insert(0,'OK',
+#               ((self.he < 200.) and 
+#                ( len(np.where(lt6000)[0]) > 5) and
+#                (np.array(BLCOLUMN['HAGL'])[-1] >= 6000.) and # the last coordinate had a height higher than 5000.
+#                (not len(np.where(pd.isnull(BLCOLUMN['THTA'][lt6000]))[0]) >0 ) and
+#                (len(np.where(lt2500)[0]) > 10.) and # distance between two points (lower than 2500m) should be smaller than 400 meters
+#                (not len(np.where(pd.isnull(BLCOLUMN['SKNT'][lt6000]))[0]) >0 ) and
+#                (not len(np.where(pd.isnull(BLCOLUMN['DRCT'][lt6000]))[0]) >0 ) and
+#                (not len(np.where(pd.isnull(BLCOLUMN['PRES'][lt6000]))[0]) >0 ) and
+#                (not len(np.where(pd.isnull(BLCOLUMN['QABS'][lt6000]))[0]) >0 ) and
+#                (not (len(np.where(np.array(BLCOLUMN['THTA'][lt6000])[2:] <= np.array(BLCOLUMN['THTA'][lt6000])[1:-1])[0]) >0) ) #absolute increasing
+#               )
+#              )
+# 
+# PARAMS.insert(0,'theta',np.float(list(BLCOLUMN['THTA'])[1]))
+# PARAMS.insert(0,'q',np.float(list(BLCOLUMN['QABS'])[1]))
+# PARAMS.insert(0,'u',np.float(list(BLCOLUMN['u_pro'])[1]))  
+# PARAMS.insert(0,'v',np.float(list(BLCOLUMN['v_pro'])[1]))
+# PARAMS.insert(0,'dtheta',np.float(list(BLCOLUMN['THTA'])[2]-list(BLCOLUMN['THTA'])[1]))
+# PARAMS.insert(0,'dq',np.float(list(BLCOLUMN['QABS'])[2]-list(BLCOLUMN['QABS'])[1]))
+# PARAMS.insert(0,'du',np.float(list(BLCOLUMN['u_pro'])[2]-list(BLCOLUMN['u_pro'])[1]))
+# PARAMS.insert(0,'dv',np.float(list(BLCOLUMN['v_pro'])[2]-list(BLCOLUMN['v_pro'])[1]))
+# 
+# 
+# PARAMS = PARAMS.T
+# 
+# 
+# self.PARAMS = PARAMS
+# self.ONE_COLUMN = USE_ONECOLUMN
+# # if self.mode == 'o': #original 
+# #     self.ONE_COLUMN = ONE_COLUMN
+# # elif self.mode == 'b': # best BLH
+# #     self.ONE_COLUMN = ONE_COLUMNb
+# # elif self.mode == 'u':# upper BLH
+# #     self.ONE_COLUMN = ONE_COLUMNu
+# # elif self.mode == 'd': # lower BLH
+# #     self.ONE_COLUMN=ONE_COLUMNd
+# # else:
+# #     self.ONE_COLUMN = ONE_COLUMN
+
diff --git a/class4gl/setup/batch_setup_era.pbs b/class4gl/setup/batch_setup_era.pbs
new file mode 100644
index 0000000..7735a7a
--- /dev/null
+++ b/class4gl/setup/batch_setup_era.pbs
@@ -0,0 +1,30 @@
+#!/bin/bash 
+#
+#PBS -j oe
+#PBS -M hendrik.wouters@ugent.be
+#PBS -m b
+#PBS -m e
+#PBS -m a
+#PBS -N c4gl_setup
+
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python Ruby
+
+EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
+
+for var in $(compgen -v | grep C4GLJOB_ ); do
+    echo $var
+    if [ "$var" != "C4GLJOB_exec" ]
+    then
+    EXEC_ALL=$EXEC_ALL" --"`echo $var | cut -c9-`"="${!var}
+    fi
+done
+
+
+# EXEC_ALL="python $exec --global-chunk-number $PBS_ARRAYID \
+#                        --split-by $split_by \
+#                        --dataset $dataset \
+#                        --experiments $experiments"
+#                  #      --path-soundings $path_soundings \
+echo Executing: $EXEC_ALL
+$EXEC_ALL
+
diff --git a/class4gl/setup/batch_setup_era.py b/class4gl/setup/batch_setup_era.py
new file mode 100644
index 0000000..7f8c0d0
--- /dev/null
+++ b/class4gl/setup/batch_setup_era.py
@@ -0,0 +1,188 @@
+
+# -*- coding: utf-8 -*-
+
+import logging
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import importlib
+spam_loader = importlib.find_loader('Pysolar')
+found = spam_loader is not None
+if found:
+    import Pysolar
+    import Pysolar.util.GetSunriseSunset
+else:
+    import pysolar as Pysolar
+    GetSunriseSunset =  Pysolar.util.get_sunrise_sunset
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--pbs_string',default='')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_YYYYMMDD',default="19810101")
+parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--latitude') # run a specific station id
+parser.add_argument('--longitude') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+parser.add_argument('--subset_experiments',default='ini') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+EXP_DEFS  =\
+{
+  'ERA-INTERIM_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+# iniitialize global data
+# ===============================
+print("Initializing global data")
+# ===============================
+globaldata = data_global()
+globaldata.sources = {**globaldata.sources,**{
+    
+        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_19830609-19830808_6hourly.nc",
+        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830609-19830808_6hourly.nc",
+        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_19830609-19830808_6hourly.nc",
+        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_19830609-19830808_6hourly.nc",
+    
+#        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
+ #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
+ #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",
+ #       "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v*_6hourly.nc",
+        }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+
+# # ===============================
+# print("Selecting station by ID")
+# # ===============================
+# stations_iter = stations_iterator(all_stations)
+# STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+# all_stations_select = pd.DataFrame([run_station])
+# print(run_station)
+
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if (args.latitude is not None) or (args.longitude is not None):
+    print('custom coordinates not implemented yet, please ask developer.')
+elif args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+#     print("making a custom station according to the coordinates")
+# 
+#     STNID = 43.23
+else:
+     print("Selecting stations from a row range in the table")
+     all_stations_select = pd.DataFrame(all_stations.table)
+     if args.last_station_row is not None:
+         all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+     if args.first_station_row is not None:
+         all_stations_select = all_station_select.iloc[int(args.first_station):]
+
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d",)
+dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d",)
+# ===============================
+print("Creating daily timeseries from", dtfirst," to ", dtlast)
+# ===============================
+DTS = [dtfirst + dt.timedelta(days=iday) for iday in \
+       range(int((dtlast + dt.timedelta(days=1) -
+                  dtfirst).total_seconds()/3600./24.))]
+
+if args.split_by != -1:
+    totalchunks = len(all_stations_select)*math.ceil(len(DTS)/int(args.split_by))
+else:
+    totalchunks = len(all_stations_select)
+
+print(totalchunks)
+
+#if args.cleanup_experiments:
+#    os.system("rm -R "+args.path_experiments+'/')
+
+# C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/setup/batch_setup_era.pbs -t 0-'+\
+            str(totalchunks-1)+" -v "
+# propagate arguments towards the job script
+lfirst = True
+for argkey in args.__dict__.keys():
+    if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
+        # default values are specified in the simulation script, so
+        # excluded here
+        (args.__dict__[argkey] is not None)
+       ):
+        if lfirst:
+            command +=' C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        else:
+            command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        lfirst=False
+
+print('Submitting array job: '+command)
+os.system(command)
diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 9d5a6f0..631e50c 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -144,15 +144,9 @@
      if args.first_station_row is not None:
          all_stations_select = all_station_select.iloc[int(args.first_station):]
 
-
-
-
-
-
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
-
 dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d",)
 dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d",)
 # ===============================
diff --git a/class4gl/setup/setup_global.py b/class4gl/setup/setup_global.py
index 79224d9..08adbb4 100644
--- a/class4gl/setup/setup_global.py
+++ b/class4gl/setup/setup_global.py
@@ -10,7 +10,7 @@
 usage:
     python setup_global.py 
     where  is an integer indicating the row index of the station list
-    under odir+'/'+fn_stations (see below)
+    under args.path_output+'/'+fn_stations (see below)
 
 this scripts should be called from the pbs script setup_global.pbs
 
@@ -29,19 +29,40 @@
 import sys
 #import copy as cp
 import numpy as np
-from sklearn.metrics import mean_squared_error
+#from sklearn.metrics import mean_squared_error
 import logging
 import datetime as dt
 import os
 import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+# parser.add_argument('--first_YYYYMMDD',default="19810101")
+# parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--station_id') # run a specific station id
+# parser.add_argument('--error_handling',default='dump_on_success')
+# parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+
+
+# args.path_output = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
 from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 
-odir = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
-fn_stations = odir+'/igra-stations_sel.txt'
+fn_stations = args.path_input+'/igra-stations.txt'
 
 
 #calculate the root mean square error
+
 def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
     """ calculated root mean squared error 
         
@@ -80,11 +101,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # which is the case for evaluating eg., mixed-layer estimates)
         y_predicted_temp = y_actual_temp*0. + y_predicted_temp
         
-    
-    return np.sqrt(mean_squared_error(y_actual_temp,y_predicted_temp))
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
 
 
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl
 from data_soundings import wyoming
 #from data_global import data_global
@@ -96,26 +117,34 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 # read the list of stations with valid ground data (list generated with
 # get_valid_stations.py)
-idir = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
-
-df_stations = pd.read_csv(fn_stations)
-
+# args.path_input = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_fwf(fn_stations,names=['Country code',\
+                                               'ID',\
+                                               'Name',\
+                                               'latitude',\
+                                               'longitude',\
+                                               'height',\
+                                               'unknown',\
+                                               'startyear',\
+                                               'endyear'])
+if args.station_id is not None:
+    df_stations = df_stations[df_stations.ID == int(args.station_id)]
+else:
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
+    if args.last_station_row is not None:
+        df_stations = df_stations[:(int(args.last_station_row)+1)]
 
 STNlist = list(df_stations.iterrows())
-NUMSTNS = len(STNlist)
-PROCS = 100
-BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
-
 
-iPROC = int(sys.argv[1])
-
-
-for iSTN,STN in STNlist[iPROC*BATCHSIZE:(iPROC+1)*BATCHSIZE]:  
+os.system('mkdir -p '+args.path_output)
+for iSTN,STN in STNlist:  
     one_run = False
 # for iSTN,STN in STNlist[5:]:  
     
-    fnout = odir+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = odir+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    fnout = args.path_output+"/"+format(STN['ID'],'05d')+"_morning.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
     
 
     # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
@@ -123,10 +152,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         
     with open(fnout,'w') as fileout, \
          open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=idir, STNM=STN['ID'])
+        wy_strm = wyoming(PATH=args.path_input, STNM=STN['ID'])
         wy_strm.set_STNM(int(STN['ID']))
 
-        # we consider all soundings after 1981
+        # we consider all soundings from 1981 onwards
         wy_strm.find_first(year=1981)
         #wy_strm.find(dt.datetime(2004,10,19,6))
         
@@ -137,7 +166,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         while wy_strm.current is not None:
             
             c4gli.clear()
-            try:
+            try: 
                 c4gli.get_profile_wyoming(wy_strm)
                 #print(STN['ID'],c4gli.pars.datetime)
                 #c4gli.get_global_input(globaldata)
@@ -179,6 +208,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             except:
                 morning_ok =False
                 print('obtain morning not good')
+
             # the next sounding will be used either for an afternoon sounding
             # or for the morning sounding of the next day.
             wy_strm.find_next()
@@ -292,12 +322,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     if one_run:
         STN.name = STN['ID']
         all_records_morning = get_records(pd.DataFrame([STN]),\
-                                      odir,\
+                                      args.path_output,\
                                       subset='morning',
                                       refetch_records=True,
                                       )
         all_records_afternoon = get_records(pd.DataFrame([STN]),\
-                                      odir,\
+                                      args.path_output,\
                                       subset='afternoon',
                                       refetch_records=True,
                                       )

From 82b3c8dedb69ce3c85e5cd168e740a95768e547e Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 15 Sep 2018 12:31:45 +0200
Subject: [PATCH 070/129] use all era-interim data

---
 class4gl/class4gl.py                       |   4 +-
 class4gl/interface/interface_cloudiness.py | 538 +++++++++++++++++++++
 class4gl/interface/interface_koeppen.py    | 149 ++++--
 class4gl/setup/setup_era.py                |  10 +-
 class4gl/setup/setup_global_afternoon.py   | 245 ++++++++++
 5 files changed, 890 insertions(+), 56 deletions(-)
 create mode 100644 class4gl/interface/interface_cloudiness.py
 create mode 100644 class4gl/setup/setup_global_afternoon.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 805ac35..1ed64f8 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -43,8 +43,8 @@
     import Pysolar
     import Pysolar.util as Pysolarutil
     GetSunriseSunset = Pysolarutil.GetSunriseSunset
-    GetAzimuth = Pysolarutil.GetAzimuth
-    GetAltitude = Pysolarutil.GetAltitude
+    GetAzimuth = Pysolarutil.solar.GetAzimuth
+    GetAltitude = Pysolarutil.solar.GetAltitude
 else:
     import pysolar as Pysolar
     Pysolarutil = Pysolar.util
diff --git a/class4gl/interface/interface_cloudiness.py b/class4gl/interface/interface_cloudiness.py
new file mode 100644
index 0000000..7dded65
--- /dev/null
+++ b/class4gl/interface/interface_cloudiness.py
@@ -0,0 +1,538 @@
+'''
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False)
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--show_control_parameters',default=True)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--figure_filename_2',default=None)
+parser.add_argument('--experiments_labels',default=None)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+if args.experiments_labels is None:
+    keylabels = args.experiments.strip().split(' ')
+else:
+    keylabels = args.experiments_labels.strip().split(';')
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if bool(args.load_globaldata):
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing,\
+                      globaldata,\
+                      refetch_records=False
+                    )
+    '''
+if args.make_figures:
+    # the lines below activate TaylorPlots but it is disabled for now
+    fig = plt.figure(figsize=(10,7))   #width,height
+    i = 1                                                                           
+    axes = {}         
+    axes_taylor = {}         
+    
+    colors = ['r','g','b','m']
+    symbols = ['*','x','+']
+    dias = {}
+    
+    for varkey in ['h','theta','q']:                                                    
+        axes[varkey] = fig.add_subplot(2,3,i)                                       
+        #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+    
+        #print(obs.std())
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        if i == 0:
+            dias[varkey]._ax.axis["left"].label.set_text(\
+                "Standard deviation (model) / Standard deviation (observations)")
+            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # Q95 = obs.quantile(0.95)
+        # Q95 = obs.quantile(0.90)
+        # Add RMS contours, and label them
+        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        #dia._ax.set_title(season.capitalize())
+    
+        dias[varkey].add_grid()
+    
+    
+        #dia.ax.plot(x99,y99,color='k')
+    
+        
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+            # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+            # clearsky = (cc < 0.05)
+            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            x, y = obs.values,mod.values
+            print(key,len(obs.values))
+    
+            STD_OBS = obs.std()
+            #scores
+            PR = pearsonr(mod,obs)[0]
+            RMSE = rmse(obs,mod)                                               
+            BIAS = np.mean(mod) - np.mean(obs)
+            STD = mod.std()
+            
+            # fit = np.polyfit(x,y,deg=1)
+            # axes[varkey].plot(x, fit[0] * x + fit[1],\
+            #                   color=colors[ikey],alpha=0.8,lw=2,\
+            #                   label=key+", "+\
+            #                               'R = '+str(round(PR,3))+', '+\
+            #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+            #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+            # axes[varkey].legend(fontsize=5)
+            
+            # print(STD)
+            # print(PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,
+                           marker='o', ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=keylabels[ikey])
+    
+        # put ticker position, see
+        # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+        # dia.ax.axis['bottom'].
+        # dia.ax.axis['left'].
+        # dia.ax.axis['left'].
+    
+        i += 1
+    
+    i = 0
+    for varkey in ['h','theta','q']:                                                    
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        keylabel = keylabels[ikey]
+        cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        clearsky = (cc < 0.05)
+    
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+    
+    
+        nbins=40       
+        x, y = obs.values,mod.values
+        
+        xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+        zi = np.zeros_like(xi)*np.nan       
+        for ibin in range(nbins):
+            xmin = x.min() + ibin * (x.max() - x.min())/nbins
+            xmax = xmin + (x.max() - x.min())/nbins
+            in_bin = ((x >= xmin) & (x < xmax))
+            ybin = y[in_bin]
+            xbin = x[in_bin]
+            if len(ybin) > 20:
+                k = kde.gaussian_kde((ybin))
+                zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+        zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+        zi_int = zi.cumsum(axis=1) 
+                     #  label=key+", "+\
+                     #                    'R = '+str(round(PR[0],3))+', '+\
+                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.5,0.84] ,
+                colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.16,0.84] ,
+                colors=['darkred'],alpha=0.5,)
+        nanxi = xi[zi != np.nan]
+        axes[varkey].set_xlim((nanxi.min(),nanxi.max()))
+        axes[varkey].set_ylim((nanxi.min(),nanxi.max()))
+        print(varkey,(nanxi.min(),nanxi.max()))
+    
+    
+        latex = {}
+        latex['dthetadt'] =  r'$d \theta / dt $'
+        latex['dqdt'] =      r'$d q / dt $'
+        latex['dhdt'] =      r'$d h / dt $'
+    
+        axes[varkey].set_xlabel('observations')     
+        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+    
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+    
+        axes[varkey].scatter(obs,mod, label='(only) '+keylabel+", "+\
+                                      'R = '+str(round(PR,3))+', '+\
+                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
+                             s=0.1,alpha=0.14,color='k')
+        axes[varkey].legend(fontsize=5)
+        
+
+
+
+        axes[varkey].set_xlabel('observations')     
+        if i==0:                                    
+            axes[varkey].set_ylabel('model')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+    
+    
+    
+    # legend for different forcing simulations (colors)
+    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    leg = []
+    for ikey,key in enumerate(args.experiments.strip().split(' ')):
+        
+        leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+        leg.append(leg1)
+    ax.axis('off')
+    #leg1 =
+    ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+    
+    
+    # # legend for different stations (symbols)
+    # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # isymbol = 0
+    # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+    #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+    #     leg.append(leg1)
+    #     isymbol += 1
+    # 
+    # # symbol for all stations
+    # leg1, = ax.plot([],'ko',markersize=10)
+    # leg.append(leg1)
+    
+    
+    # ax.axis('off')
+    # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    
+    
+    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+    
+    
+    #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+    # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+    # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+    
+    if args.figure_filename is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+    fig.show()  
+
+    if bool(args.show_control_parameters):
+
+        import seaborn as sns
+
+        pkmn_type_colors = [
+                                            '#A0A0A0',  # Poison
+                                            '#78C850',  # Grass
+                                            '#F08030',  # Fire
+                                            '#6890F0',  # Water
+                                            '#F08030',  # Fire
+                                            '#C03028',  # Fighting
+                                            '#F85888',  # Psychic
+                                            '#A8B820',  # Bug
+                                            '#A8A878',  # Normal
+                                            '#F8D030',  # Electric
+                                            '#E0C068',  # Ground
+                                            '#EE99AC',  # Fairy
+                                            '#B8A038',  # Rock
+                                            '#705898',  # Ghost
+                                            '#98D8D8',  # Ice
+                                            '#7038F8',  # Dragon
+                                           ]
+
+
+
+        sns.set_style('whitegrid')
+        #sns.set()
+        fig = pl.figure(figsize=(7,5))
+        i = 1
+        axes = {}
+        data_all = pd.DataFrame()
+        data_input = pd.DataFrame()
+        
+        
+        
+        # #for varkey in ['theta','q']:     
+        # EF =\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
+        # EF[EF<0] = np.nan
+        # EF[EF>1] = np.nan
+        
+        # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
+        
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        data_all = pd.DataFrame()
+
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        tempdatamodstats["source"] = "Soundings"
+        tempdatamodstats["source_index"] = "Soundings"
+
+        ini_ref = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+        tempdataini_this = pd.DataFrame(ini_ref.copy())
+
+        tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+        tempdatamodstats['STNID']= tempdataini_this.STNID
+        tempdatamodstats['source']= "Soundings"
+        tempdatamodstats['source_index']= "Soundings"
+        tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+        #print('hello')
+
+        tempdataini = pd.DataFrame(ini_ref)
+        tempdataini["source"] = "Soundings"
+        tempdataini["source_index"] = "Soundings"
+        tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+        #print('hello2')
+
+
+        data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+        data_input = pd.concat([data_input,tempdataini],axis=0)
+        #print(data_input.shape)
+        #print(data_all.shape)
+
+            
+        for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
+            #keylabels=args.experimts.strip().split(' ')
+            keylabel = keylabels[ikey]
+
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+            tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+            tempdatamodstats['STNID']= tempdataini_this.STNID
+            tempdatamodstats['source']= keylabel
+            tempdatamodstats['source_index']= keylabel
+            tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+            #print('hello')
+
+
+            tempdataini = pd.DataFrame(ini_ref.copy())
+            tempdataini["source"] = keylabel
+            tempdataini["source_index"] = keylabel
+            tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+    
+
+            #print('hello2')
+            index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
+            #print('hello3')
+
+            tempdataini = tempdataini.loc[index_intersect]
+            #print('hello4')
+            tempdatamodstats = tempdatamodstats.loc[index_intersect]
+            #print('hello5')
+
+
+            # data[varkey] = tempdatamodstats['d'+varkey+'dt']
+            data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+            data_input = pd.concat([data_input, tempdataini],axis=0)
+            #print(data_input.shape)
+            #print(data_all.shape)
+
+        data_input.cc = data_input.cc.clip(0.,+np.inf)
+
+        for varkey in ['h','theta','q']:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+            data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            #print(data_input.shape)
+            #print(data_all.shape)
+        #print('hello6')
+        #print(data_all.columns)
+        #print('hello7')
+        for varkey in ['h','theta','q']:
+            input_keys =['cc']
+            for input_key in input_keys:
+                varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+
+                #print('hello8')
+                #print(data_input.shape)
+                #print(data_all.shape)
+                input_key_full = input_key + "["+units[input_key]+"]"
+                data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2)
+                data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2,)
+                #print('hello9')
+                #print(data_input.shape)
+                #print(data_all.shape)
+                
+                qvalmax = data_all[varkey_full].quantile(0.999)
+                qvalmin = data_all[varkey_full].quantile(0.001)
+                select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
+                #print('hello11')
+                data_all = data_all[select_data]
+                #print('hello12')
+                data_input = data_input[select_data.values]
+                #print('hello13')
+                #print(data_input.shape)
+                #print(data_all.shape)
+                #print('hello10')
+                
+                sns.set(style="ticks", palette="pastel")
+                ax = fig.add_subplot(3,len(input_keys),i)
+                #sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+                
+                #ax.set_title(input_key_full)
+                sb = sns.boxplot(x=input_key_full, y=varkey_full, hue="source",
+                                 palette=pkmn_type_colors,
+                                # palette=["m", "g",'r','b'],
+                                 linewidth=1.2, data=data_all,sym='')
+                if i ==1:
+                     plt.legend(loc='upper right',fontsize=7.,frameon=True,framealpha=0.7)
+                else:
+                     ax.get_legend().set_visible(False)
+                #     plt.legend('off')
+                if i >= 3:
+                    ax.set_xticklabels(labels=['['+str(i)+','+str(i+1)+'[' for i in list(range(0,7))]+['[7,8]'])
+                    ax.set_xlabel('Cloudiness [okta]')
+                else:
+                    ax.set_xticklabels([])
+                    ax.set_xlabel('')
+
+                if np.mod(i,len(input_keys)) != 0:
+                    ax.set_yticklabels([])
+                    ax.set_ylabel('')
+
+                for j,artist in enumerate(ax.artists):
+                    if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
+                        # Set the linecolor on the artist to the facecolor, and set the facecolor to None
+                        #print(j,artist)
+                        col = artist.get_facecolor()
+                        #print(j,artist)
+                        artist.set_edgecolor(col)
+                        #print(j,artist)
+                        artist.set_facecolor('None')
+                
+                        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
+                        # Loop over them here, and use the same colour as above
+                        
+                        for k in range(j*5,j*5+5):
+                            line = ax.lines[k]
+                            line.set_color(col)
+                            line.set_mfc(col)
+                            line.set_mec(col)
+                
+                # Also fix the legend
+                j = 0
+                for legpatch in ax.get_legend().get_patches():
+                    if j > 0:
+
+                        col = legpatch.get_facecolor()
+                        legpatch.set_edgecolor(col)
+                        legpatch.set_facecolor('None')
+                    j +=1
+
+
+
+
+                #ax.grid()
+                #sns.despine(offset=10, trim=True)
+                i +=1
+        fig.tight_layout()
+        fig.subplots_adjust( bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
+        if args.figure_filename_2 is not None:
+            fig.savefig(args.figure_filename_2,dpi=250); print("Image file written to:", args.figure_filename_2)
+        fig.show()
+
+
+
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index cbe89f2..0054c20 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -18,6 +18,7 @@
 parser.add_argument('--show_control_parameters',default=True)
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--figure_filename_2',default=None)
+parser.add_argument('--experiments_labels',default=None)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -41,6 +42,10 @@
 
 
 
+if args.experiments_labels is None:
+    keylabels = args.experiments.strip().split(' ')
+else:
+    keylabels = args.experiments_labels.strip().split(';')
 
 def abline(slope, intercept,axis):
     """Plot a line from slope and intercept"""
@@ -123,25 +128,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False
                     )
-
 '''
 key = args.experiments.strip(' ').split(' ')[0]
 xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
 koeppenlookuptable = pd.DataFrame()
 koeppenlookuptable['KGCID'] = pd.Series(xrkoeppen['KGCID'])
-c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
-    c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
-
-koeppenlookuptable['amount'] = ""
-for ikoeppen,koeppen in koeppenlookuptable.iterrows():
-    print(ikoeppen,':',koeppen)
-    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen.KGCID)
-    print(np.sum(kgc_select))
-    koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
-
-koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
-koeppenlookuptable = koeppenlookuptable[:9]
-koeppenlookuptable = koeppenlookuptable.sort_index()
 
 
 kgccolors = {
@@ -156,17 +147,22 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     'Dfb':['darkviolet','white']    , 
 }
 kgcnames = {
-    'Dfa':'snow - fully humid - hot summer',
+    'Dfa':'snow \n fully humid \n hot summer',
     'Cfb':'green'       ,
-    'BSk':''      ,
-    'Csb':''      ,
+    'BSk':'4'      ,
+    'Csb':'5'      ,
     'Cfa':'darkgreen' ,     
-    'BWh':''      ,
-    'Aw' :''     ,
-    'Dwc':''     ,
-    'Dfb':''     ,
+    'BWh':'6'      ,
+    'Aw' :'7'     ,
+    'Dwc':'8'     ,
+    'Dfb':'9'     ,
     #'Dfa':'',
 }
+for KGCID in list(pd.Series(xrkoeppen['KGCID'])):
+    if KGCID not in kgcnames.keys():
+        kgcnames[KGCID] = KGCID
+    if KGCID not in kgccolors.keys():
+        kgccolors[KGCID] = ['k','k']
 
 
 koeppenlookuptable['color'] = ""
@@ -181,9 +177,25 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     koeppenlookuptable['name'].loc[ikoeppen] = kgcnames[koeppen.KGCID]
 
 
+
+c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
+    c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
+
+koeppenlookuptable['amount'] = ""
+for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+    print(ikoeppen,':',koeppen)
+    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
+    print(np.sum(kgc_select))
+    koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
+
+koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
+koeppenlookuptable = koeppenlookuptable[:9]
+koeppenlookuptable = koeppenlookuptable.sort_index()
+
+
 if args.make_figures:
     # the lines below activate TaylorPlots but it is disabled for now
-    fig = plt.figure(figsize=(10,7))   #width,height
+    fig = plt.figure(figsize=(7,5))   #width,height
     i = 1                                                                           
     axes = {}         
     axes_taylor = {}         
@@ -203,7 +215,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             icolor = 0
             for ikoeppen,koeppen in koeppenlookuptable.iterrows():
                 print(ikoeppen,':',koeppen)
-                kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen.KGCID)
+                kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
                 
                 koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
                 koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
@@ -249,9 +261,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
         #print(obs.std())
-        if i == 0:
+        if i == 1:
             dias[varkey]._ax.axis["left"].label.set_text(\
-                "Standard deviation (model) / Standard deviation (observations)")
+                "standard deviation (model) / standard deviation (observations)")
             # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
             # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
         #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
@@ -315,6 +327,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     for varkey in ['h','theta','q']:                                                    
         ikey = 0
         key = list(args.experiments.strip().split(' '))[ikey]
+        keylabel = keylabels[ikey]
         cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
         clearsky = (cc < 0.05)
     
@@ -358,20 +371,39 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         latex['dhdt'] =      r'$d h / dt $'
     
         axes[varkey].set_xlabel('observations')     
-        axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
-    
+
+        if varkey == 'q':
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+r'$10^{-3} \times $'+units['d'+varkey+'dt']+']')        
+        else:
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')     
+       #  c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname']
+
         PR = pearsonr(mod,obs)[0]
         RMSE = rmse(obs,mod)                                               
         BIAS = np.mean(mod) - np.mean(obs)
         STD = mod.std()
     
-        axes[varkey].scatter(obs,mod, label='(only) '+key+", "+\
-                                      'R = '+str(round(PR,3))+', '+\
-                                      'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-                                      'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] ,\
-                             s=0.1,alpha=0.14,color='k')
-        axes[varkey].legend(fontsize=5)
-        
+        axes[varkey].scatter(obs,mod, label='All',s=0.1,alpha=0.14,color='k')
+
+
+
+        #axes[varkey].legend(fontsize=5)
+
+        #trans = ax.get_xaxis_transform() # x in data untis, y in axes fraction
+
+        if varkey == 'q':
+            annotate_text = 'PC = '+str(round(PR,3))+'\n'+\
+                           'RMSE = '+str(round(RMSE*1000.,5))+r'$10^{-3} \times $'+ units['d'+varkey+'dt']+'\n'+\
+                           'BIAS = '+str(round(BIAS*1000.,5))+r'$10^{-3} \times $'+units['d'+varkey+'dt'] 
+        else:
+            annotate_text = 'PC = '+str(round(PR,3))+'\n'+\
+                           'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+'\n'+\
+                           'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] 
+
+
+        ann = axes[varkey].annotate(annotate_text, xy=(0.05, .95 ), xycoords='axes fraction',fontsize=7,
+       horizontalalignment='left', verticalalignment='top' 
+        )
 
 
 
@@ -380,7 +412,26 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             axes[varkey].set_ylabel('model')                                            
         abline(1,0,axis=axes[varkey])
         i +=1
-    
+
+        #axes[varkey].axis('equal')
+        axes[varkey].set_aspect('equal')
+
+        # To specify the number of ticks on both or any single axes
+        # plt.locator_params(axis='x', nbins=6)
+        #plt.locator_params( nbins=10)
+        axes[varkey].xaxis.set_major_locator(ticker.MaxNLocator(4))
+        axes[varkey].yaxis.set_major_locator(ticker.MaxNLocator(4))
+        # axes[varkey].xaxis.set_major_locator(ticker.MultipleLocator(5))
+        # axes[varkey].yaxis.set_major_locator(ticker.MultipleLocator(5))
+
+        if varkey == 'q':
+            ticks = ticker.FuncFormatter(lambda x, pos:
+                                         '{0:g}'.format(x*1000.))
+            axes[varkey].xaxis.set_major_formatter(ticks)
+            axes[varkey].yaxis.set_major_formatter(ticks)
+
+        #     # axes[varkey].set_xticklabels(labels=ax.get_xticklabels()*1000.)
+        #     # axes[varkey].set_yticklabels(labels=ax.get_yticklabels()*1000.)
     
     
     # # legend for different forcing simulations (colors)
@@ -412,7 +463,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
     
     
-    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+    fig.subplots_adjust(top=0.95,bottom=0.09,left=0.08,right=0.94,hspace=0.35,wspace=0.29)
     
     
     #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
@@ -420,7 +471,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
     
     if args.figure_filename is not None:
-        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+        fig.savefig(args.figure_filename,dpi=250); print("Image file written to:",args.figure_filename)
     fig.show()  
 
     if bool(args.show_control_parameters):
@@ -450,7 +501,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
         sns.set_style('whitegrid')
         #sns.set()
-        fig = pl.figure(figsize=(12,8))
+        fig = pl.figure(figsize=(7,5))
         i = 1
         axes = {}
         data_all = pd.DataFrame()
@@ -498,21 +549,22 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #print(data_all.shape)
 
             
-        for key in list(args.experiments.strip().split(' ')):
+        for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
+            keylabel = keylabels[ikey]
 
             tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
             tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
             tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
             tempdatamodstats['STNID']= tempdataini_this.STNID
-            tempdatamodstats['source']= key
-            tempdatamodstats['source_index']= key
+            tempdatamodstats['source']= keylabel
+            tempdatamodstats['source_index']= keylabel
             tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
             #print('hello')
 
 
             tempdataini = pd.DataFrame(ini_ref.copy())
-            tempdataini["source"] = key 
-            tempdataini["source_index"] = key
+            tempdataini["source"] = keylabel
+            tempdataini["source_index"] = keylabel
             tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
     
 
@@ -581,12 +633,13 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                             # palette=["m", "g",'r','b'],
                              linewidth=1.2, data=data_all,sym='')
             if i ==1:
-                 plt.legend(loc='upper right',fontsize=7.)
+                 plt.legend(loc='upper right',fontsize=7.,frameon=True,framealpha=0.7)
             else:
                  ax.get_legend().set_visible(False)
             #     plt.legend('off')
-            if i >= 2:
-                ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=45.,ha='right')
+            if i >= 3:
+                ax.set_xticklabels(labels=ax.get_xticklabels())
+                ax.set_xlabel('Köppen climate class')
             else:
                 ax.set_xticklabels([])
                 ax.set_xlabel('')
@@ -630,9 +683,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             #sns.despine(offset=10, trim=True)
             i +=1
         fig.tight_layout()
-        fig.subplots_adjust( bottom=0.18,left=0.09,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
+        fig.subplots_adjust( bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
         if args.figure_filename_2 is not None:
-            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
+            fig.savefig(args.figure_filename_2,dpi=250); print("Image file written to:", args.figure_filename_2)
         fig.show()
 
 
diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 631e50c..0c8ea96 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -88,12 +88,10 @@
 # ===============================
 globaldata = data_global()
 globaldata.sources = {**globaldata.sources,**{
-    
-        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_19830609-19830808_6hourly.nc",
-        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830609-19830808_6hourly.nc",
-        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_19830609-19830808_6hourly.nc",
-        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_19830609-19830808_6hourly.nc",
-    
+        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_????????-????????_6hourly.nc",
+        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_????????-????????_6hourly.nc",
+        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_????????-????????_6hourly.nc",
+        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_????????-????????_6hourly.nc",
 #        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
  #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
  #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",
diff --git a/class4gl/setup/setup_global_afternoon.py b/class4gl/setup/setup_global_afternoon.py
new file mode 100644
index 0000000..4a49a97
--- /dev/null
+++ b/class4gl/setup/setup_global_afternoon.py
@@ -0,0 +1,245 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under args.path_output+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+#from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+# parser.add_argument('--first_YYYYMMDD',default="19810101")
+# parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--station_id') # run a specific station id
+# parser.add_argument('--error_handling',default='dump_on_success')
+# parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+
+
+# args.path_output = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+fn_stations = args.path_input+'/igra-stations.txt'
+
+
+#calculate the root mean square error
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+# args.path_input = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+df_stations = pd.read_fwf(fn_stations,names=['Country code',\
+                                               'ID',\
+                                               'Name',\
+                                               'latitude',\
+                                               'longitude',\
+                                               'height',\
+                                               'unknown',\
+                                               'startyear',\
+                                               'endyear'])
+if args.station_id is not None:
+    df_stations = df_stations[df_stations.ID == int(args.station_id)]
+else:
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
+    if args.last_station_row is not None:
+        df_stations = df_stations[:(int(args.last_station_row)+1)]
+
+STNlist = list(df_stations.iterrows())
+
+os.system('mkdir -p '+args.path_output)
+for iSTN,STN in STNlist:  
+    one_run = False
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = args.path_output+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout:
+        wy_strm = wyoming(PATH=args.path_input, STNM=STN['ID'])
+        wy_strm.set_STNM(int(STN['ID']))
+
+        # we consider all soundings from 1981 onwards
+        wy_strm.find_first(year=1981)
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        
+        # so we continue as long as we can find a new sounding
+                
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            try: 
+                c4gli.get_profile_wyoming(wy_strm)
+                #print(STN['ID'],c4gli.pars.datetime)
+                #c4gli.get_global_input(globaldata)
+
+                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+                logic = dict()
+
+		#still needs to be changed to afternoon
+                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+                logic['daylight'] = \
+                    ((c4gli.pars.ldatetime_daylight - 
+                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
+                
+                logic['springsummer'] = (c4gli.pars.theta > 278.)
+                
+                # we take 3000 because previous analysis (ie., HUMPPA) has
+                # focussed towards such altitude
+                le3000 = (c4gli.air_balloon.z <= 3000.)
+                logic['10measurements'] = (np.sum(le3000) >= 5) 
+
+                #leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+                #logic['mlerrlow'] = (\
+                #        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                #        (not np.isnan(c4gli.pars.theta)) and \
+                #        (rmse(c4gli.air_balloon.theta[leh] , \
+                #              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                #              )
+    
+
+                #logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+                    
+                print('logic:', logic)
+                # the result
+                morning_ok = np.mean(list(logic.values()))
+                print(morning_ok,c4gli.pars.ldatetime)
+
+            except:
+                morning_ok =False
+                print('obtain morning not good')
+
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+                                
+            print(morning_ok)
+            if morning_ok == 1.:
+                c4gli.get_global_input(globaldata)
+                print('VERY CLOSE...')
+                if c4gli.check_source_globaldata() and \
+	            (c4gli.check_source(source='wyoming',\
+		                                   check_only_sections='pars')):
+                    c4gli.dump(fileout)
+                    one_run=True		     
+                    print('HIT!!!')
+            wy_strm.find_next()
+                
+    if one_run:
+        STN.name = STN['ID']
+
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      args.path_output,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    else:
+        os.system('rm '+fnout)
+        
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+

From efa6280ca03795eb217646aa76b77a5bfe2b45bd Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 15 Sep 2018 12:35:15 +0200
Subject: [PATCH 071/129] use all era-interim data

---
 class4gl/setup/setup_era.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 0c8ea96..43c1c73 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -88,10 +88,10 @@
 # ===============================
 globaldata = data_global()
 globaldata.sources = {**globaldata.sources,**{
-        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_????????-????????_6hourly.nc",
-        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_????????-????????_6hourly.nc",
-        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_????????-????????_6hourly.nc",
-        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_????????-????????_6hourly.nc",
+        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
+        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
+        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
+        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
 #        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
  #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
  #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",

From a1b42c34151df5cd2c04a117ce48ad5f80511162 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 15 Sep 2018 12:37:16 +0200
Subject: [PATCH 072/129] use all era-interim data

---
 class4gl/setup/setup_era.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 43c1c73..96ce88f 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -92,10 +92,6 @@
         "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
         "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
         "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
-#        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
- #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
- #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",
- #       "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v*_6hourly.nc",
         }}
 
 # ...  and load initial data pages

From f02f4200938c00a7e042a31dd3b68dc4a1eea559 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 18 Sep 2018 15:08:49 +0200
Subject: [PATCH 073/129] Fix Ps input; implement warning message in case of
 invalid input

---
 class4gl/c4gl_setup.o4759290-0             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-1             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-2             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-3             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-4             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-5             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-6             | 1251 ++++++++++++++++++++
 class4gl/c4gl_setup.o4759290-7             |  469 ++++++++
 class4gl/c4gl_sim.o4759326-0               |  227 ++++
 class4gl/c4gl_sim.o4759326-1               |  231 ++++
 class4gl/c4gl_sim.o4759326-2               |  227 ++++
 class4gl/c4gl_sim.o4759326-3               |  227 ++++
 class4gl/c4gl_sim.o4759326-4               |   10 +
 class4gl/c4gl_sim.o4759326-5               |   10 +
 class4gl/c4gl_sim.o4759326-6               |   10 +
 class4gl/c4gl_sim.o4759326-7               |   10 +
 class4gl/class4gl.py                       |   46 +-
 class4gl/interface/interface_cloudiness.py |   18 +-
 class4gl/interface/interface_koeppen.py    |   52 +-
 class4gl/model.py                          |   46 +-
 class4gl/simulations/simulations.py        |   24 +-
 21 files changed, 10307 insertions(+), 57 deletions(-)
 create mode 100644 class4gl/c4gl_setup.o4759290-0
 create mode 100644 class4gl/c4gl_setup.o4759290-1
 create mode 100644 class4gl/c4gl_setup.o4759290-2
 create mode 100644 class4gl/c4gl_setup.o4759290-3
 create mode 100644 class4gl/c4gl_setup.o4759290-4
 create mode 100644 class4gl/c4gl_setup.o4759290-5
 create mode 100644 class4gl/c4gl_setup.o4759290-6
 create mode 100644 class4gl/c4gl_setup.o4759290-7
 create mode 100644 class4gl/c4gl_sim.o4759326-0
 create mode 100644 class4gl/c4gl_sim.o4759326-1
 create mode 100644 class4gl/c4gl_sim.o4759326-2
 create mode 100644 class4gl/c4gl_sim.o4759326-3
 create mode 100644 class4gl/c4gl_sim.o4759326-4
 create mode 100644 class4gl/c4gl_sim.o4759326-5
 create mode 100644 class4gl/c4gl_sim.o4759326-6
 create mode 100644 class4gl/c4gl_sim.o4759326-7

diff --git a/class4gl/c4gl_setup.o4759290-0 b/class4gl/c4gl_setup.o4759290-0
new file mode 100644
index 0000000..45f4cc7
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-0
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml
+0 1981-01-01 00:00:00
+1 1981-01-02 00:00:00
+2 1981-01-03 00:00:00
+3 1981-01-04 00:00:00
+4 1981-01-05 00:00:00
+5 1981-01-06 00:00:00
+6 1981-01-07 00:00:00
+7 1981-01-08 00:00:00
+8 1981-01-09 00:00:00
+9 1981-01-10 00:00:00
+10 1981-01-11 00:00:00
+11 1981-01-12 00:00:00
+12 1981-01-13 00:00:00
+13 1981-01-14 00:00:00
+14 1981-01-15 00:00:00
+15 1981-01-16 00:00:00
+16 1981-01-17 00:00:00
+17 1981-01-18 00:00:00
+18 1981-01-19 00:00:00
+19 1981-01-20 00:00:00
+20 1981-01-21 00:00:00
+21 1981-01-22 00:00:00
+22 1981-01-23 00:00:00
+23 1981-01-24 00:00:00
+24 1981-01-25 00:00:00
+25 1981-01-26 00:00:00
+26 1981-01-27 00:00:00
+27 1981-01-28 00:00:00
+28 1981-01-29 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-01-30 00:00:00
+30 1981-01-31 00:00:00
+31 1981-02-01 00:00:00
+32 1981-02-02 00:00:00
+33 1981-02-03 00:00:00
+34 1981-02-04 00:00:00
+35 1981-02-05 00:00:00
+36 1981-02-06 00:00:00
+37 1981-02-07 00:00:00
+38 1981-02-08 00:00:00
+39 1981-02-09 00:00:00
+40 1981-02-10 00:00:00
+41 1981-02-11 00:00:00
+42 1981-02-12 00:00:00
+43 1981-02-13 00:00:00
+44 1981-02-14 00:00:00
+45 1981-02-15 00:00:00
+46 1981-02-16 00:00:00
+47 1981-02-17 00:00:00
+48 1981-02-18 00:00:00
+49 1981-02-19 00:00:00
+pkl file older than yaml file, so I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 51396
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.51396').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.51396 
+ obs record registered
+ next record: 102622
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.102622').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.102622 
+ obs record registered
+ next record: 155319
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.155319').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.155319 
+ obs record registered
+ next record: 207321
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.207321').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.207321 
+ obs record registered
+ next record: 261499
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.261499').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.261499 
+ obs record registered
+ next record: 312096
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.312096').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.312096 
+ obs record registered
+ next record: 363706
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.363706').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.363706 
+ obs record registered
+ next record: 416568
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.416568').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.416568 
+ obs record registered
+ next record: 470247
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.470247').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.470247 
+ obs record registered
+ next record: 524351
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.524351').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.524351 
+ obs record registered
+ next record: 577126
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.577126').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.577126 
+ obs record registered
+ next record: 629651
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.629651').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.629651 
+ obs record registered
+ next record: 682101
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.682101').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.682101 
+ obs record registered
+ next record: 734044
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.734044').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.734044 
+ obs record registered
+ next record: 786398
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.786398').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.786398 
+ obs record registered
+ next record: 838710
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.838710').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.838710 
+ obs record registered
+ next record: 891166
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.891166').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.891166 
+ obs record registered
+ next record: 943327
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.943327').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.943327 
+ obs record registered
+ next record: 995731
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.995731').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.995731 
+ obs record registered
+ next record: 1050024
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1050024').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1050024 
+ obs record registered
+ next record: 1102851
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1102851').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1102851 
+ obs record registered
+ next record: 1154781
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1154781').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1154781 
+ obs record registered
+ next record: 1208836
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1208836').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1208836 
+ obs record registered
+ next record: 1263823
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1263823').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1263823 
+ obs record registered
+ next record: 1318780
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1318780').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1318780 
+ obs record registered
+ next record: 1368688
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1368688').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1368688 
+ obs record registered
+ next record: 1420916
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1420916').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1420916 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1472567
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1472567').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1472567 
+ obs record registered
+ next record: 1524967
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1524967').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1524967 
+ obs record registered
+ next record: 1576324
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1576324').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1576324 
+ obs record registered
+ next record: 1629313
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1629313').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1629313 
+ obs record registered
+ next record: 1680371
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1680371').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1680371 
+ obs record registered
+ next record: 1731186
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1731186').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1731186 
+ obs record registered
+ next record: 1782240
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1782240').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1782240 
+ obs record registered
+ next record: 1833412
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1833412').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1833412 
+ obs record registered
+ next record: 1886384
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1886384').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1886384 
+ obs record registered
+ next record: 1937565
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1937565').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1937565 
+ obs record registered
+ next record: 1988408
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1988408').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1988408 
+ obs record registered
+ next record: 2038456
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2038456').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2038456 
+ obs record registered
+ next record: 2089432
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2089432').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2089432 
+ obs record registered
+ next record: 2141348
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2141348').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2141348 
+ obs record registered
+ next record: 2192964
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2192964').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2192964 
+ obs record registered
+ next record: 2247428
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2247428').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2247428 
+ obs record registered
+ next record: 2302316
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2302316').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2302316 
+ obs record registered
+ next record: 2356848
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2356848').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2356848 
+ obs record registered
+ next record: 2411058
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2411058').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2411058 
+ obs record registered
+ next record: 2462275
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2462275').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2462275 
+ obs record registered
+ next record: 2516002
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2516002').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2516002 
+ obs record registered
+ next record: 2568449
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2568449').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2568449 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-1 b/class4gl/c4gl_setup.o4759290-1
new file mode 100644
index 0000000..d958506
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-1
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml
+0 1981-02-20 00:00:00
+1 1981-02-21 00:00:00
+2 1981-02-22 00:00:00
+3 1981-02-23 00:00:00
+4 1981-02-24 00:00:00
+5 1981-02-25 00:00:00
+6 1981-02-26 00:00:00
+7 1981-02-27 00:00:00
+8 1981-02-28 00:00:00
+9 1981-03-01 00:00:00
+10 1981-03-02 00:00:00
+11 1981-03-03 00:00:00
+12 1981-03-04 00:00:00
+13 1981-03-05 00:00:00
+14 1981-03-06 00:00:00
+15 1981-03-07 00:00:00
+16 1981-03-08 00:00:00
+17 1981-03-09 00:00:00
+18 1981-03-10 00:00:00
+19 1981-03-11 00:00:00
+20 1981-03-12 00:00:00
+21 1981-03-13 00:00:00
+22 1981-03-14 00:00:00
+23 1981-03-15 00:00:00
+24 1981-03-16 00:00:00
+25 1981-03-17 00:00:00
+26 1981-03-18 00:00:00
+27 1981-03-19 00:00:00
+28 1981-03-20 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-03-21 00:00:00
+30 1981-03-22 00:00:00
+31 1981-03-23 00:00:00
+32 1981-03-24 00:00:00
+33 1981-03-25 00:00:00
+34 1981-03-26 00:00:00
+35 1981-03-27 00:00:00
+36 1981-03-28 00:00:00
+37 1981-03-29 00:00:00
+38 1981-03-30 00:00:00
+39 1981-03-31 00:00:00
+40 1981-04-01 00:00:00
+41 1981-04-02 00:00:00
+42 1981-04-03 00:00:00
+43 1981-04-04 00:00:00
+44 1981-04-05 00:00:00
+45 1981-04-06 00:00:00
+46 1981-04-07 00:00:00
+47 1981-04-08 00:00:00
+48 1981-04-09 00:00:00
+49 1981-04-10 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 53735
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.53735').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.53735 
+ obs record registered
+ next record: 107051
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.107051').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.107051 
+ obs record registered
+ next record: 159809
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.159809').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.159809 
+ obs record registered
+ next record: 210583
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.210583').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.210583 
+ obs record registered
+ next record: 262669
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.262669').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.262669 
+ obs record registered
+ next record: 315998
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.315998').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.315998 
+ obs record registered
+ next record: 368733
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.368733').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.368733 
+ obs record registered
+ next record: 422182
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.422182').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.422182 
+ obs record registered
+ next record: 473690
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.473690').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.473690 
+ obs record registered
+ next record: 525232
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.525232').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.525232 
+ obs record registered
+ next record: 578431
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.578431').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.578431 
+ obs record registered
+ next record: 633046
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.633046').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.633046 
+ obs record registered
+ next record: 686127
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.686127').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.686127 
+ obs record registered
+ next record: 738483
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.738483').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.738483 
+ obs record registered
+ next record: 790644
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.790644').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.790644 
+ obs record registered
+ next record: 844792
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.844792').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.844792 
+ obs record registered
+ next record: 897654
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.897654').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.897654 
+ obs record registered
+ next record: 950524
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.950524').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.950524 
+ obs record registered
+ next record: 1003819
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1003819').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1003819 
+ obs record registered
+ next record: 1057080
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1057080').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1057080 
+ obs record registered
+ next record: 1109138
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1109138').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1109138 
+ obs record registered
+ next record: 1162278
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1162278').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1162278 
+ obs record registered
+ next record: 1216404
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1216404').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1216404 
+ obs record registered
+ next record: 1268030
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1268030').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1268030 
+ obs record registered
+ next record: 1320921
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1320921').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1320921 
+ obs record registered
+ next record: 1372574
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1372574').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1372574 
+ obs record registered
+ next record: 1424704
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1424704').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1424704 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1476503
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1476503').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1476503 
+ obs record registered
+ next record: 1528706
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1528706').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1528706 
+ obs record registered
+ next record: 1583120
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1583120').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1583120 
+ obs record registered
+ next record: 1635666
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1635666').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1635666 
+ obs record registered
+ next record: 1689323
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1689323').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1689323 
+ obs record registered
+ next record: 1741836
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1741836').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1741836 
+ obs record registered
+ next record: 1795229
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1795229').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1795229 
+ obs record registered
+ next record: 1847312
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1847312').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1847312 
+ obs record registered
+ next record: 1900220
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1900220').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1900220 
+ obs record registered
+ next record: 1954053
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1954053').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1954053 
+ obs record registered
+ next record: 2005224
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2005224').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2005224 
+ obs record registered
+ next record: 2055085
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2055085').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2055085 
+ obs record registered
+ next record: 2109238
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2109238').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2109238 
+ obs record registered
+ next record: 2158379
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2158379').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2158379 
+ obs record registered
+ next record: 2213439
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2213439').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2213439 
+ obs record registered
+ next record: 2265091
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2265091').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2265091 
+ obs record registered
+ next record: 2312591
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2312591').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2312591 
+ obs record registered
+ next record: 2363001
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2363001').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2363001 
+ obs record registered
+ next record: 2417249
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2417249').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2417249 
+ obs record registered
+ next record: 2472181
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2472181').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2472181 
+ obs record registered
+ next record: 2523821
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2523821').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2523821 
+ obs record registered
+ next record: 2576934
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2576934').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2576934 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-2 b/class4gl/c4gl_setup.o4759290-2
new file mode 100644
index 0000000..d9771a1
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-2
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml
+0 1981-04-11 00:00:00
+1 1981-04-12 00:00:00
+2 1981-04-13 00:00:00
+3 1981-04-14 00:00:00
+4 1981-04-15 00:00:00
+5 1981-04-16 00:00:00
+6 1981-04-17 00:00:00
+7 1981-04-18 00:00:00
+8 1981-04-19 00:00:00
+9 1981-04-20 00:00:00
+10 1981-04-21 00:00:00
+11 1981-04-22 00:00:00
+12 1981-04-23 00:00:00
+13 1981-04-24 00:00:00
+14 1981-04-25 00:00:00
+15 1981-04-26 00:00:00
+16 1981-04-27 00:00:00
+17 1981-04-28 00:00:00
+18 1981-04-29 00:00:00
+19 1981-04-30 00:00:00
+20 1981-05-01 00:00:00
+21 1981-05-02 00:00:00
+22 1981-05-03 00:00:00
+23 1981-05-04 00:00:00
+24 1981-05-05 00:00:00
+25 1981-05-06 00:00:00
+26 1981-05-07 00:00:00
+27 1981-05-08 00:00:00
+28 1981-05-09 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-05-10 00:00:00
+30 1981-05-11 00:00:00
+31 1981-05-12 00:00:00
+32 1981-05-13 00:00:00
+33 1981-05-14 00:00:00
+34 1981-05-15 00:00:00
+35 1981-05-16 00:00:00
+36 1981-05-17 00:00:00
+37 1981-05-18 00:00:00
+38 1981-05-19 00:00:00
+39 1981-05-20 00:00:00
+40 1981-05-21 00:00:00
+41 1981-05-22 00:00:00
+42 1981-05-23 00:00:00
+43 1981-05-24 00:00:00
+44 1981-05-25 00:00:00
+45 1981-05-26 00:00:00
+46 1981-05-27 00:00:00
+47 1981-05-28 00:00:00
+48 1981-05-29 00:00:00
+49 1981-05-30 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 51067
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.51067').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.51067 
+ obs record registered
+ next record: 104322
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.104322').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.104322 
+ obs record registered
+ next record: 158125
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.158125').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.158125 
+ obs record registered
+ next record: 210001
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.210001').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.210001 
+ obs record registered
+ next record: 263270
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.263270').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.263270 
+ obs record registered
+ next record: 317457
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.317457').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.317457 
+ obs record registered
+ next record: 368268
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.368268').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.368268 
+ obs record registered
+ next record: 422781
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.422781').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.422781 
+ obs record registered
+ next record: 476050
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.476050').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.476050 
+ obs record registered
+ next record: 528316
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.528316').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.528316 
+ obs record registered
+ next record: 580931
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.580931').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.580931 
+ obs record registered
+ next record: 635773
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.635773').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.635773 
+ obs record registered
+ next record: 686190
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.686190').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.686190 
+ obs record registered
+ next record: 736979
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.736979').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.736979 
+ obs record registered
+ next record: 791508
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.791508').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.791508 
+ obs record registered
+ next record: 844468
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.844468').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.844468 
+ obs record registered
+ next record: 899058
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.899058').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.899058 
+ obs record registered
+ next record: 950395
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.950395').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.950395 
+ obs record registered
+ next record: 1001879
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1001879').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1001879 
+ obs record registered
+ next record: 1054711
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1054711').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1054711 
+ obs record registered
+ next record: 1107591
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1107591').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1107591 
+ obs record registered
+ next record: 1161498
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1161498').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1161498 
+ obs record registered
+ next record: 1216461
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1216461').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1216461 
+ obs record registered
+ next record: 1270638
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1270638').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1270638 
+ obs record registered
+ next record: 1323032
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1323032').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1323032 
+ obs record registered
+ next record: 1374748
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1374748').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1374748 
+ obs record registered
+ next record: 1426449
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1426449').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1426449 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1479418
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1479418').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1479418 
+ obs record registered
+ next record: 1533453
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1533453').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1533453 
+ obs record registered
+ next record: 1585360
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1585360').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1585360 
+ obs record registered
+ next record: 1638642
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1638642').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1638642 
+ obs record registered
+ next record: 1693282
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1693282').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1693282 
+ obs record registered
+ next record: 1746969
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1746969').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1746969 
+ obs record registered
+ next record: 1798551
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1798551').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1798551 
+ obs record registered
+ next record: 1851912
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1851912').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1851912 
+ obs record registered
+ next record: 1906431
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1906431').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1906431 
+ obs record registered
+ next record: 1957671
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1957671').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1957671 
+ obs record registered
+ next record: 2008390
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2008390').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2008390 
+ obs record registered
+ next record: 2059270
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2059270').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2059270 
+ obs record registered
+ next record: 2113052
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2113052').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2113052 
+ obs record registered
+ next record: 2167306
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2167306').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2167306 
+ obs record registered
+ next record: 2222303
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2222303').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2222303 
+ obs record registered
+ next record: 2276984
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2276984').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2276984 
+ obs record registered
+ next record: 2328277
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2328277').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2328277 
+ obs record registered
+ next record: 2381273
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2381273').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2381273 
+ obs record registered
+ next record: 2435826
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2435826').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2435826 
+ obs record registered
+ next record: 2489579
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2489579').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2489579 
+ obs record registered
+ next record: 2543769
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2543769').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2543769 
+ obs record registered
+ next record: 2597864
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2597864').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2597864 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-3 b/class4gl/c4gl_setup.o4759290-3
new file mode 100644
index 0000000..17f4526
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-3
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml
+0 1981-05-31 00:00:00
+1 1981-06-01 00:00:00
+2 1981-06-02 00:00:00
+3 1981-06-03 00:00:00
+4 1981-06-04 00:00:00
+5 1981-06-05 00:00:00
+6 1981-06-06 00:00:00
+7 1981-06-07 00:00:00
+8 1981-06-08 00:00:00
+9 1981-06-09 00:00:00
+10 1981-06-10 00:00:00
+11 1981-06-11 00:00:00
+12 1981-06-12 00:00:00
+13 1981-06-13 00:00:00
+14 1981-06-14 00:00:00
+15 1981-06-15 00:00:00
+16 1981-06-16 00:00:00
+17 1981-06-17 00:00:00
+18 1981-06-18 00:00:00
+19 1981-06-19 00:00:00
+20 1981-06-20 00:00:00
+21 1981-06-21 00:00:00
+22 1981-06-22 00:00:00
+23 1981-06-23 00:00:00
+24 1981-06-24 00:00:00
+25 1981-06-25 00:00:00
+26 1981-06-26 00:00:00
+27 1981-06-27 00:00:00
+28 1981-06-28 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-06-29 00:00:00
+30 1981-06-30 00:00:00
+31 1981-07-01 00:00:00
+32 1981-07-02 00:00:00
+33 1981-07-03 00:00:00
+34 1981-07-04 00:00:00
+35 1981-07-05 00:00:00
+36 1981-07-06 00:00:00
+37 1981-07-07 00:00:00
+38 1981-07-08 00:00:00
+39 1981-07-09 00:00:00
+40 1981-07-10 00:00:00
+41 1981-07-11 00:00:00
+42 1981-07-12 00:00:00
+43 1981-07-13 00:00:00
+44 1981-07-14 00:00:00
+45 1981-07-15 00:00:00
+46 1981-07-16 00:00:00
+47 1981-07-17 00:00:00
+48 1981-07-18 00:00:00
+49 1981-07-19 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 54256
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.54256').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.54256 
+ obs record registered
+ next record: 106783
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.106783').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.106783 
+ obs record registered
+ next record: 161041
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.161041').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.161041 
+ obs record registered
+ next record: 213499
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.213499').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.213499 
+ obs record registered
+ next record: 268606
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.268606').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.268606 
+ obs record registered
+ next record: 321942
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.321942').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.321942 
+ obs record registered
+ next record: 376318
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.376318').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.376318 
+ obs record registered
+ next record: 429745
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.429745').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.429745 
+ obs record registered
+ next record: 480238
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.480238').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.480238 
+ obs record registered
+ next record: 534353
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.534353').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.534353 
+ obs record registered
+ next record: 587556
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.587556').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.587556 
+ obs record registered
+ next record: 642546
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.642546').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.642546 
+ obs record registered
+ next record: 696672
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.696672').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.696672 
+ obs record registered
+ next record: 748004
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.748004').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.748004 
+ obs record registered
+ next record: 798522
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.798522').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.798522 
+ obs record registered
+ next record: 849366
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.849366').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.849366 
+ obs record registered
+ next record: 900431
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.900431').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.900431 
+ obs record registered
+ next record: 955333
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.955333').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.955333 
+ obs record registered
+ next record: 1008606
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1008606').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1008606 
+ obs record registered
+ next record: 1062216
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1062216').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1062216 
+ obs record registered
+ next record: 1116153
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1116153').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1116153 
+ obs record registered
+ next record: 1169750
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1169750').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1169750 
+ obs record registered
+ next record: 1220569
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1220569').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1220569 
+ obs record registered
+ next record: 1274715
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1274715').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1274715 
+ obs record registered
+ next record: 1325734
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1325734').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1325734 
+ obs record registered
+ next record: 1378235
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1378235').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1378235 
+ obs record registered
+ next record: 1430796
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1430796').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1430796 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1484617
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1484617').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1484617 
+ obs record registered
+ next record: 1539402
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1539402').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1539402 
+ obs record registered
+ next record: 1591441
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1591441').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1591441 
+ obs record registered
+ next record: 1645914
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1645914').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1645914 
+ obs record registered
+ next record: 1698613
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1698613').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1698613 
+ obs record registered
+ next record: 1751712
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1751712').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1751712 
+ obs record registered
+ next record: 1805578
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1805578').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1805578 
+ obs record registered
+ next record: 1859984
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1859984').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1859984 
+ obs record registered
+ next record: 1912886
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1912886').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1912886 
+ obs record registered
+ next record: 1966234
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1966234').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1966234 
+ obs record registered
+ next record: 2020399
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2020399').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2020399 
+ obs record registered
+ next record: 2071191
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2071191').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2071191 
+ obs record registered
+ next record: 2122985
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2122985').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2122985 
+ obs record registered
+ next record: 2176760
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2176760').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2176760 
+ obs record registered
+ next record: 2230944
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2230944').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2230944 
+ obs record registered
+ next record: 2284495
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2284495').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2284495 
+ obs record registered
+ next record: 2337035
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2337035').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2337035 
+ obs record registered
+ next record: 2390194
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2390194').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2390194 
+ obs record registered
+ next record: 2444461
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2444461').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2444461 
+ obs record registered
+ next record: 2497851
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2497851').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2497851 
+ obs record registered
+ next record: 2551916
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2551916').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2551916 
+ obs record registered
+ next record: 2606609
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2606609').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2606609 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-4 b/class4gl/c4gl_setup.o4759290-4
new file mode 100644
index 0000000..7fc0cea
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-4
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml
+0 1981-07-20 00:00:00
+1 1981-07-21 00:00:00
+2 1981-07-22 00:00:00
+3 1981-07-23 00:00:00
+4 1981-07-24 00:00:00
+5 1981-07-25 00:00:00
+6 1981-07-26 00:00:00
+7 1981-07-27 00:00:00
+8 1981-07-28 00:00:00
+9 1981-07-29 00:00:00
+10 1981-07-30 00:00:00
+11 1981-07-31 00:00:00
+12 1981-08-01 00:00:00
+13 1981-08-02 00:00:00
+14 1981-08-03 00:00:00
+15 1981-08-04 00:00:00
+16 1981-08-05 00:00:00
+17 1981-08-06 00:00:00
+18 1981-08-07 00:00:00
+19 1981-08-08 00:00:00
+20 1981-08-09 00:00:00
+21 1981-08-10 00:00:00
+22 1981-08-11 00:00:00
+23 1981-08-12 00:00:00
+24 1981-08-13 00:00:00
+25 1981-08-14 00:00:00
+26 1981-08-15 00:00:00
+27 1981-08-16 00:00:00
+28 1981-08-17 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-08-18 00:00:00
+30 1981-08-19 00:00:00
+31 1981-08-20 00:00:00
+32 1981-08-21 00:00:00
+33 1981-08-22 00:00:00
+34 1981-08-23 00:00:00
+35 1981-08-24 00:00:00
+36 1981-08-25 00:00:00
+37 1981-08-26 00:00:00
+38 1981-08-27 00:00:00
+39 1981-08-28 00:00:00
+40 1981-08-29 00:00:00
+41 1981-08-30 00:00:00
+42 1981-08-31 00:00:00
+43 1981-09-01 00:00:00
+44 1981-09-02 00:00:00
+45 1981-09-03 00:00:00
+46 1981-09-04 00:00:00
+47 1981-09-05 00:00:00
+48 1981-09-06 00:00:00
+49 1981-09-07 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 51731
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.51731').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.51731 
+ obs record registered
+ next record: 104580
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.104580').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.104580 
+ obs record registered
+ next record: 157565
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.157565').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.157565 
+ obs record registered
+ next record: 210613
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.210613').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.210613 
+ obs record registered
+ next record: 264470
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.264470').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.264470 
+ obs record registered
+ next record: 318729
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.318729').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.318729 
+ obs record registered
+ next record: 370870
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.370870').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.370870 
+ obs record registered
+ next record: 423492
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.423492').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.423492 
+ obs record registered
+ next record: 474750
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.474750').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.474750 
+ obs record registered
+ next record: 529842
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.529842').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.529842 
+ obs record registered
+ next record: 583680
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.583680').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.583680 
+ obs record registered
+ next record: 637895
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.637895').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.637895 
+ obs record registered
+ next record: 692631
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.692631').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.692631 
+ obs record registered
+ next record: 746761
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.746761').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.746761 
+ obs record registered
+ next record: 800591
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.800591').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.800591 
+ obs record registered
+ next record: 854394
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.854394').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.854394 
+ obs record registered
+ next record: 909045
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.909045').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.909045 
+ obs record registered
+ next record: 963060
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.963060').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.963060 
+ obs record registered
+ next record: 1015112
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1015112').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1015112 
+ obs record registered
+ next record: 1067515
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1067515').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1067515 
+ obs record registered
+ next record: 1121265
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1121265').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1121265 
+ obs record registered
+ next record: 1175359
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1175359').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1175359 
+ obs record registered
+ next record: 1227436
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1227436').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1227436 
+ obs record registered
+ next record: 1279990
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1279990').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1279990 
+ obs record registered
+ next record: 1333481
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1333481').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1333481 
+ obs record registered
+ next record: 1388277
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1388277').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1388277 
+ obs record registered
+ next record: 1440447
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1440447').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1440447 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1492950
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1492950').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1492950 
+ obs record registered
+ next record: 1544819
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1544819').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1544819 
+ obs record registered
+ next record: 1597484
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1597484').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1597484 
+ obs record registered
+ next record: 1650523
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1650523').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1650523 
+ obs record registered
+ next record: 1703570
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1703570').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1703570 
+ obs record registered
+ next record: 1755369
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1755369').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1755369 
+ obs record registered
+ next record: 1808308
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1808308').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1808308 
+ obs record registered
+ next record: 1862932
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1862932').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1862932 
+ obs record registered
+ next record: 1917988
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1917988').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1917988 
+ obs record registered
+ next record: 1972660
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1972660').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1972660 
+ obs record registered
+ next record: 2027192
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2027192').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2027192 
+ obs record registered
+ next record: 2080750
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2080750').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2080750 
+ obs record registered
+ next record: 2134876
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2134876').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2134876 
+ obs record registered
+ next record: 2187060
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2187060').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2187060 
+ obs record registered
+ next record: 2240918
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2240918').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2240918 
+ obs record registered
+ next record: 2293459
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2293459').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2293459 
+ obs record registered
+ next record: 2345446
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2345446').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2345446 
+ obs record registered
+ next record: 2399559
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2399559').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2399559 
+ obs record registered
+ next record: 2453394
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2453394').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2453394 
+ obs record registered
+ next record: 2507446
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2507446').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2507446 
+ obs record registered
+ next record: 2560803
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2560803').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2560803 
+ obs record registered
+ next record: 2613551
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2613551').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2613551 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-5 b/class4gl/c4gl_setup.o4759290-5
new file mode 100644
index 0000000..3ffddc2
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-5
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml
+0 1981-09-08 00:00:00
+1 1981-09-09 00:00:00
+2 1981-09-10 00:00:00
+3 1981-09-11 00:00:00
+4 1981-09-12 00:00:00
+5 1981-09-13 00:00:00
+6 1981-09-14 00:00:00
+7 1981-09-15 00:00:00
+8 1981-09-16 00:00:00
+9 1981-09-17 00:00:00
+10 1981-09-18 00:00:00
+11 1981-09-19 00:00:00
+12 1981-09-20 00:00:00
+13 1981-09-21 00:00:00
+14 1981-09-22 00:00:00
+15 1981-09-23 00:00:00
+16 1981-09-24 00:00:00
+17 1981-09-25 00:00:00
+18 1981-09-26 00:00:00
+19 1981-09-27 00:00:00
+20 1981-09-28 00:00:00
+21 1981-09-29 00:00:00
+22 1981-09-30 00:00:00
+23 1981-10-01 00:00:00
+24 1981-10-02 00:00:00
+25 1981-10-03 00:00:00
+26 1981-10-04 00:00:00
+27 1981-10-05 00:00:00
+28 1981-10-06 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-10-07 00:00:00
+30 1981-10-08 00:00:00
+31 1981-10-09 00:00:00
+32 1981-10-10 00:00:00
+33 1981-10-11 00:00:00
+34 1981-10-12 00:00:00
+35 1981-10-13 00:00:00
+36 1981-10-14 00:00:00
+37 1981-10-15 00:00:00
+38 1981-10-16 00:00:00
+39 1981-10-17 00:00:00
+40 1981-10-18 00:00:00
+41 1981-10-19 00:00:00
+42 1981-10-20 00:00:00
+43 1981-10-21 00:00:00
+44 1981-10-22 00:00:00
+45 1981-10-23 00:00:00
+46 1981-10-24 00:00:00
+47 1981-10-25 00:00:00
+48 1981-10-26 00:00:00
+49 1981-10-27 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 53791
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.53791').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.53791 
+ obs record registered
+ next record: 108418
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.108418').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.108418 
+ obs record registered
+ next record: 160595
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.160595').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.160595 
+ obs record registered
+ next record: 213556
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.213556').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.213556 
+ obs record registered
+ next record: 267801
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.267801').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.267801 
+ obs record registered
+ next record: 322353
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.322353').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.322353 
+ obs record registered
+ next record: 376117
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.376117').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.376117 
+ obs record registered
+ next record: 428437
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.428437').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.428437 
+ obs record registered
+ next record: 482412
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.482412').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.482412 
+ obs record registered
+ next record: 534383
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.534383').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.534383 
+ obs record registered
+ next record: 587814
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.587814').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.587814 
+ obs record registered
+ next record: 639583
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.639583').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.639583 
+ obs record registered
+ next record: 692536
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.692536').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.692536 
+ obs record registered
+ next record: 746174
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.746174').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.746174 
+ obs record registered
+ next record: 798143
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.798143').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.798143 
+ obs record registered
+ next record: 850877
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.850877').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.850877 
+ obs record registered
+ next record: 905196
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.905196').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.905196 
+ obs record registered
+ next record: 959041
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.959041').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.959041 
+ obs record registered
+ next record: 1010607
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1010607').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1010607 
+ obs record registered
+ next record: 1063046
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1063046').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1063046 
+ obs record registered
+ next record: 1118074
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1118074').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1118074 
+ obs record registered
+ next record: 1173117
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1173117').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1173117 
+ obs record registered
+ next record: 1225841
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1225841').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1225841 
+ obs record registered
+ next record: 1276984
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1276984').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1276984 
+ obs record registered
+ next record: 1329882
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1329882').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1329882 
+ obs record registered
+ next record: 1384174
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1384174').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1384174 
+ obs record registered
+ next record: 1438389
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1438389').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1438389 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1492962
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1492962').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1492962 
+ obs record registered
+ next record: 1544189
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1544189').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1544189 
+ obs record registered
+ next record: 1598345
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1598345').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1598345 
+ obs record registered
+ next record: 1651761
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1651761').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1651761 
+ obs record registered
+ next record: 1705208
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1705208').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1705208 
+ obs record registered
+ next record: 1759539
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1759539').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1759539 
+ obs record registered
+ next record: 1812463
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1812463').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1812463 
+ obs record registered
+ next record: 1865446
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1865446').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1865446 
+ obs record registered
+ next record: 1918746
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1918746').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1918746 
+ obs record registered
+ next record: 1972414
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1972414').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1972414 
+ obs record registered
+ next record: 2025735
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2025735').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2025735 
+ obs record registered
+ next record: 2079164
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2079164').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2079164 
+ obs record registered
+ next record: 2132791
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2132791').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2132791 
+ obs record registered
+ next record: 2181910
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2181910').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2181910 
+ obs record registered
+ next record: 2233649
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2233649').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2233649 
+ obs record registered
+ next record: 2284947
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2284947').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2284947 
+ obs record registered
+ next record: 2339510
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2339510').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2339510 
+ obs record registered
+ next record: 2391981
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2391981').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2391981 
+ obs record registered
+ next record: 2443387
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2443387').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2443387 
+ obs record registered
+ next record: 2497540
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2497540').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2497540 
+ obs record registered
+ next record: 2552060
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2552060').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2552060 
+ obs record registered
+ next record: 2604997
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2604997').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2604997 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-6 b/class4gl/c4gl_setup.o4759290-6
new file mode 100644
index 0000000..bd1fb54
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-6
@@ -0,0 +1,1251 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml
+0 1981-10-28 00:00:00
+1 1981-10-29 00:00:00
+2 1981-10-30 00:00:00
+3 1981-10-31 00:00:00
+4 1981-11-01 00:00:00
+5 1981-11-02 00:00:00
+6 1981-11-03 00:00:00
+7 1981-11-04 00:00:00
+8 1981-11-05 00:00:00
+9 1981-11-06 00:00:00
+10 1981-11-07 00:00:00
+11 1981-11-08 00:00:00
+12 1981-11-09 00:00:00
+13 1981-11-10 00:00:00
+14 1981-11-11 00:00:00
+15 1981-11-12 00:00:00
+16 1981-11-13 00:00:00
+17 1981-11-14 00:00:00
+18 1981-11-15 00:00:00
+19 1981-11-16 00:00:00
+20 1981-11-17 00:00:00
+21 1981-11-18 00:00:00
+22 1981-11-19 00:00:00
+23 1981-11-20 00:00:00
+24 1981-11-21 00:00:00
+25 1981-11-22 00:00:00
+26 1981-11-23 00:00:00
+27 1981-11-24 00:00:00
+28 1981-11-25 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+
+29 1981-11-26 00:00:00
+30 1981-11-27 00:00:00
+31 1981-11-28 00:00:00
+32 1981-11-29 00:00:00
+33 1981-11-30 00:00:00
+34 1981-12-01 00:00:00
+35 1981-12-02 00:00:00
+36 1981-12-03 00:00:00
+37 1981-12-04 00:00:00
+38 1981-12-05 00:00:00
+39 1981-12-06 00:00:00
+40 1981-12-07 00:00:00
+41 1981-12-08 00:00:00
+42 1981-12-09 00:00:00
+43 1981-12-10 00:00:00
+44 1981-12-11 00:00:00
+45 1981-12-12 00:00:00
+46 1981-12-13 00:00:00
+47 1981-12-14 00:00:00
+48 1981-12-15 00:00:00
+49 1981-12-16 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml"...
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 55140
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.55140').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.55140 
+ obs record registered
+ next record: 108893
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.108893').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.108893 
+ obs record registered
+ next record: 163443
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.163443').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.163443 
+ obs record registered
+ next record: 218397
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.218397').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.218397 
+ obs record registered
+ next record: 271582
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.271582').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.271582 
+ obs record registered
+ next record: 324721
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.324721').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.324721 
+ obs record registered
+ next record: 377607
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.377607').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.377607 
+ obs record registered
+ next record: 430834
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.430834').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.430834 
+ obs record registered
+ next record: 483626
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.483626').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.483626 
+ obs record registered
+ next record: 535205
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.535205').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.535205 
+ obs record registered
+ next record: 589944
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.589944').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.589944 
+ obs record registered
+ next record: 642475
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.642475').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.642475 
+ obs record registered
+ next record: 694024
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.694024').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.694024 
+ obs record registered
+ next record: 748833
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.748833').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.748833 
+ obs record registered
+ next record: 803389
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.803389').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.803389 
+ obs record registered
+ next record: 856629
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.856629').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.856629 
+ obs record registered
+ next record: 910612
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.910612').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.910612 
+ obs record registered
+ next record: 965476
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.965476').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.965476 
+ obs record registered
+ next record: 1020450
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1020450').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1020450 
+ obs record registered
+ next record: 1074651
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1074651').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1074651 
+ obs record registered
+ next record: 1127994
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1127994').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1127994 
+ obs record registered
+ next record: 1182110
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1182110').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1182110 
+ obs record registered
+ next record: 1233619
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1233619').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1233619 
+ obs record registered
+ next record: 1284930
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1284930').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1284930 
+ obs record registered
+ next record: 1337484
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1337484').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1337484 
+ obs record registered
+ next record: 1392464
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1392464').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1392464 
+ obs record registered
+ next record: 1446248
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1446248').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1446248 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ obs record registered
+ next record: 1499604
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1499604').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1499604 
+ obs record registered
+ next record: 1552283
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1552283').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1552283 
+ obs record registered
+ next record: 1606822
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1606822').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1606822 
+ obs record registered
+ next record: 1658571
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1658571').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1658571 
+ obs record registered
+ next record: 1712543
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1712543').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1712543 
+ obs record registered
+ next record: 1766710
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1766710').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1766710 
+ obs record registered
+ next record: 1819637
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1819637').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1819637 
+ obs record registered
+ next record: 1870451
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1870451').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1870451 
+ obs record registered
+ next record: 1921627
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1921627').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1921627 
+ obs record registered
+ next record: 1975018
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1975018').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1975018 
+ obs record registered
+ next record: 2027134
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2027134').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2027134 
+ obs record registered
+ next record: 2079285
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2079285').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2079285 
+ obs record registered
+ next record: 2134131
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2134131').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2134131 
+ obs record registered
+ next record: 2185735
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2185735').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2185735 
+ obs record registered
+ next record: 2237453
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2237453').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2237453 
+ obs record registered
+ next record: 2291618
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2291618').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2291618 
+ obs record registered
+ next record: 2345783
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2345783').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2345783 
+ obs record registered
+ next record: 2399221
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2399221').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2399221 
+ obs record registered
+ next record: 2454054
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2454054').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2454054 
+ obs record registered
+ next record: 2508671
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2508671').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2508671 
+ obs record registered
+ next record: 2563249
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2563249').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2563249 
+ obs record registered
+ next record: 2616063
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.2616063').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.2616063 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-7 b/class4gl/c4gl_setup.o4759290-7
new file mode 100644
index 0000000..ee5acc9
--- /dev/null
+++ b/class4gl/c4gl_setup.o4759290-7
@@ -0,0 +1,469 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_error_handling
+C4GLJOB_exec
+C4GLJOB_first_YYYYMMDD
+C4GLJOB_last_YYYYMMDD
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_experiments
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
+  warnings.warn('omitting pressure field p and advection')
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  enable_cftimeindex)
+/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
+  return self.func(self.array[key])
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+Initializing global data
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
+setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
+setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
+setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
+calculating texture
+calculating texture type
+calculating soil parameter
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
+opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
+start looping over chunk
+Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml
+0 1981-12-17 00:00:00
+1 1981-12-18 00:00:00
+2 1981-12-19 00:00:00
+3 1981-12-20 00:00:00
+4 1981-12-21 00:00:00
+5 1981-12-22 00:00:00
+6 1981-12-23 00:00:00
+7 1981-12-24 00:00:00
+8 1981-12-25 00:00:00
+9 1981-12-26 00:00:00
+10 1981-12-27 00:00:00
+11 1981-12-28 00:00:00
+12 1981-12-29 00:00:00
+13 1981-12-30 00:00:00
+14 1981-12-31 00:00:00
+15 1982-01-01 00:00:00
+pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml"...
+refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml".../user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame.
+Try using .loc[row_indexer,col_indexer] = value instead
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  air_ap_head[column] = ml_mean[column]
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
+A value is trying to be set on a copy of a slice from a DataFrame
+
+See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
+  mode=air_ap_mode)
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
+  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+ next record: 4
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.4 
+ obs record registered
+ next record: 53111
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.53111').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.53111 
+ obs record registered
+ next record: 106374
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.106374').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.106374 
+ obs record registered
+ next record: 159575
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.159575').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.159575 
+ obs record registered
+ next record: 214479
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.214479').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.214479 
+ obs record registered
+ next record: 265342
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.265342').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.265342 
+ obs record registered
+ next record: 320174
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.320174').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.320174 
+ obs record registered
+ next record: 372684
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.372684').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.372684 
+ obs record registered
+ next record: 423846
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.423846').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.423846 
+ obs record registered
+ next record: 476614
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.476614').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.476614 
+ obs record registered
+ next record: 529909
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.529909').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.529909 
+ obs record registered
+ next record: 581984
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.581984').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.581984 
+ obs record registered
+ next record: 634675
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.634675').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.634675 
+ obs record registered
+ next record: 688831
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.688831').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.688831 
+ obs record registered
+ next record: 743330
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.743330').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.743330 
+ obs record registered
+ next record: 797329
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.797329').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.797329 
+ obs record registered
+writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_sim.o4759326-0 b/class4gl/c4gl_sim.o4759326-0
new file mode 100644
index 0000000..f8ba869
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-0
@@ -0,0 +1,227 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+       Unnamed: 0          filename  latitude  longitude
+74560           0  74560_0_ini.yaml     40.15     -89.33
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (0)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 0
+Fetching initial/forcing records
+starting station chunk number: 0(size: 50 soundings)
+starting 1 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.4 
+Warning: Ps input was provided (99384.96484375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91367.05564375Pa).
+run not succesfull
+starting 2 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.51396').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.51396 
+Warning: Ps input was provided (100124.83463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89846.70143541667Pa).
+run not succesfull
+starting 3 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.102622').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.102622 
+Warning: Ps input was provided (99980.35416666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96094.41696666667Pa).
+run not succesfull
+starting 4 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.155319').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.155319 
+Warning: Ps input was provided (101228.97786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94990.99506458333Pa).
+run not succesfull
+starting 5 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.207321').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.207321 
+Warning: Ps input was provided (100561.96223958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99736.74503958333Pa).
+run not succesfull
+starting 6 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.261499').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.261499 
+Warning: Ps input was provided (98617.18880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89281.99280208333Pa).
+run not succesfull
+starting 7 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.312096').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.312096 
+Warning: Ps input was provided (99861.65104166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91664.80744166666Pa).
+run not succesfull
+starting 8 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.363706').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.363706 
+Warning: Ps input was provided (99783.63932291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96377.99972291668Pa).
+run not succesfull
+starting 9 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.416568').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.416568 
+Warning: Ps input was provided (100101.34765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98246.08045625Pa).
+run not succesfull
+starting 10 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.470247').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.470247 
+Warning: Ps input was provided (101217.34895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100305.01895833333Pa).
+run not succesfull
+starting 11 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.524351').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.524351 
+Warning: Ps input was provided (101140.765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99524.470025Pa).
+run not succesfull
+starting 12 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.577126').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.577126 
+Warning: Ps input was provided (99971.93880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94347.27720208332Pa).
+run not succesfull
+starting 13 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.629651').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.629651 
+Warning: Ps input was provided (98563.27864583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93396.54784583332Pa).
+run not succesfull
+starting 14 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.682101').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.682101 
+Warning: Ps input was provided (98789.33203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97675.70083125Pa).
+run not succesfull
+starting 15 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.734044').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.734044 
+Warning: Ps input was provided (99563.50390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95163.13030625Pa).
+run not succesfull
+starting 16 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.786398').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.786398 
+Warning: Ps input was provided (100669.421875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95027.102275Pa).
+run not succesfull
+starting 17 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.838710').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.838710 
+Warning: Ps input was provided (100948.26692708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96705.63812708332Pa).
+run not succesfull
+starting 18 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.891166').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.891166 
+Warning: Ps input was provided (100156.60026041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92800.27746041668Pa).
+run not succesfull
+starting 19 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.943327').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.943327 
+Warning: Ps input was provided (99349.36979166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93320.92859166667Pa).
+run not succesfull
+starting 20 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.995731').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.995731 
+Warning: Ps input was provided (99228.83203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98584.90363125Pa).
+run not succesfull
+starting 21 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1050024').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1050024 
+Warning: Ps input was provided (99770.28515625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96156.28115625Pa).
+run not succesfull
+starting 22 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1102851').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1102851 
+Warning: Ps input was provided (99380.921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94221.254275Pa).
+run not succesfull
+starting 23 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1154781').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1154781 
+Warning: Ps input was provided (99095.53255208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98209.10095208333Pa).
+run not succesfull
+starting 24 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1208836').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1208836 
+Warning: Ps input was provided (98733.72786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98616.00786458333Pa).
+run not succesfull
+starting 25 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1263823').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1263823 
+Warning: Ps input was provided (97895.48697916667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97777.76697916667Pa).
+run not succesfull
+starting 26 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1318780').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1318780 
+Warning: Ps input was provided (98477.62369791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (78494.65369791667Pa).
+run not succesfull
+starting 27 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1368688').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1368688 
+Warning: Ps input was provided (98800.078125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93290.782125Pa).
+run not succesfull
+starting 28 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1420916').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1420916 
+Warning: Ps input was provided (99248.63541666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90668.02461666668Pa).
+run not succesfull
+starting 29 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1472567').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1472567 
+Warning: Ps input was provided (100427.43880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96005.87560208332Pa).
+run not succesfull
+starting 30 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1524967').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1524967 
+Warning: Ps input was provided (101123.49348958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90677.02068958333Pa).
+run not succesfull
+starting 31 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1576324').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1576324 
+Warning: Ps input was provided (99848.95182291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95586.31062291667Pa).
+run not succesfull
+starting 32 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1629313').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1629313 
+Warning: Ps input was provided (97864.29296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87103.50776875Pa).
+run not succesfull
+starting 33 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1680371').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1680371 
+Warning: Ps input was provided (99503.52864583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87630.28944583333Pa).
+run not succesfull
+starting 34 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1731186').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1731186 
+Warning: Ps input was provided (100169.15364583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86911.52724583333Pa).
+run not succesfull
+starting 35 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1782240').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1782240 
+Warning: Ps input was provided (100522.32291666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88935.14331666667Pa).
+run not succesfull
+starting 36 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1833412').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1833412 
+Warning: Ps input was provided (100123.75260416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96482.67300416667Pa).
+run not succesfull
+starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
+  warnings.warn("Key '"+key+"' may not be implemented.")
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
+  self.L = zsl/self.zeta
+ 366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1886384').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1886384 
+Warning: Ps input was provided (99870.125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89217.6422Pa).
+run not succesfull
+starting 38 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1937565').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1937565 
+Warning: Ps input was provided (98704.2734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86112.9422375Pa).
+run not succesfull
+starting 39 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1988408').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1988408 
+Warning: Ps input was provided (99314.90885416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (81565.08725416668Pa).
+run not succesfull
+starting 40 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2038456').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2038456 
+Warning: Ps input was provided (99467.91145833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84809.41705833333Pa).
+run not succesfull
+starting 41 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2089432').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2089432 
+Warning: Ps input was provided (97532.21354166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91573.22714166668Pa).
+run not succesfull
+starting 42 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2141348').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2141348 
+Warning: Ps input was provided (100356.95442708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92337.86802708333Pa).
+run not succesfull
+starting 43 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2192964').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2192964 
+Warning: Ps input was provided (101625.39453125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101236.91853125Pa).
+run not succesfull
+starting 44 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2247428').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2247428 
+Warning: Ps input was provided (101831.296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101700.627675Pa).
+run not succesfull
+starting 45 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2302316').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2302316 
+Warning: Ps input was provided (101392.83463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101039.67463541667Pa).
+run not succesfull
+starting 46 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2356848').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2356848 
+Warning: Ps input was provided (100442.67317708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99184.24637708333Pa).
+run not succesfull
+starting 47 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2411058').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2411058 
+Warning: Ps input was provided (99915.86848958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88082.65408958333Pa).
+run not succesfull
+starting 48 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2462275').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2462275 
+Warning: Ps input was provided (99762.47265625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97849.52265625Pa).
+run not succesfull
+starting 49 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2516002').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2516002 
+Warning: Ps input was provided (99270.4765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93171.4033625Pa).
+run not succesfull
+starting 50 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2568449').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2568449 
+Warning: Ps input was provided (98692.77994791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97569.73114791667Pa).
+run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-1 b/class4gl/c4gl_sim.o4759326-1
new file mode 100644
index 0000000..87274a1
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-1
@@ -0,0 +1,231 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+       Unnamed: 0          filename  latitude  longitude
+74560           0  74560_0_ini.yaml     40.15     -89.33
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (1)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 1
+Fetching initial/forcing records
+starting station chunk number: 1(size: 50 soundings)
+starting 1 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.4 
+Warning: Ps input was provided (99189.77604166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97712.39004166667Pa).
+run not succesfull
+starting 2 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.53735').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.53735 
+Warning: Ps input was provided (98917.74609375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96435.03129375Pa).
+run not succesfull
+starting 3 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.107051').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.107051 
+Warning: Ps input was provided (97149.79296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93409.82856875Pa).
+run not succesfull
+starting 4 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.159809').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.159809 
+Warning: Ps input was provided (97484.921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (82631.012275Pa).
+run not succesfull
+starting 5 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.210583').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.210583 
+Warning: Ps input was provided (99121.34895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93010.50375833333Pa).
+run not succesfull
+starting 6 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.262669').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.262669 
+Warning: Ps input was provided (99433.81119791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97557.35439791667Pa).
+run not succesfull
+starting 7 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.315998').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.315998 
+Warning: Ps input was provided (100124.39713541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95708.71993541667Pa).
+run not succesfull
+starting 8 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.368733').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.368733 
+Warning: Ps input was provided (99289.96223958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96497.64383958332Pa).
+run not succesfull
+starting 9 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.422182').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.422182 
+Warning: Ps input was provided (99191.98567708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90040.43287708332Pa).
+run not succesfull
+starting 10 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.473690').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.473690 
+Warning: Ps input was provided (99402.77604166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90833.93724166667Pa).
+run not succesfull
+starting 11 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.525232').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.525232 
+Warning: Ps input was provided (99445.84114583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96549.92914583333Pa).
+run not succesfull
+starting 12 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.578431').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.578431 
+Warning: Ps input was provided (99561.35026041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99443.63026041667Pa).
+run not succesfull
+starting 13 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.633046').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.633046 
+Warning: Ps input was provided (98115.078125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97646.552525Pa).
+run not succesfull
+starting 14 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.686127').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.686127 
+Warning: Ps input was provided (98767.19401041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93118.98841041667Pa).
+run not succesfull
+starting 15 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.738483').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.738483 
+Warning: Ps input was provided (99317.18489583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94815.57209583333Pa).
+run not succesfull
+starting 16 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.790644').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.790644 
+Warning: Ps input was provided (100321.02083333333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100132.66883333333Pa).
+run not succesfull
+starting 17 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.844792').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.844792 
+Warning: Ps input was provided (100697.07291666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100287.40731666668Pa).
+run not succesfull
+starting 18 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.897654').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.897654 
+Warning: Ps input was provided (100432.74088541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99341.47648541667Pa).
+run not succesfull
+starting 19 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.950524').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.950524 
+Warning: Ps input was provided (100163.28125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99861.91805Pa).
+run not succesfull
+starting 20 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1003819').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1003819 
+Warning: Ps input was provided (100273.30598958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98120.20718958332Pa).
+run not succesfull
+starting 21 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1057080').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1057080 
+Warning: Ps input was provided (99064.4140625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91138.3264625Pa).
+run not succesfull
+starting 22 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1109138').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1109138 
+Warning: Ps input was provided (99177.81380208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96793.98380208333Pa).
+run not succesfull
+starting 23 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1162278').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1162278 
+Warning: Ps input was provided (99862.77734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99573.18614375Pa).
+run not succesfull
+starting 24 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1216404').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1216404 
+Warning: Ps input was provided (98215.37630208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88103.22830208333Pa).
+run not succesfull
+starting 25 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1268030').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1268030 
+Warning: Ps input was provided (99520.6640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98864.9636625Pa).
+run not succesfull
+starting 26 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1320921').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1320921 
+Warning: Ps input was provided (97935.72135416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90289.80735416667Pa).
+run not succesfull
+starting 27 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1372574').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1372574 
+Warning: Ps input was provided (98352.68619791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92378.39619791668Pa).
+run not succesfull
+starting 28 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1424704').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1424704 
+Warning: Ps input was provided (98539.91276041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92226.58916041667Pa).
+run not succesfull
+starting 29 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1476503').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1476503 
+Warning: Ps input was provided (98725.5390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93864.8802625Pa).
+run not succesfull
+starting 30 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1528706').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1528706 
+Warning: Ps input was provided (98957.06510416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98042.38070416667Pa).
+run not succesfull
+starting 31 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1583120').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1583120 
+Warning: Ps input was provided (99557.94270833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93847.34550833332Pa).
+run not succesfull
+starting 32 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1635666').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1635666 
+Warning: Ps input was provided (99887.29296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98538.22176875Pa).
+run not succesfull
+starting 33 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1689323').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1689323 
+Warning: Ps input was provided (99869.75911458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98305.26031458333Pa).
+run not succesfull
+starting 34 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1741836').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1741836 
+Warning: Ps input was provided (100003.671875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98346.174275Pa).
+run not succesfull
+starting 35 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1795229').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1795229 
+Warning: Ps input was provided (99218.69921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91785.85841875Pa).
+run not succesfull
+starting 36 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1847312').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1847312 
+Warning: Ps input was provided (100100.86328125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96563.37728125Pa).
+run not succesfull
+starting 37 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1900220').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1900220 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
+  warnings.warn("Key '"+key+"' may not be implemented.")
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
+  self.L = zsl/self.zeta
+
+Warning: Ps input was provided (99535.61067708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98012.31387708333Pa).
+run not succesfull
+starting 38 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1954053').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1954053 
+Warning: Ps input was provided (98121.40234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86747.29594375Pa).
+run not succesfull
+starting 39 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2005224').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2005224 
+Warning: Ps input was provided (98072.640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (76835.952625Pa).
+run not succesfull
+starting 40 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2055085').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2055085 
+Warning: Ps input was provided (98379.67578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97726.32978125Pa).
+run not succesfull
+starting 41 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2109238').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2109238 
+Warning: Ps input was provided (98887.31901041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (71017.10901041667Pa).
+run not succesfull
+starting 42 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2158379').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2158379 
+Warning: Ps input was provided (99447.98372395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99166.63292395833Pa).
+run not succesfull
+starting 43 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2213439').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2213439 
+Warning: Ps input was provided (98189.0390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89527.2014625Pa).
+run not succesfull
+starting 44 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2265091').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2265091 
+Warning: Ps input was provided (97722.154296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (49966.681896875Pa).
+LCL calculation not converged!!
+RHlcl = -0.000035, zlcl=7775135.081090, theta=296.584021, q=0.004835
+LCL calculation not converged!!
+RHlcl = -0.000035, zlcl=7775135.081090, theta=296.584021, q=0.004835
+run not succesfull
+starting 45 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2312591').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2312591 
+Warning: Ps input was provided (99512.51041666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83614.42441666668Pa).
+run not succesfull
+starting 46 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2363001').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2363001 
+Warning: Ps input was provided (100458.30859375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100280.55139375Pa).
+run not succesfull
+starting 47 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2417249').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2417249 
+Warning: Ps input was provided (99404.5234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99218.5258375Pa).
+run not succesfull
+starting 48 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2472181').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2472181 
+Warning: Ps input was provided (98729.45247395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90835.14927395833Pa).
+run not succesfull
+starting 49 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2523821').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2523821 
+Warning: Ps input was provided (99818.05989583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97451.88789583332Pa).
+run not succesfull
+starting 50 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2576934').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2576934 
+Warning: Ps input was provided (99569.44205729167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97954.32365729166Pa).
+run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-2 b/class4gl/c4gl_sim.o4759326-2
new file mode 100644
index 0000000..6c90b67
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-2
@@ -0,0 +1,227 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+       Unnamed: 0          filename  latitude  longitude
+74560           0  74560_0_ini.yaml     40.15     -89.33
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (2)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 2
+Fetching initial/forcing records
+starting station chunk number: 2(size: 50 soundings)
+starting 1 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.4 
+Warning: Ps input was provided (99219.09375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87069.21255Pa).
+run not succesfull
+starting 2 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.51067').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.51067 
+Warning: Ps input was provided (99111.41341145833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96887.68261145832Pa).
+run not succesfull
+starting 3 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.104322').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.104322 
+Warning: Ps input was provided (99240.26953125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97729.92193125Pa).
+run not succesfull
+starting 4 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.158125').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.158125 
+Warning: Ps input was provided (100401.01627604167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94004.11147604167Pa).
+run not succesfull
+starting 5 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.210001').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.210001 
+Warning: Ps input was provided (101597.61653645833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98645.19893645833Pa).
+run not succesfull
+starting 6 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.263270').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.263270 
+Warning: Ps input was provided (100343.29231770833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99933.62671770834Pa).
+run not succesfull
+starting 7 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.317457').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.317457 
+Warning: Ps input was provided (99029.62109375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83470.56869375Pa).
+run not succesfull
+starting 8 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.368268').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.368268 
+Warning: Ps input was provided (100035.25390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99644.42350625Pa).
+run not succesfull
+starting 9 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.422781').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.422781 
+Warning: Ps input was provided (99447.54231770833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96552.80751770832Pa).
+run not succesfull
+starting 10 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.476050').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.476050 
+Warning: Ps input was provided (100032.83854166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92228.00254166668Pa).
+run not succesfull
+starting 11 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.528316').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.528316 
+Warning: Ps input was provided (99849.60807291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94654.62447291666Pa).
+run not succesfull
+starting 12 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.580931').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.580931 
+Warning: Ps input was provided (98739.5625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98586.5265Pa).
+run not succesfull
+starting 13 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.635773').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.635773 
+Warning: Ps input was provided (98331.50260416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (82513.46620416667Pa).
+run not succesfull
+starting 14 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.686190').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.686190 
+Warning: Ps input was provided (98651.11783854167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85587.72943854168Pa).
+run not succesfull
+starting 15 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.736979').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.736979 
+Warning: Ps input was provided (99153.72395833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98759.36195833333Pa).
+run not succesfull
+starting 16 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.791508').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.791508 
+Warning: Ps input was provided (98915.3125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95224.7905Pa).
+run not succesfull
+starting 17 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.844468').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.844468 
+Warning: Ps input was provided (99249.35872395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98826.74392395833Pa).
+run not succesfull
+starting 18 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.899058').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.899058 
+Warning: Ps input was provided (98736.89192708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87358.07672708333Pa).
+run not succesfull
+starting 19 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.950395').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.950395 
+Warning: Ps input was provided (98866.00325520833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89488.42805520832Pa).
+run not succesfull
+starting 20 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1001879').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1001879 
+Warning: Ps input was provided (98843.9375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94478.8799Pa).
+run not succesfull
+starting 21 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1054711').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1054711 
+Warning: Ps input was provided (99160.09635416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95736.79875416667Pa).
+run not succesfull
+starting 22 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1107591').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1107591 
+Warning: Ps input was provided (99728.29817708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98725.32377708332Pa).
+run not succesfull
+starting 23 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1161498').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1161498 
+Warning: Ps input was provided (99118.85416666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98987.00776666668Pa).
+run not succesfull
+starting 24 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1216461').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1216461 
+Warning: Ps input was provided (99008.45247395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97757.08887395833Pa).
+run not succesfull
+starting 25 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1270638').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1270638 
+Warning: Ps input was provided (99078.38802083333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93394.86642083334Pa).
+run not succesfull
+starting 26 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1323032').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1323032 
+Warning: Ps input was provided (99683.82486979167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91330.41366979167Pa).
+run not succesfull
+starting 27 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1374748').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1374748 
+Warning: Ps input was provided (99829.79166666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89606.98686666667Pa).
+run not succesfull
+starting 28 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1426449').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1426449 
+Warning: Ps input was provided (99290.98893229167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95774.69253229166Pa).
+run not succesfull
+starting 29 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1479418').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1479418 
+Warning: Ps input was provided (99007.84830729167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98137.89750729167Pa).
+run not succesfull
+starting 30 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1533453').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1533453 
+Warning: Ps input was provided (98365.1796875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91098.3240875Pa).
+run not succesfull
+starting 31 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1585360').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1585360 
+Warning: Ps input was provided (98840.94791666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98285.30951666668Pa).
+run not succesfull
+starting 32 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1638642').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1638642 
+Warning: Ps input was provided (99649.287109375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99080.699509375Pa).
+run not succesfull
+starting 33 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1693282').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1693282 
+Warning: Ps input was provided (99164.91276041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97164.84996041667Pa).
+run not succesfull
+starting 34 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1746969').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1746969 
+Warning: Ps input was provided (98023.13151041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89570.83551041667Pa).
+run not succesfull
+starting 35 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1798551').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1798551 
+Warning: Ps input was provided (99066.55598958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96748.64918958333Pa).
+run not succesfull
+starting 36 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1851912').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1851912 
+Warning: Ps input was provided (99497.17252604167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99370.03492604167Pa).
+run not succesfull
+starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
+  warnings.warn("Key '"+key+"' may not be implemented.")
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
+  self.L = zsl/self.zeta
+ 366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1906431').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1906431 
+Warning: Ps input was provided (99586.98111979167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94123.59591979167Pa).
+run not succesfull
+starting 38 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1957671').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1957671 
+Warning: Ps input was provided (99235.24479166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84176.50239166667Pa).
+run not succesfull
+starting 39 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2008390').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2008390 
+Warning: Ps input was provided (99279.59440104167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85735.90840104167Pa).
+run not succesfull
+starting 40 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2059270').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2059270 
+Warning: Ps input was provided (99774.5234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98984.6222375Pa).
+run not succesfull
+starting 41 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2113052').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2113052 
+Warning: Ps input was provided (99802.658203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98880.910603125Pa).
+run not succesfull
+starting 42 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2167306').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2167306 
+Warning: Ps input was provided (99349.02734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99158.32094375Pa).
+run not succesfull
+starting 43 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2222303').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2222303 
+Warning: Ps input was provided (98955.87239583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98241.31199583333Pa).
+run not succesfull
+starting 44 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2276984').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2276984 
+Warning: Ps input was provided (98735.572265625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86832.903065625Pa).
+run not succesfull
+starting 45 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2328277').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2328277 
+Warning: Ps input was provided (99026.02408854167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95189.52928854167Pa).
+run not succesfull
+starting 46 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2381273').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2381273 
+Warning: Ps input was provided (98960.017578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98404.379178125Pa).
+run not succesfull
+starting 47 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2435826').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2435826 
+Warning: Ps input was provided (98886.17122395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97028.54962395833Pa).
+run not succesfull
+starting 48 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2489579').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2489579 
+Warning: Ps input was provided (99096.7578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97814.7870125Pa).
+run not succesfull
+starting 49 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2543769').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2543769 
+Warning: Ps input was provided (98649.583984375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98244.627184375Pa).
+run not succesfull
+starting 50 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2597864').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2597864 
+Warning: Ps input was provided (98760.32682291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97915.09722291667Pa).
+run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-3 b/class4gl/c4gl_sim.o4759326-3
new file mode 100644
index 0000000..57c4168
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-3
@@ -0,0 +1,227 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
+getting a list of stations
+defining all_stations_select
+Selecting station by ID
+station numbers included in the whole batch (all chunks): [74560]
+       Unnamed: 0          filename  latitude  longitude
+74560           0  74560_0_ini.yaml     40.15     -89.33
+getting all records of the whole batch
+determining the station and its chunk number according global_chunk_number (3)
+chunks_current_station 8
+station =  [74560]
+station chunk number: 3
+Fetching initial/forcing records
+starting station chunk number: 3(size: 50 soundings)
+starting 1 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.4 
+Warning: Ps input was provided (99334.38498263889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98008.8577826389Pa).
+run not succesfull
+starting 2 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.54256').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.54256 
+Warning: Ps input was provided (98974.41840277778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93374.47800277777Pa).
+run not succesfull
+starting 3 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.106783').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.106783 
+Warning: Ps input was provided (98773.94704861111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97569.67144861112Pa).
+run not succesfull
+starting 4 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.161041').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.161041 
+Warning: Ps input was provided (98655.97786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93577.53706458333Pa).
+run not succesfull
+starting 5 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.213499').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.213499 
+Warning: Ps input was provided (99006.80989583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98821.98949583333Pa).
+run not succesfull
+starting 6 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.268606').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.268606 
+Warning: Ps input was provided (98851.09939236111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96326.00539236111Pa).
+run not succesfull
+starting 7 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.321942').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.321942 
+Warning: Ps input was provided (98580.45746527778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97994.21186527779Pa).
+run not succesfull
+starting 8 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.376318').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.376318 
+Warning: Ps input was provided (98692.86935763889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98522.17535763889Pa).
+run not succesfull
+starting 9 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.429745').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.429745 
+Warning: Ps input was provided (97911.22092013889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (80203.77852013889Pa).
+run not succesfull
+starting 10 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.480238').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.480238 
+Warning: Ps input was provided (98075.484375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97173.749175Pa).
+run not succesfull
+starting 11 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.534353').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.534353 
+Warning: Ps input was provided (98681.65972222222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96007.06132222222Pa).
+run not succesfull
+starting 12 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.587556').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.587556 
+Warning: Ps input was provided (99226.03385416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98950.56905416667Pa).
+run not succesfull
+starting 13 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.642546').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.642546 
+Warning: Ps input was provided (99068.84461805556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97983.46621805556Pa).
+run not succesfull
+starting 14 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.696672').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.696672 
+Warning: Ps input was provided (98850.8203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86857.5067125Pa).
+run not succesfull
+starting 15 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.748004').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.748004 
+Warning: Ps input was provided (98882.85069444444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (80400.81069444443Pa).
+run not succesfull
+starting 16 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.798522').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.798522 
+Warning: Ps input was provided (98935.29166666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84607.59046666668Pa).
+run not succesfull
+starting 17 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.849366').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.849366 
+Warning: Ps input was provided (99346.84895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87599.57015833333Pa).
+run not succesfull
+starting 18 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.900431').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.900431 
+Warning: Ps input was provided (99613.65928819444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99492.40768819443Pa).
+run not succesfull
+starting 19 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.955333').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.955333 
+Warning: Ps input was provided (99327.306640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96987.033040625Pa).
+run not succesfull
+starting 20 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1008606').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1008606 
+Warning: Ps input was provided (99090.17664930556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97185.46704930556Pa).
+run not succesfull
+starting 21 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1062216').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1062216 
+Warning: Ps input was provided (98801.10199652778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96923.46799652778Pa).
+run not succesfull
+starting 22 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1116153').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1116153 
+Warning: Ps input was provided (98502.55295138889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97001.6229513889Pa).
+run not succesfull
+starting 23 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1169750').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1169750 
+Warning: Ps input was provided (98958.93836805556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83577.64316805557Pa).
+run not succesfull
+starting 24 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1220569').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1220569 
+Warning: Ps input was provided (99661.33854166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98757.24894166666Pa).
+run not succesfull
+starting 25 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1274715').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1274715 
+Warning: Ps input was provided (99208.20182291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85307.82422291668Pa).
+run not succesfull
+starting 26 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1325734').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1325734 
+Warning: Ps input was provided (99667.44791666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93415.33871666667Pa).
+run not succesfull
+starting 27 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1378235').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1378235 
+Warning: Ps input was provided (99869.86935763889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98178.23295763889Pa).
+run not succesfull
+starting 28 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1430796').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1430796 
+Warning: Ps input was provided (99770.04644097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97653.44084097222Pa).
+run not succesfull
+starting 29 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1484617').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1484617 
+Warning: Ps input was provided (99515.08116319444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99389.12076319444Pa).
+run not succesfull
+starting 30 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1539402').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1539402 
+Warning: Ps input was provided (99617.5703125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92184.7295125Pa).
+run not succesfull
+starting 31 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1591441').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1591441 
+Warning: Ps input was provided (99509.58333333333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98973.95733333332Pa).
+run not succesfull
+starting 32 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1645914').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1645914 
+Warning: Ps input was provided (99475.33116319444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93772.97436319444Pa).
+run not succesfull
+starting 33 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1698613').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1698613 
+Warning: Ps input was provided (99442.09157986111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95909.31437986111Pa).
+run not succesfull
+starting 34 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1751712').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1751712 
+Warning: Ps input was provided (99330.49479166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97372.81119166667Pa).
+run not succesfull
+starting 35 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1805578').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1805578 
+Warning: Ps input was provided (98861.93576388889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98717.1401638889Pa).
+run not succesfull
+starting 36 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1859984').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1859984 
+Warning: Ps input was provided (98695.76085069444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94772.15325069443Pa).
+run not succesfull
+starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
+  warnings.warn("Key '"+key+"' may not be implemented.")
+/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
+  self.L = zsl/self.zeta
+ 366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1912886').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1912886 
+Warning: Ps input was provided (99132.26996527778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96674.27636527778Pa).
+run not succesfull
+starting 38 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1966234').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1966234 
+Warning: Ps input was provided (99497.02994791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98510.53634791667Pa).
+run not succesfull
+starting 39 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2020399').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2020399 
+Warning: Ps input was provided (99728.96788194444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85688.50348194444Pa).
+run not succesfull
+starting 40 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2071191').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2071191 
+Warning: Ps input was provided (99652.02690972222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88797.06570972221Pa).
+run not succesfull
+starting 41 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2122985').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2122985 
+Warning: Ps input was provided (99688.40147569444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98187.47147569444Pa).
+run not succesfull
+starting 42 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2176760').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2176760 
+Warning: Ps input was provided (99471.74782986111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98455.82422986111Pa).
+run not succesfull
+starting 43 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2230944').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2230944 
+Warning: Ps input was provided (99348.51519097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96937.60959097222Pa).
+run not succesfull
+starting 44 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2284495').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2284495 
+Warning: Ps input was provided (99275.59982638889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92649.1410263889Pa).
+run not succesfull
+starting 45 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2337035').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2337035 
+Warning: Ps input was provided (99152.58463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96658.09783541667Pa).
+run not succesfull
+starting 46 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2390194').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2390194 
+Warning: Ps input was provided (98868.35069444444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97950.13469444444Pa).
+run not succesfull
+starting 47 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2444461').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2444461 
+Warning: Ps input was provided (99050.93793402778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96213.88593402778Pa).
+run not succesfull
+starting 48 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2497851').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2497851 
+Warning: Ps input was provided (99175.79644097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97772.57404097222Pa).
+run not succesfull
+starting 49 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2551916').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2551916 
+Warning: Ps input was provided (99178.48090277778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98734.67650277779Pa).
+run not succesfull
+starting 50 out of 50 (station total:  366 )
+ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2606609').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2606609 
+Warning: Ps input was provided (98840.40798611111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94689.60078611111Pa).
+run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-4 b/class4gl/c4gl_sim.o4759326-4
new file mode 100644
index 0000000..1dfedff
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-4
@@ -0,0 +1,10 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-5 b/class4gl/c4gl_sim.o4759326-5
new file mode 100644
index 0000000..6d9339a
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-5
@@ -0,0 +1,10 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-6 b/class4gl/c4gl_sim.o4759326-6
new file mode 100644
index 0000000..d48e632
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-6
@@ -0,0 +1,10 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-7 b/class4gl/c4gl_sim.o4759326-7
new file mode 100644
index 0000000..5976742
--- /dev/null
+++ b/class4gl/c4gl_sim.o4759326-7
@@ -0,0 +1,10 @@
+C4GLJOB_c4gl_path_lib
+C4GLJOB_exec
+C4GLJOB_experiments
+C4GLJOB_path_experiments
+C4GLJOB_path_forcing
+C4GLJOB_runtime
+C4GLJOB_split_by
+C4GLJOB_station_id
+C4GLJOB_subset_forcing
+Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 1ed64f8..a3c8cc8 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -116,7 +116,7 @@ class class4gl_input(object):
     #    class4gl_input = type('class4gl_input', (model_input,gl_input,gl_dia), dict(c='c'))
     """
 
-    def __init__(self,set_pars_defaults=True,debug_level=None):
+    def __init__(self,set_pars_defaults=True,debug_level=logging.WARNING):
 
         """ set up logger (see: https://docs.python.org/2/howto/logging.html)
         """
@@ -125,26 +125,26 @@ def __init__(self,set_pars_defaults=True,debug_level=None):
         if debug_level is not None:
             self.logger.setLevel(debug_level)
 
-        # # create logger
-        # self.logger = logging.getLogger('class4gl_input')
-        # self.logger.setLevel(debug_level)
-
-        # # create console handler and set level to debug
-        # ch = logging.StreamHandler()
-        # ch.setLevel(debug_level)
-
-        # # create formatter
-        # formatter = logging.Formatter('%(asctime)s - \
-        #                                %(name)s - \
-        #                                %(levelname)s - \
-        #                                %(message)s')
-        # add formatter to ch
-        # ch.setFormatter(formatter)
+            # # create logger
+            # #self.logger = logging.getLogger('class4gl_input')
+            # #self.logger.setLevel(debug_level)
+
+            # # create console handler and set level to debug
+            # ch = logging.StreamHandler()
+            # ch.setLevel(debug_level)
+
+            # # create formatter
+            # formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+            # # add formatter to ch
+            # ch.setFormatter(formatter)
      
-        # # add ch to logger
-        # self.logger.addHandler(ch)
+            # # add ch to logger
+            # self.logger.addHandler(ch)
+            # # print("TESTTESTSETSTETETS")
+            # # self.logger.warning("testsetsetsttets")
+            # #stop
 
-        # """ end set up logger """
+            # # """ end set up logger """
 
 
 
@@ -1321,6 +1321,7 @@ def check_source(self,source,check_only_sections=None):
                     # self.logger.info('Expected key '+datakey+\
                     #                  ' is not in parameter input')                        
                     source_ok = False                                           
+                    print(datakey)
                 elif (checkdata[datakey] is None) or \
                      (pd.isnull(checkdata[datakey]) is True):                    
         
@@ -1328,6 +1329,7 @@ def check_source(self,source,check_only_sections=None):
                     #                  '" is invalid: ('+ \
                     # str(self.__dict__[section].__dict__[datakey])+')')         
                     source_ok = False
+                    self.logger.warning(datakey+' is invalid: '+ str(checkdata[datakey]))
 
         return source_ok
 
@@ -1346,13 +1348,13 @@ def check_source_globaldata(self):
         # we only allow non-polar stations
         if not (self.pars.lat <= 60.):
             source_globaldata_ok = False
-            self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+            self.logger.warning('cveg  is invalid: ('+str(self.pars.cveg)+')')
         
         # check lat and lon
         if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
             source_globaldata_ok = False
-            self.logger.info('lat  is invalid: ('+str(self.pars.lat)+')')
-            self.logger.info('or lon  is invalid: ('+str(self.pars.lon)+')')
+            self.logger.warning('lat  is invalid: ('+str(self.pars.lat)+')')
+            self.logger.warning('or lon  is invalid: ('+str(self.pars.lon)+')')
         else:
             # we only check the ground parameter data (pars section). The 
             # profile data (air_ap section) are supposed to be valid in any 
diff --git a/class4gl/interface/interface_cloudiness.py b/class4gl/interface/interface_cloudiness.py
index 7dded65..82afd1e 100644
--- a/class4gl/interface/interface_cloudiness.py
+++ b/class4gl/interface/interface_cloudiness.py
@@ -127,7 +127,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False
                     )
-    '''
+    
+'''
 if args.make_figures:
     # the lines below activate TaylorPlots but it is disabled for now
     fig = plt.figure(figsize=(10,7))   #width,height
@@ -347,7 +348,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
         sns.set_style('whitegrid')
         #sns.set()
-        fig = pl.figure(figsize=(7,5))
+        fig = pl.figure(figsize=(11,7))
         i = 1
         axes = {}
         data_all = pd.DataFrame()
@@ -493,6 +494,17 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                     ax.set_yticklabels([])
                     ax.set_ylabel('')
 
+                if varkey == 'q':
+                    ticks = ticker.FuncFormatter(lambda x, pos:
+                                                 '{0:g}'.format(x*1000.))
+                    #ax.xaxis.set_major_formatter(ticks)
+                    ax.yaxis.set_major_formatter(ticks)
+
+                    ax.set_ylabel(latex['d'+varkey+'dt']+' ['+r'$10^{-3} \times $'+units['d'+varkey+'dt']+']')        
+                else:
+                    ax.set_ylabel(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')        
+
+
                 for j,artist in enumerate(ax.artists):
                     if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
                         # Set the linecolor on the artist to the facecolor, and set the facecolor to None
@@ -531,7 +543,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         fig.tight_layout()
         fig.subplots_adjust( bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
         if args.figure_filename_2 is not None:
-            fig.savefig(args.figure_filename_2,dpi=250); print("Image file written to:", args.figure_filename_2)
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
         fig.show()
 
 
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index 0054c20..9a71bf8 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -1,4 +1,4 @@
-'''
+
 import numpy as np
 
 import pandas as pd
@@ -41,7 +41,6 @@
 # importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
 
 
-
 if args.experiments_labels is None:
     keylabels = args.experiments.strip().split(' ')
 else:
@@ -128,7 +127,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False
                     )
-'''
+
+
 key = args.experiments.strip(' ').split(' ')[0]
 xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
 koeppenlookuptable = pd.DataFrame()
@@ -195,7 +195,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 if args.make_figures:
     # the lines below activate TaylorPlots but it is disabled for now
-    fig = plt.figure(figsize=(7,5))   #width,height
+    fig = plt.figure(figsize=(11,7))   #width,height
     i = 1                                                                           
     axes = {}         
     axes_taylor = {}         
@@ -401,12 +401,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                            'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] 
 
 
-        ann = axes[varkey].annotate(annotate_text, xy=(0.05, .95 ), xycoords='axes fraction',fontsize=7,
+        ann = axes[varkey].annotate(annotate_text, xy=(0.05, .95 ), xycoords='axes fraction',fontsize=8,
        horizontalalignment='left', verticalalignment='top' 
         )
 
 
-
         axes[varkey].set_xlabel('observations')     
         if i==0:                                    
             axes[varkey].set_ylabel('model')                                            
@@ -471,7 +470,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
     
     if args.figure_filename is not None:
-        fig.savefig(args.figure_filename,dpi=250); print("Image file written to:",args.figure_filename)
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
     fig.show()  
 
     if bool(args.show_control_parameters):
@@ -501,7 +500,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
         sns.set_style('whitegrid')
         #sns.set()
-        fig = pl.figure(figsize=(7,5))
+        fig = pl.figure(figsize=(11,7))
         i = 1
         axes = {}
         data_all = pd.DataFrame()
@@ -617,6 +616,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             data_all = data_all[select_data]
             #print('hello12')
             data_input = data_input[select_data.values]
+
+            data_input = data_input[data_all.KGCname.isin(list(koeppenlookuptable.KGCID))]
+            data_all = data_all[data_all.KGCname.isin(list(koeppenlookuptable.KGCID))]
             #print('hello13')
             #print(data_input.shape)
             #print(data_all.shape)
@@ -638,7 +640,24 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                  ax.get_legend().set_visible(False)
             #     plt.legend('off')
             if i >= 3:
-                ax.set_xticklabels(labels=ax.get_xticklabels())
+                idx = 0
+                for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+
+                    ax.annotate(koeppen.KGCID,
+                                xy=((idx+.5)/len(koeppenlookuptable),-0.00),
+                                color=koeppen.textcolor, 
+                                xycoords='axes fraction',
+                                weight='bold',
+                                fontsize=8.,
+                                horizontalalignment='center',
+                                verticalalignment='center' ,
+                                bbox={'edgecolor':'black',
+                                      'boxstyle':'circle',
+                                      'fc':koeppen.color,
+                                      'alpha':1.0}
+                               )
+                    idx+=1
+                ax.set_xticklabels([])#labels=ax.get_xticklabels())
                 ax.set_xlabel('Köppen climate class')
             else:
                 ax.set_xticklabels([])
@@ -646,6 +665,18 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
             # ax.set_yticklabels([])
             # ax.set_ylabel('')
+            if varkey == 'q':
+                ticks = ticker.FuncFormatter(lambda x, pos:
+                                             '{0:g}'.format(x*1000.))
+                #ax.xaxis.set_major_formatter(ticks)
+                ax.yaxis.set_major_formatter(ticks)
+
+                ax.set_ylabel(latex['d'+varkey+'dt']+' ['+r'$10^{-3} \times $'+units['d'+varkey+'dt']+']')        
+            else:
+                ax.set_ylabel(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')        
+
+
+
 
             for j,artist in enumerate(ax.artists):
                 if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
@@ -679,13 +710,14 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 
 
+
             #ax.grid()
             #sns.despine(offset=10, trim=True)
             i +=1
         fig.tight_layout()
         fig.subplots_adjust( bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
         if args.figure_filename_2 is not None:
-            fig.savefig(args.figure_filename_2,dpi=250); print("Image file written to:", args.figure_filename_2)
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
         fig.show()
 
 
diff --git a/class4gl/model.py b/class4gl/model.py
index 6a9d2c3..3993200 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -376,16 +376,23 @@ def init(self):
                                  layer height needs to be equal to the second \
                                  and third \
                                  level of the vertical profile input!")
-            # initialize q from its profile when available
-            p_old = self.Ps
-            p_new = self.air_ap.p[indexh[0][0]]
-            
-            if ((p_old is not None) & (p_old != p_new)):
-                print("Warning: Ps input was provided ("+str(p_old)+\
-                    "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
-                    +str(p_new)+"Pa).")
-                                    
-            self.Ps = p_new
+
+            # # initialize q from its profile when available
+            # p_old = self.Ps
+            # p_new = self.air_ap.p[indexh[0][0]]
+            # print(indexh)
+            # #stop
+            # 
+            # if ((p_old is not None) & (p_old != p_new)):
+            #     print("Warning: Ps input was provided ("+str(p_old)+\
+            #         "Pa), but it is now overwritten by the first level (index 0) of p_pro which is different ("\
+            #         +str(p_new)+"Pa).")
+            #                         
+            # self.Ps = p_new
+
+
+
+
             # these variables/namings are more convenient to work with in the code
             # we will update the original variables afterwards
             #self.air_ap['q'] = self.air_ap.QABS/1000.
@@ -1534,7 +1541,11 @@ def jarvis_stewart(self):
         f3 = 1. / np.exp(- self.gD * (self.esat - self.e) / 100.)
         f4 = 1./ (1. - 0.0016 * (298.0-self.theta)**2.)
   
+        #if np.isnan(self.LAI):
+
         self.rs = self.rsmin / self.LAI * f1 * f2 * f3 * f4
+        # print(self.rs,self.LAI,f1,f2,f3,f4)
+        # stop
 
     def factorial(self,k):
         factorial = 1
@@ -1641,8 +1652,8 @@ def run_land_surface(self):
           self.ra = (self.Cs * ueff)**-1.
         else:
           self.ra = ueff / max(1.e-3, self.ustar)**2.
+        # print(self.ra,self.Cs,ueff)
 
-        #print('ra',self.ra,self.ustar,ueff)
 
         # first calculate essential thermodynamic variables
         self.esat    = esat(self.theta)
@@ -1677,9 +1688,10 @@ def run_land_surface(self):
             / (self.rho * self.cp / self.ra + self.cveg * (1. - self.cliq) * self.rho * self.Lv / (self.ra + self.rs) * self.dqsatdT \
             + (1. - self.cveg) * self.rho * self.Lv / (self.ra + self.rssoil) * self.dqsatdT + self.cveg * self.cliq * self.rho * self.Lv / self.ra * self.dqsatdT + self.Lambda)
 
-        #print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
-        #print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
-        #print('Ts',self.rs)
+        # print('Ts',self.Ts,self.Q,self.rho,self.cp,self.ra,self.theta)
+        # print('Ts',self.cveg, self.cliq,self.Lv,self.Lambda,self.dqsatdT)
+        # print('Ts',self.rs)
+        #print(self.air_ap.p)
 
         esatsurf      = esat(self.Ts)
         self.qsatsurf = qsat(self.Ts, self.Ps)
@@ -1692,7 +1704,11 @@ def run_land_surface(self):
   
         self.LE     = self.LEsoil + self.LEveg + self.LEliq
         self.H      = self.rho * self.cp / self.ra * (self.Ts - self.theta)
-        #print('H',self.ra,self.Ts,self.theta)
+
+        # print('ra',self.ra,self.ustar,ueff)
+        # print(self.Cs)
+        # print('H',self.ra,self.Ts,self.theta)
+
         self.G      = self.Lambda * (self.Ts - self.Tsoil)
         self.LEpot  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv)
         self.LEref  = (self.dqsatdT * (self.Q - self.G) + self.rho * self.cp / self.ra * (self.qsat - self.q)) / (self.dqsatdT + self.cp / self.Lv * (1. + self.rsmin / self.LAI / self.ra))
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 114d23a..7b02734 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -272,8 +272,14 @@
 
                     if args.error_handling == 'dump_always':
                         try:
-                            c4gl.run()
-                            print('run succesfull')
+                            if c4gli_morning.check_source_globaldata():
+                            
+
+                                c4gl.run()
+                                print('run succesfull')
+                            else:
+                                print('global data not ok')
+                                raise ValueError('global data not ok')
                         except:
                             print('run not succesfull')
                         onerun = True
@@ -289,9 +295,13 @@
                     # in this case, only the file will dumped if the runs were
                     # successful
                     elif args.error_handling == 'dump_on_success':
-                        try:
-                            c4gl.run()
-                            print('run succesfull')
+                       try:
+                            if c4gli_morning.check_source_globaldata():
+                                c4gl.run()
+                                print('run succesfull')
+                            else:
+                                print('global data not ok')
+                                raise ValueError('global data not ok')
                             c4gli_morning.dump(file_ini)
                             
                             
@@ -300,8 +310,8 @@
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                        except:
-                            print('run not succesfull')
+                       except:
+                           print('run not succesfull')
                     isim += 1
 
 

From b82d0021f067ec7c678868d6378159620cd13ba2 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 18 Sep 2018 15:09:06 +0200
Subject: [PATCH 074/129] Fix Ps input; implement warning message in case of
 invalid input

---
 class4gl/c4gl_sim.o4759326-0 | 227 ----------------------------------
 class4gl/c4gl_sim.o4759326-1 | 231 -----------------------------------
 class4gl/c4gl_sim.o4759326-2 | 227 ----------------------------------
 class4gl/c4gl_sim.o4759326-3 | 227 ----------------------------------
 class4gl/c4gl_sim.o4759326-4 |  10 --
 class4gl/c4gl_sim.o4759326-5 |  10 --
 class4gl/c4gl_sim.o4759326-6 |  10 --
 class4gl/c4gl_sim.o4759326-7 |  10 --
 8 files changed, 952 deletions(-)
 delete mode 100644 class4gl/c4gl_sim.o4759326-0
 delete mode 100644 class4gl/c4gl_sim.o4759326-1
 delete mode 100644 class4gl/c4gl_sim.o4759326-2
 delete mode 100644 class4gl/c4gl_sim.o4759326-3
 delete mode 100644 class4gl/c4gl_sim.o4759326-4
 delete mode 100644 class4gl/c4gl_sim.o4759326-5
 delete mode 100644 class4gl/c4gl_sim.o4759326-6
 delete mode 100644 class4gl/c4gl_sim.o4759326-7

diff --git a/class4gl/c4gl_sim.o4759326-0 b/class4gl/c4gl_sim.o4759326-0
deleted file mode 100644
index f8ba869..0000000
--- a/class4gl/c4gl_sim.o4759326-0
+++ /dev/null
@@ -1,227 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-       Unnamed: 0          filename  latitude  longitude
-74560           0  74560_0_ini.yaml     40.15     -89.33
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (0)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 0
-Fetching initial/forcing records
-starting station chunk number: 0(size: 50 soundings)
-starting 1 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.4 
-Warning: Ps input was provided (99384.96484375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91367.05564375Pa).
-run not succesfull
-starting 2 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.51396').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.51396 
-Warning: Ps input was provided (100124.83463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89846.70143541667Pa).
-run not succesfull
-starting 3 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.102622').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.102622 
-Warning: Ps input was provided (99980.35416666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96094.41696666667Pa).
-run not succesfull
-starting 4 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.155319').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.155319 
-Warning: Ps input was provided (101228.97786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94990.99506458333Pa).
-run not succesfull
-starting 5 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.207321').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.207321 
-Warning: Ps input was provided (100561.96223958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99736.74503958333Pa).
-run not succesfull
-starting 6 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.261499').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.261499 
-Warning: Ps input was provided (98617.18880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89281.99280208333Pa).
-run not succesfull
-starting 7 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.312096').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.312096 
-Warning: Ps input was provided (99861.65104166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91664.80744166666Pa).
-run not succesfull
-starting 8 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.363706').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.363706 
-Warning: Ps input was provided (99783.63932291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96377.99972291668Pa).
-run not succesfull
-starting 9 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.416568').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.416568 
-Warning: Ps input was provided (100101.34765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98246.08045625Pa).
-run not succesfull
-starting 10 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.470247').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.470247 
-Warning: Ps input was provided (101217.34895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100305.01895833333Pa).
-run not succesfull
-starting 11 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.524351').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.524351 
-Warning: Ps input was provided (101140.765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99524.470025Pa).
-run not succesfull
-starting 12 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.577126').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.577126 
-Warning: Ps input was provided (99971.93880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94347.27720208332Pa).
-run not succesfull
-starting 13 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.629651').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.629651 
-Warning: Ps input was provided (98563.27864583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93396.54784583332Pa).
-run not succesfull
-starting 14 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.682101').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.682101 
-Warning: Ps input was provided (98789.33203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97675.70083125Pa).
-run not succesfull
-starting 15 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.734044').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.734044 
-Warning: Ps input was provided (99563.50390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95163.13030625Pa).
-run not succesfull
-starting 16 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.786398').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.786398 
-Warning: Ps input was provided (100669.421875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95027.102275Pa).
-run not succesfull
-starting 17 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.838710').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.838710 
-Warning: Ps input was provided (100948.26692708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96705.63812708332Pa).
-run not succesfull
-starting 18 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.891166').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.891166 
-Warning: Ps input was provided (100156.60026041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92800.27746041668Pa).
-run not succesfull
-starting 19 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.943327').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.943327 
-Warning: Ps input was provided (99349.36979166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93320.92859166667Pa).
-run not succesfull
-starting 20 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.995731').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.995731 
-Warning: Ps input was provided (99228.83203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98584.90363125Pa).
-run not succesfull
-starting 21 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1050024').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1050024 
-Warning: Ps input was provided (99770.28515625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96156.28115625Pa).
-run not succesfull
-starting 22 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1102851').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1102851 
-Warning: Ps input was provided (99380.921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94221.254275Pa).
-run not succesfull
-starting 23 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1154781').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1154781 
-Warning: Ps input was provided (99095.53255208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98209.10095208333Pa).
-run not succesfull
-starting 24 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1208836').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1208836 
-Warning: Ps input was provided (98733.72786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98616.00786458333Pa).
-run not succesfull
-starting 25 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1263823').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1263823 
-Warning: Ps input was provided (97895.48697916667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97777.76697916667Pa).
-run not succesfull
-starting 26 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1318780').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1318780 
-Warning: Ps input was provided (98477.62369791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (78494.65369791667Pa).
-run not succesfull
-starting 27 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1368688').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1368688 
-Warning: Ps input was provided (98800.078125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93290.782125Pa).
-run not succesfull
-starting 28 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1420916').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1420916 
-Warning: Ps input was provided (99248.63541666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90668.02461666668Pa).
-run not succesfull
-starting 29 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1472567').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1472567 
-Warning: Ps input was provided (100427.43880208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96005.87560208332Pa).
-run not succesfull
-starting 30 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1524967').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1524967 
-Warning: Ps input was provided (101123.49348958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90677.02068958333Pa).
-run not succesfull
-starting 31 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1576324').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1576324 
-Warning: Ps input was provided (99848.95182291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95586.31062291667Pa).
-run not succesfull
-starting 32 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1629313').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1629313 
-Warning: Ps input was provided (97864.29296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87103.50776875Pa).
-run not succesfull
-starting 33 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1680371').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1680371 
-Warning: Ps input was provided (99503.52864583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87630.28944583333Pa).
-run not succesfull
-starting 34 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1731186').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1731186 
-Warning: Ps input was provided (100169.15364583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86911.52724583333Pa).
-run not succesfull
-starting 35 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1782240').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1782240 
-Warning: Ps input was provided (100522.32291666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88935.14331666667Pa).
-run not succesfull
-starting 36 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1833412').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1833412 
-Warning: Ps input was provided (100123.75260416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96482.67300416667Pa).
-run not succesfull
-starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
-  warnings.warn("Key '"+key+"' may not be implemented.")
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
-  self.L = zsl/self.zeta
- 366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1886384').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1886384 
-Warning: Ps input was provided (99870.125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89217.6422Pa).
-run not succesfull
-starting 38 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1937565').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1937565 
-Warning: Ps input was provided (98704.2734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86112.9422375Pa).
-run not succesfull
-starting 39 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1988408').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1988408 
-Warning: Ps input was provided (99314.90885416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (81565.08725416668Pa).
-run not succesfull
-starting 40 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2038456').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2038456 
-Warning: Ps input was provided (99467.91145833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84809.41705833333Pa).
-run not succesfull
-starting 41 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2089432').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2089432 
-Warning: Ps input was provided (97532.21354166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91573.22714166668Pa).
-run not succesfull
-starting 42 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2141348').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2141348 
-Warning: Ps input was provided (100356.95442708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92337.86802708333Pa).
-run not succesfull
-starting 43 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2192964').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2192964 
-Warning: Ps input was provided (101625.39453125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101236.91853125Pa).
-run not succesfull
-starting 44 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2247428').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2247428 
-Warning: Ps input was provided (101831.296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101700.627675Pa).
-run not succesfull
-starting 45 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2302316').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2302316 
-Warning: Ps input was provided (101392.83463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (101039.67463541667Pa).
-run not succesfull
-starting 46 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2356848').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2356848 
-Warning: Ps input was provided (100442.67317708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99184.24637708333Pa).
-run not succesfull
-starting 47 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2411058').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2411058 
-Warning: Ps input was provided (99915.86848958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88082.65408958333Pa).
-run not succesfull
-starting 48 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2462275').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2462275 
-Warning: Ps input was provided (99762.47265625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97849.52265625Pa).
-run not succesfull
-starting 49 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2516002').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2516002 
-Warning: Ps input was provided (99270.4765625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93171.4033625Pa).
-run not succesfull
-starting 50 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.2568449').to_json" > /local/4759326[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.2568449 
-Warning: Ps input was provided (98692.77994791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97569.73114791667Pa).
-run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-1 b/class4gl/c4gl_sim.o4759326-1
deleted file mode 100644
index 87274a1..0000000
--- a/class4gl/c4gl_sim.o4759326-1
+++ /dev/null
@@ -1,231 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-       Unnamed: 0          filename  latitude  longitude
-74560           0  74560_0_ini.yaml     40.15     -89.33
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (1)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 1
-Fetching initial/forcing records
-starting station chunk number: 1(size: 50 soundings)
-starting 1 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.4 
-Warning: Ps input was provided (99189.77604166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97712.39004166667Pa).
-run not succesfull
-starting 2 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.53735').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.53735 
-Warning: Ps input was provided (98917.74609375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96435.03129375Pa).
-run not succesfull
-starting 3 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.107051').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.107051 
-Warning: Ps input was provided (97149.79296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93409.82856875Pa).
-run not succesfull
-starting 4 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.159809').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.159809 
-Warning: Ps input was provided (97484.921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (82631.012275Pa).
-run not succesfull
-starting 5 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.210583').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.210583 
-Warning: Ps input was provided (99121.34895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93010.50375833333Pa).
-run not succesfull
-starting 6 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.262669').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.262669 
-Warning: Ps input was provided (99433.81119791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97557.35439791667Pa).
-run not succesfull
-starting 7 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.315998').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.315998 
-Warning: Ps input was provided (100124.39713541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95708.71993541667Pa).
-run not succesfull
-starting 8 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.368733').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.368733 
-Warning: Ps input was provided (99289.96223958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96497.64383958332Pa).
-run not succesfull
-starting 9 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.422182').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.422182 
-Warning: Ps input was provided (99191.98567708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90040.43287708332Pa).
-run not succesfull
-starting 10 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.473690').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.473690 
-Warning: Ps input was provided (99402.77604166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90833.93724166667Pa).
-run not succesfull
-starting 11 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.525232').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.525232 
-Warning: Ps input was provided (99445.84114583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96549.92914583333Pa).
-run not succesfull
-starting 12 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.578431').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.578431 
-Warning: Ps input was provided (99561.35026041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99443.63026041667Pa).
-run not succesfull
-starting 13 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.633046').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.633046 
-Warning: Ps input was provided (98115.078125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97646.552525Pa).
-run not succesfull
-starting 14 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.686127').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.686127 
-Warning: Ps input was provided (98767.19401041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93118.98841041667Pa).
-run not succesfull
-starting 15 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.738483').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.738483 
-Warning: Ps input was provided (99317.18489583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94815.57209583333Pa).
-run not succesfull
-starting 16 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.790644').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.790644 
-Warning: Ps input was provided (100321.02083333333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100132.66883333333Pa).
-run not succesfull
-starting 17 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.844792').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.844792 
-Warning: Ps input was provided (100697.07291666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100287.40731666668Pa).
-run not succesfull
-starting 18 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.897654').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.897654 
-Warning: Ps input was provided (100432.74088541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99341.47648541667Pa).
-run not succesfull
-starting 19 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.950524').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.950524 
-Warning: Ps input was provided (100163.28125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99861.91805Pa).
-run not succesfull
-starting 20 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1003819').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1003819 
-Warning: Ps input was provided (100273.30598958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98120.20718958332Pa).
-run not succesfull
-starting 21 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1057080').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1057080 
-Warning: Ps input was provided (99064.4140625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91138.3264625Pa).
-run not succesfull
-starting 22 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1109138').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1109138 
-Warning: Ps input was provided (99177.81380208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96793.98380208333Pa).
-run not succesfull
-starting 23 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1162278').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1162278 
-Warning: Ps input was provided (99862.77734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99573.18614375Pa).
-run not succesfull
-starting 24 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1216404').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1216404 
-Warning: Ps input was provided (98215.37630208333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88103.22830208333Pa).
-run not succesfull
-starting 25 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1268030').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1268030 
-Warning: Ps input was provided (99520.6640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98864.9636625Pa).
-run not succesfull
-starting 26 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1320921').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1320921 
-Warning: Ps input was provided (97935.72135416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90289.80735416667Pa).
-run not succesfull
-starting 27 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1372574').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1372574 
-Warning: Ps input was provided (98352.68619791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92378.39619791668Pa).
-run not succesfull
-starting 28 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1424704').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1424704 
-Warning: Ps input was provided (98539.91276041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92226.58916041667Pa).
-run not succesfull
-starting 29 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1476503').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1476503 
-Warning: Ps input was provided (98725.5390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93864.8802625Pa).
-run not succesfull
-starting 30 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1528706').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1528706 
-Warning: Ps input was provided (98957.06510416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98042.38070416667Pa).
-run not succesfull
-starting 31 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1583120').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1583120 
-Warning: Ps input was provided (99557.94270833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93847.34550833332Pa).
-run not succesfull
-starting 32 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1635666').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1635666 
-Warning: Ps input was provided (99887.29296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98538.22176875Pa).
-run not succesfull
-starting 33 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1689323').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1689323 
-Warning: Ps input was provided (99869.75911458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98305.26031458333Pa).
-run not succesfull
-starting 34 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1741836').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1741836 
-Warning: Ps input was provided (100003.671875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98346.174275Pa).
-run not succesfull
-starting 35 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1795229').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1795229 
-Warning: Ps input was provided (99218.69921875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91785.85841875Pa).
-run not succesfull
-starting 36 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1847312').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1847312 
-Warning: Ps input was provided (100100.86328125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96563.37728125Pa).
-run not succesfull
-starting 37 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1900220').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1900220 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
-  warnings.warn("Key '"+key+"' may not be implemented.")
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
-  self.L = zsl/self.zeta
-
-Warning: Ps input was provided (99535.61067708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98012.31387708333Pa).
-run not succesfull
-starting 38 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1954053').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1954053 
-Warning: Ps input was provided (98121.40234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86747.29594375Pa).
-run not succesfull
-starting 39 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2005224').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2005224 
-Warning: Ps input was provided (98072.640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (76835.952625Pa).
-run not succesfull
-starting 40 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2055085').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2055085 
-Warning: Ps input was provided (98379.67578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97726.32978125Pa).
-run not succesfull
-starting 41 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2109238').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2109238 
-Warning: Ps input was provided (98887.31901041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (71017.10901041667Pa).
-run not succesfull
-starting 42 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2158379').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2158379 
-Warning: Ps input was provided (99447.98372395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99166.63292395833Pa).
-run not succesfull
-starting 43 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2213439').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2213439 
-Warning: Ps input was provided (98189.0390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89527.2014625Pa).
-run not succesfull
-starting 44 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2265091').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2265091 
-Warning: Ps input was provided (97722.154296875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (49966.681896875Pa).
-LCL calculation not converged!!
-RHlcl = -0.000035, zlcl=7775135.081090, theta=296.584021, q=0.004835
-LCL calculation not converged!!
-RHlcl = -0.000035, zlcl=7775135.081090, theta=296.584021, q=0.004835
-run not succesfull
-starting 45 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2312591').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2312591 
-Warning: Ps input was provided (99512.51041666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83614.42441666668Pa).
-run not succesfull
-starting 46 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2363001').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2363001 
-Warning: Ps input was provided (100458.30859375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (100280.55139375Pa).
-run not succesfull
-starting 47 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2417249').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2417249 
-Warning: Ps input was provided (99404.5234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99218.5258375Pa).
-run not succesfull
-starting 48 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2472181').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2472181 
-Warning: Ps input was provided (98729.45247395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (90835.14927395833Pa).
-run not succesfull
-starting 49 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2523821').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2523821 
-Warning: Ps input was provided (99818.05989583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97451.88789583332Pa).
-run not succesfull
-starting 50 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.2576934').to_json" > /local/4759326[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.2576934 
-Warning: Ps input was provided (99569.44205729167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97954.32365729166Pa).
-run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-2 b/class4gl/c4gl_sim.o4759326-2
deleted file mode 100644
index 6c90b67..0000000
--- a/class4gl/c4gl_sim.o4759326-2
+++ /dev/null
@@ -1,227 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-       Unnamed: 0          filename  latitude  longitude
-74560           0  74560_0_ini.yaml     40.15     -89.33
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (2)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 2
-Fetching initial/forcing records
-starting station chunk number: 2(size: 50 soundings)
-starting 1 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.4 
-Warning: Ps input was provided (99219.09375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87069.21255Pa).
-run not succesfull
-starting 2 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.51067').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.51067 
-Warning: Ps input was provided (99111.41341145833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96887.68261145832Pa).
-run not succesfull
-starting 3 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.104322').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.104322 
-Warning: Ps input was provided (99240.26953125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97729.92193125Pa).
-run not succesfull
-starting 4 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.158125').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.158125 
-Warning: Ps input was provided (100401.01627604167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94004.11147604167Pa).
-run not succesfull
-starting 5 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.210001').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.210001 
-Warning: Ps input was provided (101597.61653645833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98645.19893645833Pa).
-run not succesfull
-starting 6 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.263270').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.263270 
-Warning: Ps input was provided (100343.29231770833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99933.62671770834Pa).
-run not succesfull
-starting 7 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.317457').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.317457 
-Warning: Ps input was provided (99029.62109375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83470.56869375Pa).
-run not succesfull
-starting 8 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.368268').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.368268 
-Warning: Ps input was provided (100035.25390625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99644.42350625Pa).
-run not succesfull
-starting 9 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.422781').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.422781 
-Warning: Ps input was provided (99447.54231770833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96552.80751770832Pa).
-run not succesfull
-starting 10 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.476050').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.476050 
-Warning: Ps input was provided (100032.83854166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92228.00254166668Pa).
-run not succesfull
-starting 11 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.528316').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.528316 
-Warning: Ps input was provided (99849.60807291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94654.62447291666Pa).
-run not succesfull
-starting 12 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.580931').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.580931 
-Warning: Ps input was provided (98739.5625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98586.5265Pa).
-run not succesfull
-starting 13 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.635773').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.635773 
-Warning: Ps input was provided (98331.50260416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (82513.46620416667Pa).
-run not succesfull
-starting 14 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.686190').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.686190 
-Warning: Ps input was provided (98651.11783854167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85587.72943854168Pa).
-run not succesfull
-starting 15 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.736979').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.736979 
-Warning: Ps input was provided (99153.72395833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98759.36195833333Pa).
-run not succesfull
-starting 16 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.791508').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.791508 
-Warning: Ps input was provided (98915.3125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95224.7905Pa).
-run not succesfull
-starting 17 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.844468').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.844468 
-Warning: Ps input was provided (99249.35872395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98826.74392395833Pa).
-run not succesfull
-starting 18 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.899058').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.899058 
-Warning: Ps input was provided (98736.89192708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87358.07672708333Pa).
-run not succesfull
-starting 19 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.950395').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.950395 
-Warning: Ps input was provided (98866.00325520833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89488.42805520832Pa).
-run not succesfull
-starting 20 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1001879').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1001879 
-Warning: Ps input was provided (98843.9375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94478.8799Pa).
-run not succesfull
-starting 21 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1054711').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1054711 
-Warning: Ps input was provided (99160.09635416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95736.79875416667Pa).
-run not succesfull
-starting 22 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1107591').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1107591 
-Warning: Ps input was provided (99728.29817708333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98725.32377708332Pa).
-run not succesfull
-starting 23 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1161498').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1161498 
-Warning: Ps input was provided (99118.85416666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98987.00776666668Pa).
-run not succesfull
-starting 24 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1216461').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1216461 
-Warning: Ps input was provided (99008.45247395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97757.08887395833Pa).
-run not succesfull
-starting 25 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1270638').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1270638 
-Warning: Ps input was provided (99078.38802083333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93394.86642083334Pa).
-run not succesfull
-starting 26 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1323032').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1323032 
-Warning: Ps input was provided (99683.82486979167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91330.41366979167Pa).
-run not succesfull
-starting 27 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1374748').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1374748 
-Warning: Ps input was provided (99829.79166666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89606.98686666667Pa).
-run not succesfull
-starting 28 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1426449').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1426449 
-Warning: Ps input was provided (99290.98893229167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95774.69253229166Pa).
-run not succesfull
-starting 29 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1479418').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1479418 
-Warning: Ps input was provided (99007.84830729167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98137.89750729167Pa).
-run not succesfull
-starting 30 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1533453').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1533453 
-Warning: Ps input was provided (98365.1796875Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (91098.3240875Pa).
-run not succesfull
-starting 31 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1585360').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1585360 
-Warning: Ps input was provided (98840.94791666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98285.30951666668Pa).
-run not succesfull
-starting 32 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1638642').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1638642 
-Warning: Ps input was provided (99649.287109375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99080.699509375Pa).
-run not succesfull
-starting 33 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1693282').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1693282 
-Warning: Ps input was provided (99164.91276041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97164.84996041667Pa).
-run not succesfull
-starting 34 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1746969').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1746969 
-Warning: Ps input was provided (98023.13151041667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (89570.83551041667Pa).
-run not succesfull
-starting 35 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1798551').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1798551 
-Warning: Ps input was provided (99066.55598958333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96748.64918958333Pa).
-run not succesfull
-starting 36 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1851912').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1851912 
-Warning: Ps input was provided (99497.17252604167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99370.03492604167Pa).
-run not succesfull
-starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
-  warnings.warn("Key '"+key+"' may not be implemented.")
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
-  self.L = zsl/self.zeta
- 366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1906431').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1906431 
-Warning: Ps input was provided (99586.98111979167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94123.59591979167Pa).
-run not succesfull
-starting 38 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1957671').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1957671 
-Warning: Ps input was provided (99235.24479166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84176.50239166667Pa).
-run not succesfull
-starting 39 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2008390').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2008390 
-Warning: Ps input was provided (99279.59440104167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85735.90840104167Pa).
-run not succesfull
-starting 40 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2059270').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2059270 
-Warning: Ps input was provided (99774.5234375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98984.6222375Pa).
-run not succesfull
-starting 41 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2113052').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2113052 
-Warning: Ps input was provided (99802.658203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98880.910603125Pa).
-run not succesfull
-starting 42 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2167306').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2167306 
-Warning: Ps input was provided (99349.02734375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99158.32094375Pa).
-run not succesfull
-starting 43 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2222303').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2222303 
-Warning: Ps input was provided (98955.87239583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98241.31199583333Pa).
-run not succesfull
-starting 44 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2276984').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2276984 
-Warning: Ps input was provided (98735.572265625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86832.903065625Pa).
-run not succesfull
-starting 45 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2328277').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2328277 
-Warning: Ps input was provided (99026.02408854167Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95189.52928854167Pa).
-run not succesfull
-starting 46 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2381273').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2381273 
-Warning: Ps input was provided (98960.017578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98404.379178125Pa).
-run not succesfull
-starting 47 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2435826').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2435826 
-Warning: Ps input was provided (98886.17122395833Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97028.54962395833Pa).
-run not succesfull
-starting 48 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2489579').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2489579 
-Warning: Ps input was provided (99096.7578125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97814.7870125Pa).
-run not succesfull
-starting 49 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2543769').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2543769 
-Warning: Ps input was provided (98649.583984375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98244.627184375Pa).
-run not succesfull
-starting 50 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.2597864').to_json" > /local/4759326[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.2597864 
-Warning: Ps input was provided (98760.32682291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97915.09722291667Pa).
-run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-3 b/class4gl/c4gl_sim.o4759326-3
deleted file mode 100644
index 57c4168..0000000
--- a/class4gl/c4gl_sim.o4759326-3
+++ /dev/null
@@ -1,227 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-       Unnamed: 0          filename  latitude  longitude
-74560           0  74560_0_ini.yaml     40.15     -89.33
-getting all records of the whole batch
-determining the station and its chunk number according global_chunk_number (3)
-chunks_current_station 8
-station =  [74560]
-station chunk number: 3
-Fetching initial/forcing records
-starting station chunk number: 3(size: 50 soundings)
-starting 1 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.4').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.4 
-Warning: Ps input was provided (99334.38498263889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98008.8577826389Pa).
-run not succesfull
-starting 2 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.54256').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.54256 
-Warning: Ps input was provided (98974.41840277778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93374.47800277777Pa).
-run not succesfull
-starting 3 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.106783').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.106783 
-Warning: Ps input was provided (98773.94704861111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97569.67144861112Pa).
-run not succesfull
-starting 4 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.161041').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.161041 
-Warning: Ps input was provided (98655.97786458333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93577.53706458333Pa).
-run not succesfull
-starting 5 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.213499').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.213499 
-Warning: Ps input was provided (99006.80989583333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98821.98949583333Pa).
-run not succesfull
-starting 6 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.268606').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.268606 
-Warning: Ps input was provided (98851.09939236111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96326.00539236111Pa).
-run not succesfull
-starting 7 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.321942').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.321942 
-Warning: Ps input was provided (98580.45746527778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97994.21186527779Pa).
-run not succesfull
-starting 8 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.376318').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.376318 
-Warning: Ps input was provided (98692.86935763889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98522.17535763889Pa).
-run not succesfull
-starting 9 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.429745').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.429745 
-Warning: Ps input was provided (97911.22092013889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (80203.77852013889Pa).
-run not succesfull
-starting 10 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.480238').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.480238 
-Warning: Ps input was provided (98075.484375Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97173.749175Pa).
-run not succesfull
-starting 11 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.534353').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.534353 
-Warning: Ps input was provided (98681.65972222222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96007.06132222222Pa).
-run not succesfull
-starting 12 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.587556').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.587556 
-Warning: Ps input was provided (99226.03385416667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98950.56905416667Pa).
-run not succesfull
-starting 13 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.642546').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.642546 
-Warning: Ps input was provided (99068.84461805556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97983.46621805556Pa).
-run not succesfull
-starting 14 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.696672').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.696672 
-Warning: Ps input was provided (98850.8203125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (86857.5067125Pa).
-run not succesfull
-starting 15 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.748004').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.748004 
-Warning: Ps input was provided (98882.85069444444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (80400.81069444443Pa).
-run not succesfull
-starting 16 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.798522').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.798522 
-Warning: Ps input was provided (98935.29166666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (84607.59046666668Pa).
-run not succesfull
-starting 17 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.849366').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.849366 
-Warning: Ps input was provided (99346.84895833333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (87599.57015833333Pa).
-run not succesfull
-starting 18 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.900431').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.900431 
-Warning: Ps input was provided (99613.65928819444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99492.40768819443Pa).
-run not succesfull
-starting 19 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.955333').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.955333 
-Warning: Ps input was provided (99327.306640625Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96987.033040625Pa).
-run not succesfull
-starting 20 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1008606').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1008606 
-Warning: Ps input was provided (99090.17664930556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97185.46704930556Pa).
-run not succesfull
-starting 21 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1062216').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1062216 
-Warning: Ps input was provided (98801.10199652778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96923.46799652778Pa).
-run not succesfull
-starting 22 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1116153').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1116153 
-Warning: Ps input was provided (98502.55295138889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97001.6229513889Pa).
-run not succesfull
-starting 23 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1169750').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1169750 
-Warning: Ps input was provided (98958.93836805556Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (83577.64316805557Pa).
-run not succesfull
-starting 24 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1220569').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1220569 
-Warning: Ps input was provided (99661.33854166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98757.24894166666Pa).
-run not succesfull
-starting 25 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1274715').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1274715 
-Warning: Ps input was provided (99208.20182291667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85307.82422291668Pa).
-run not succesfull
-starting 26 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1325734').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1325734 
-Warning: Ps input was provided (99667.44791666667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93415.33871666667Pa).
-run not succesfull
-starting 27 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1378235').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1378235 
-Warning: Ps input was provided (99869.86935763889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98178.23295763889Pa).
-run not succesfull
-starting 28 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1430796').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1430796 
-Warning: Ps input was provided (99770.04644097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97653.44084097222Pa).
-run not succesfull
-starting 29 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1484617').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1484617 
-Warning: Ps input was provided (99515.08116319444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (99389.12076319444Pa).
-run not succesfull
-starting 30 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1539402').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1539402 
-Warning: Ps input was provided (99617.5703125Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92184.7295125Pa).
-run not succesfull
-starting 31 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1591441').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1591441 
-Warning: Ps input was provided (99509.58333333333Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98973.95733333332Pa).
-run not succesfull
-starting 32 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1645914').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1645914 
-Warning: Ps input was provided (99475.33116319444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (93772.97436319444Pa).
-run not succesfull
-starting 33 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1698613').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1698613 
-Warning: Ps input was provided (99442.09157986111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (95909.31437986111Pa).
-run not succesfull
-starting 34 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1751712').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1751712 
-Warning: Ps input was provided (99330.49479166667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97372.81119166667Pa).
-run not succesfull
-starting 35 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1805578').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1805578 
-Warning: Ps input was provided (98861.93576388889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98717.1401638889Pa).
-run not succesfull
-starting 36 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1859984').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1859984 
-Warning: Ps input was provided (98695.76085069444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94772.15325069443Pa).
-run not succesfull
-starting 37 out of 50 (station total: /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:345: UserWarning: Key 'index' may not be implemented.
-  warnings.warn("Key '"+key+"' may not be implemented.")
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/model.py:1454: RuntimeWarning: divide by zero encountered in double_scalars
-  self.L = zsl/self.zeta
- 366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1912886').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1912886 
-Warning: Ps input was provided (99132.26996527778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96674.27636527778Pa).
-run not succesfull
-starting 38 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1966234').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1966234 
-Warning: Ps input was provided (99497.02994791667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98510.53634791667Pa).
-run not succesfull
-starting 39 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2020399').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2020399 
-Warning: Ps input was provided (99728.96788194444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (85688.50348194444Pa).
-run not succesfull
-starting 40 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2071191').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2071191 
-Warning: Ps input was provided (99652.02690972222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (88797.06570972221Pa).
-run not succesfull
-starting 41 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2122985').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2122985 
-Warning: Ps input was provided (99688.40147569444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98187.47147569444Pa).
-run not succesfull
-starting 42 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2176760').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2176760 
-Warning: Ps input was provided (99471.74782986111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98455.82422986111Pa).
-run not succesfull
-starting 43 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2230944').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2230944 
-Warning: Ps input was provided (99348.51519097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96937.60959097222Pa).
-run not succesfull
-starting 44 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2284495').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2284495 
-Warning: Ps input was provided (99275.59982638889Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (92649.1410263889Pa).
-run not succesfull
-starting 45 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2337035').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2337035 
-Warning: Ps input was provided (99152.58463541667Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96658.09783541667Pa).
-run not succesfull
-starting 46 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2390194').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2390194 
-Warning: Ps input was provided (98868.35069444444Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97950.13469444444Pa).
-run not succesfull
-starting 47 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2444461').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2444461 
-Warning: Ps input was provided (99050.93793402778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (96213.88593402778Pa).
-run not succesfull
-starting 48 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2497851').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2497851 
-Warning: Ps input was provided (99175.79644097222Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (97772.57404097222Pa).
-run not succesfull
-starting 49 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2551916').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2551916 
-Warning: Ps input was provided (99178.48090277778Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (98734.67650277779Pa).
-run not succesfull
-starting 50 out of 50 (station total:  366 )
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.2606609').to_json" > /local/4759326[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.2606609 
-Warning: Ps input was provided (98840.40798611111Pa), but it is now overwritten by the first level (index 0) of p_pro which is different (94689.60078611111Pa).
-run not succesfull
diff --git a/class4gl/c4gl_sim.o4759326-4 b/class4gl/c4gl_sim.o4759326-4
deleted file mode 100644
index 1dfedff..0000000
--- a/class4gl/c4gl_sim.o4759326-4
+++ /dev/null
@@ -1,10 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-5 b/class4gl/c4gl_sim.o4759326-5
deleted file mode 100644
index 6d9339a..0000000
--- a/class4gl/c4gl_sim.o4759326-5
+++ /dev/null
@@ -1,10 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-6 b/class4gl/c4gl_sim.o4759326-6
deleted file mode 100644
index d48e632..0000000
--- a/class4gl/c4gl_sim.o4759326-6
+++ /dev/null
@@ -1,10 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini
diff --git a/class4gl/c4gl_sim.o4759326-7 b/class4gl/c4gl_sim.o4759326-7
deleted file mode 100644
index 5976742..0000000
--- a/class4gl/c4gl_sim.o4759326-7
+++ /dev/null
@@ -1,10 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_exec
-C4GLJOB_experiments
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/simulations/simulations.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --experiments=ERA_NOAC --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ --path_forcing=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --runtime=from_input --split_by=50 --station_id=74560 --subset_forcing=ini

From ff9253c76127c79896812130e91867e419174402 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 18 Sep 2018 16:18:20 +0200
Subject: [PATCH 075/129] fix searching for first file of a station.

---
 class4gl/interface_functions.py | 39 ++++++++++++++++++++++++++-------
 class4gl/interface_multi.py     |  4 ++++
 2 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 0da9a00..ca45f7e 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -192,10 +192,26 @@ def __init__(self,path,suffix='ini',refetch_stations=True):
         self.table = self.table.set_index('STNID')
 
     def get_stations(self,suffix):
-        stations_list_files = glob.glob(self.path+'/?????_0_'+suffix+'.yaml')
+        stations_list_files = glob.glob(self.path+'/?????_*_'+suffix+'.yaml')
         if len(stations_list_files) == 0:
             stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
+        else:
+            # this weird section retreives the first file of every station
+            stations_list_files_1 = [station_file[:len(self.path+'/?????')] for \
+                                   station_file in stations_list_files]
+            stations_list_files_2 = [station_file[len(self.path+'/?????'):] for \
+                                   station_file in stations_list_files]
+            print(stations_list_files_1)
+            stations_list_files_new = []
+            stations_list_files_skip = []
+            for istat,stations_file_1 in  enumerate(stations_list_files_1):
+                if stations_file_1 not in stations_list_files_skip:
+                    stations_list_files_skip.append(stations_file_1)
+                    stations_list_files_new.append(stations_file_1+stations_list_files_2[istat])
+            stations_list_files = stations_list_files_new
+            
         stations_list_files.sort()
+
         if len(stations_list_files) == 0:
             raise ValueError('no stations found that match "'+self.path+'/?????[_0]_'+suffix+'.yaml'+'"')
         stations_list = []
@@ -379,13 +395,20 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
             else:
                 chunk = 0
                 end_of_chunks = False
-                while not end_of_chunks:
-                    fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
-                    if os.path.isfile(path_yaml+'/'+fn):
-                        dictfnchunks.append(dict(fn=fn,chunk=chunk))
-                    else:
-                        end_of_chunks = True
-                    chunk += 1
+                station_list_files = glob.glob(path_yaml+'/'+format(STNID,'05d')+'_*_'+subset+'.yaml')
+                station_list_files.sort()
+                for station_path_file in station_list_files:
+                    fn = station_path_file.split('/')[-1]
+                    chunk = int(fn.split('_')[1])
+                    dictfnchunks.append(dict(fn=fn,chunk=chunk))
+
+                # while not end_of_chunks:
+                #     fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
+                #     if os.path.isfile(path_yaml+'/'+fn):
+                #         dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                #     else:
+                #         end_of_chunks = True
+                #     chunk += 1
 
             # globyamlfilenames = path_yaml+'/'+format(STNID,'05d')+'*_'+subset+'.yaml'
             # yamlfilenames = glob.glob(globyamlfilenames)
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 6e7cd2f..25f0f09 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -129,6 +129,10 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
         self.frames['stats']['records_all_stations_mod'].index = \
             self.frames['stats']['records_all_stations_ini'].index 
 
+        
+        if len(self.frames['stats']['records_all_stations_ini']):
+            raise ValueError('no class records found. Aborting')
+
         self.frames['stats']['records_all_stations_ini']['dates'] = \
             self.frames['stats']['records_all_stations_ini']['ldatetime'].dt.date
 

From b13181353e0ede40a66bf29841c55fed44965ee4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 18 Sep 2018 21:11:26 +0200
Subject: [PATCH 076/129] fix searching for first file of a station.

---
 class4gl/interface/interface.py | 12 +++++++-----
 class4gl/interface_functions.py |  2 +-
 class4gl/interface_multi.py     |  6 +++---
 3 files changed, 11 insertions(+), 9 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 1af9e4c..2e1c1be 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -205,11 +205,13 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     for varkey in ['h','theta','q']:                                                    
         ikey = 0
         key = list(args.experiments.strip().split(' '))[ikey]
-        cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-        clearsky = (cc < 0.05)
+        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        # clearsky = (cc < 0.05)
     
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
     
     
         nbins=40       
@@ -433,7 +435,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #print(data_all.columns)
         #print('hello7')
         for varkey in ['h','theta','q']:
-            input_keys =['wg','cc']
+            input_keys =['wg','cc','advt']
             for input_key in input_keys:
                 varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index ca45f7e..0c0cf32 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -201,7 +201,7 @@ def get_stations(self,suffix):
                                    station_file in stations_list_files]
             stations_list_files_2 = [station_file[len(self.path+'/?????'):] for \
                                    station_file in stations_list_files]
-            print(stations_list_files_1)
+            #print(stations_list_files_1)
             stations_list_files_new = []
             stations_list_files_skip = []
             for istat,stations_file_1 in  enumerate(stations_list_files_1):
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 25f0f09..9509387 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -130,7 +130,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
             self.frames['stats']['records_all_stations_ini'].index 
 
         
-        if len(self.frames['stats']['records_all_stations_ini']):
+        if len(self.frames['stats']['records_all_stations_ini']) ==0:
             raise ValueError('no class records found. Aborting')
 
         self.frames['stats']['records_all_stations_ini']['dates'] = \
@@ -1266,8 +1266,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                                  +'LT')
 
             #print('r17')
-            print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
-            print(hmax)
+            #print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            #print(hmax)
             valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
             if valid_mod:
 

From 861e7f8b7007476d5de959f6377c264c7e7562b9 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 19 Sep 2018 09:45:44 +0200
Subject: [PATCH 077/129] make simulations.py less strict, so that writing out
 data does not fail.

---
 class4gl/class4gl.py                | 11 ++++++-----
 class4gl/simulations/simulations.py | 24 ++++++++++--------------
 2 files changed, 16 insertions(+), 19 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index a3c8cc8..7afe040 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -1285,7 +1285,7 @@ def query_source(self,var):
             if var in vars_in_source:
                 return source
 
-    def check_source(self,source,check_only_sections=None):
+    def check_source(self,source,check_only_sections=None,ignore_keys=[]):
         """ this procedure checks whether data of a specified source is valid.
 
         INPUT:
@@ -1321,9 +1321,9 @@ def check_source(self,source,check_only_sections=None):
                     # self.logger.info('Expected key '+datakey+\
                     #                  ' is not in parameter input')                        
                     source_ok = False                                           
-                    print(datakey)
-                elif (checkdata[datakey] is None) or \
-                     (pd.isnull(checkdata[datakey]) is True):                    
+                elif (datakey not in ignore_keys) and \
+                     ((checkdata[datakey] is None) or \
+                     (pd.isnull(checkdata[datakey]) is True)):                    
         
                     # self.logger.info('Key value of "'+datakey+\
                     #                  '" is invalid: ('+ \
@@ -1362,7 +1362,8 @@ def check_source_globaldata(self):
             source_ok = self.check_source(source='globaldata',\
                                           check_only_sections=['air_ac',\
                                                                'air_ap',\
-                                                               'pars'])
+                                                               'pars'],
+                                         ignore_keys=[])
             if not source_ok:
                 source_globaldata_ok = False
         
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 7b02734..444d87b 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -272,14 +272,11 @@
 
                     if args.error_handling == 'dump_always':
                         try:
-                            if c4gli_morning.check_source_globaldata():
-                            
-
-                                c4gl.run()
-                                print('run succesfull')
-                            else:
-                                print('global data not ok')
-                                raise ValueError('global data not ok')
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
+                            c4gl.run()
+                            print('run succesfull')
                         except:
                             print('run not succesfull')
                         onerun = True
@@ -296,12 +293,11 @@
                     # successful
                     elif args.error_handling == 'dump_on_success':
                        try:
-                            if c4gli_morning.check_source_globaldata():
-                                c4gl.run()
-                                print('run succesfull')
-                            else:
-                                print('global data not ok')
-                                raise ValueError('global data not ok')
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
+                            c4gl.run()
+                            print('run succesfull')
                             c4gli_morning.dump(file_ini)
                             
                             

From cfd1914de77ce2547a11fb2acd6ffe4b00355171 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 19 Sep 2018 14:55:05 +0200
Subject: [PATCH 078/129] Fix errors in case there are more than 10 chunk
 files.

---
 class4gl/c4gl_setup.o4759290-0      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-1      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-2      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-3      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-4      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-5      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-6      | 1251 ---------------------------
 class4gl/c4gl_setup.o4759290-7      |  469 ----------
 class4gl/simulations/simulations.py |    4 +-
 9 files changed, 2 insertions(+), 9228 deletions(-)
 delete mode 100644 class4gl/c4gl_setup.o4759290-0
 delete mode 100644 class4gl/c4gl_setup.o4759290-1
 delete mode 100644 class4gl/c4gl_setup.o4759290-2
 delete mode 100644 class4gl/c4gl_setup.o4759290-3
 delete mode 100644 class4gl/c4gl_setup.o4759290-4
 delete mode 100644 class4gl/c4gl_setup.o4759290-5
 delete mode 100644 class4gl/c4gl_setup.o4759290-6
 delete mode 100644 class4gl/c4gl_setup.o4759290-7

diff --git a/class4gl/c4gl_setup.o4759290-0 b/class4gl/c4gl_setup.o4759290-0
deleted file mode 100644
index 45f4cc7..0000000
--- a/class4gl/c4gl_setup.o4759290-0
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 0 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml
-0 1981-01-01 00:00:00
-1 1981-01-02 00:00:00
-2 1981-01-03 00:00:00
-3 1981-01-04 00:00:00
-4 1981-01-05 00:00:00
-5 1981-01-06 00:00:00
-6 1981-01-07 00:00:00
-7 1981-01-08 00:00:00
-8 1981-01-09 00:00:00
-9 1981-01-10 00:00:00
-10 1981-01-11 00:00:00
-11 1981-01-12 00:00:00
-12 1981-01-13 00:00:00
-13 1981-01-14 00:00:00
-14 1981-01-15 00:00:00
-15 1981-01-16 00:00:00
-16 1981-01-17 00:00:00
-17 1981-01-18 00:00:00
-18 1981-01-19 00:00:00
-19 1981-01-20 00:00:00
-20 1981-01-21 00:00:00
-21 1981-01-22 00:00:00
-22 1981-01-23 00:00:00
-23 1981-01-24 00:00:00
-24 1981-01-25 00:00:00
-25 1981-01-26 00:00:00
-26 1981-01-27 00:00:00
-27 1981-01-28 00:00:00
-28 1981-01-29 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-01-30 00:00:00
-30 1981-01-31 00:00:00
-31 1981-02-01 00:00:00
-32 1981-02-02 00:00:00
-33 1981-02-03 00:00:00
-34 1981-02-04 00:00:00
-35 1981-02-05 00:00:00
-36 1981-02-06 00:00:00
-37 1981-02-07 00:00:00
-38 1981-02-08 00:00:00
-39 1981-02-09 00:00:00
-40 1981-02-10 00:00:00
-41 1981-02-11 00:00:00
-42 1981-02-12 00:00:00
-43 1981-02-13 00:00:00
-44 1981-02-14 00:00:00
-45 1981-02-15 00:00:00
-46 1981-02-16 00:00:00
-47 1981-02-17 00:00:00
-48 1981-02-18 00:00:00
-49 1981-02-19 00:00:00
-pkl file older than yaml file, so I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.4 
- obs record registered
- next record: 51396
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.51396').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.51396 
- obs record registered
- next record: 102622
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.102622').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.102622 
- obs record registered
- next record: 155319
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.155319').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.155319 
- obs record registered
- next record: 207321
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.207321').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.207321 
- obs record registered
- next record: 261499
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.261499').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.261499 
- obs record registered
- next record: 312096
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.312096').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.312096 
- obs record registered
- next record: 363706
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.363706').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.363706 
- obs record registered
- next record: 416568
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.416568').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.416568 
- obs record registered
- next record: 470247
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.470247').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.470247 
- obs record registered
- next record: 524351
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.524351').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.524351 
- obs record registered
- next record: 577126
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.577126').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.577126 
- obs record registered
- next record: 629651
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.629651').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.629651 
- obs record registered
- next record: 682101
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.682101').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.682101 
- obs record registered
- next record: 734044
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.734044').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.734044 
- obs record registered
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-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.yaml.1420916').to_json" > /local/4759290[0].master15.delcatty.gent.vsc/74560_0_ini.yaml.buffer.json.1420916 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
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- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_0_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-1 b/class4gl/c4gl_setup.o4759290-1
deleted file mode 100644
index d958506..0000000
--- a/class4gl/c4gl_setup.o4759290-1
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 1 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml
-0 1981-02-20 00:00:00
-1 1981-02-21 00:00:00
-2 1981-02-22 00:00:00
-3 1981-02-23 00:00:00
-4 1981-02-24 00:00:00
-5 1981-02-25 00:00:00
-6 1981-02-26 00:00:00
-7 1981-02-27 00:00:00
-8 1981-02-28 00:00:00
-9 1981-03-01 00:00:00
-10 1981-03-02 00:00:00
-11 1981-03-03 00:00:00
-12 1981-03-04 00:00:00
-13 1981-03-05 00:00:00
-14 1981-03-06 00:00:00
-15 1981-03-07 00:00:00
-16 1981-03-08 00:00:00
-17 1981-03-09 00:00:00
-18 1981-03-10 00:00:00
-19 1981-03-11 00:00:00
-20 1981-03-12 00:00:00
-21 1981-03-13 00:00:00
-22 1981-03-14 00:00:00
-23 1981-03-15 00:00:00
-24 1981-03-16 00:00:00
-25 1981-03-17 00:00:00
-26 1981-03-18 00:00:00
-27 1981-03-19 00:00:00
-28 1981-03-20 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-03-21 00:00:00
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-31 1981-03-23 00:00:00
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-49 1981-04-10 00:00:00
-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.4 
- obs record registered
- next record: 53735
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.53735').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.53735 
- obs record registered
- next record: 107051
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.107051').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.107051 
- obs record registered
- next record: 159809
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.159809').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.159809 
- obs record registered
- next record: 210583
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.210583').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.210583 
- obs record registered
- next record: 262669
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.262669').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.262669 
- obs record registered
- next record: 315998
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.315998').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.315998 
- obs record registered
- next record: 368733
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.368733').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.368733 
- obs record registered
- next record: 422182
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.422182').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.422182 
- obs record registered
- next record: 473690
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.473690').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.473690 
- obs record registered
- next record: 525232
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.525232').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.525232 
- obs record registered
- next record: 578431
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.578431').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.578431 
- obs record registered
- next record: 633046
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.633046').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.633046 
- obs record registered
- next record: 686127
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.686127').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.686127 
- obs record registered
- next record: 738483
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.738483').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.738483 
- obs record registered
- next record: 790644
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.790644').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.790644 
- obs record registered
- next record: 844792
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.844792').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.844792 
- obs record registered
- next record: 897654
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.897654').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.897654 
- obs record registered
- next record: 950524
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.950524').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.950524 
- obs record registered
- next record: 1003819
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1003819').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1003819 
- obs record registered
- next record: 1057080
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1057080').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1057080 
- obs record registered
- next record: 1109138
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1109138').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1109138 
- obs record registered
- next record: 1162278
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1162278').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1162278 
- obs record registered
- next record: 1216404
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1216404').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1216404 
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-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.yaml.1424704').to_json" > /local/4759290[1].master15.delcatty.gent.vsc/74560_1_ini.yaml.buffer.json.1424704 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
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- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_1_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-2 b/class4gl/c4gl_setup.o4759290-2
deleted file mode 100644
index d9771a1..0000000
--- a/class4gl/c4gl_setup.o4759290-2
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 2 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml
-0 1981-04-11 00:00:00
-1 1981-04-12 00:00:00
-2 1981-04-13 00:00:00
-3 1981-04-14 00:00:00
-4 1981-04-15 00:00:00
-5 1981-04-16 00:00:00
-6 1981-04-17 00:00:00
-7 1981-04-18 00:00:00
-8 1981-04-19 00:00:00
-9 1981-04-20 00:00:00
-10 1981-04-21 00:00:00
-11 1981-04-22 00:00:00
-12 1981-04-23 00:00:00
-13 1981-04-24 00:00:00
-14 1981-04-25 00:00:00
-15 1981-04-26 00:00:00
-16 1981-04-27 00:00:00
-17 1981-04-28 00:00:00
-18 1981-04-29 00:00:00
-19 1981-04-30 00:00:00
-20 1981-05-01 00:00:00
-21 1981-05-02 00:00:00
-22 1981-05-03 00:00:00
-23 1981-05-04 00:00:00
-24 1981-05-05 00:00:00
-25 1981-05-06 00:00:00
-26 1981-05-07 00:00:00
-27 1981-05-08 00:00:00
-28 1981-05-09 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-05-10 00:00:00
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-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.4 
- obs record registered
- next record: 51067
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.51067').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.51067 
- obs record registered
- next record: 104322
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.104322').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.104322 
- obs record registered
- next record: 158125
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.158125').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.158125 
- obs record registered
- next record: 210001
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.210001').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.210001 
- obs record registered
- next record: 263270
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.263270').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.263270 
- obs record registered
- next record: 317457
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.317457').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.317457 
- obs record registered
- next record: 368268
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.368268').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.368268 
- obs record registered
- next record: 422781
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.422781').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.422781 
- obs record registered
- next record: 476050
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.476050').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.476050 
- obs record registered
- next record: 528316
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.528316').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.528316 
- obs record registered
- next record: 580931
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.580931').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.580931 
- obs record registered
- next record: 635773
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.635773').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.635773 
- obs record registered
- next record: 686190
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.686190').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.686190 
- obs record registered
- next record: 736979
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.736979').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.736979 
- obs record registered
- next record: 791508
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.791508').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.791508 
- obs record registered
- next record: 844468
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.844468').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.844468 
- obs record registered
- next record: 899058
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.899058').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.899058 
- obs record registered
- next record: 950395
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.950395').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.950395 
- obs record registered
- next record: 1001879
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1001879').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1001879 
- obs record registered
- next record: 1054711
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1054711').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1054711 
- obs record registered
- next record: 1107591
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1107591').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1107591 
- obs record registered
- next record: 1161498
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1161498').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1161498 
- obs record registered
- next record: 1216461
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1216461').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1216461 
- obs record registered
- next record: 1270638
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1270638').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1270638 
- obs record registered
- next record: 1323032
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1323032').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1323032 
- obs record registered
- next record: 1374748
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1374748').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1374748 
- obs record registered
- next record: 1426449
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.yaml.1426449').to_json" > /local/4759290[2].master15.delcatty.gent.vsc/74560_2_ini.yaml.buffer.json.1426449 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
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- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_2_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-3 b/class4gl/c4gl_setup.o4759290-3
deleted file mode 100644
index 17f4526..0000000
--- a/class4gl/c4gl_setup.o4759290-3
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 3 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml
-0 1981-05-31 00:00:00
-1 1981-06-01 00:00:00
-2 1981-06-02 00:00:00
-3 1981-06-03 00:00:00
-4 1981-06-04 00:00:00
-5 1981-06-05 00:00:00
-6 1981-06-06 00:00:00
-7 1981-06-07 00:00:00
-8 1981-06-08 00:00:00
-9 1981-06-09 00:00:00
-10 1981-06-10 00:00:00
-11 1981-06-11 00:00:00
-12 1981-06-12 00:00:00
-13 1981-06-13 00:00:00
-14 1981-06-14 00:00:00
-15 1981-06-15 00:00:00
-16 1981-06-16 00:00:00
-17 1981-06-17 00:00:00
-18 1981-06-18 00:00:00
-19 1981-06-19 00:00:00
-20 1981-06-20 00:00:00
-21 1981-06-21 00:00:00
-22 1981-06-22 00:00:00
-23 1981-06-23 00:00:00
-24 1981-06-24 00:00:00
-25 1981-06-25 00:00:00
-26 1981-06-26 00:00:00
-27 1981-06-27 00:00:00
-28 1981-06-28 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-06-29 00:00:00
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-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.4 
- obs record registered
- next record: 54256
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.54256').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.54256 
- obs record registered
- next record: 106783
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.106783').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.106783 
- obs record registered
- next record: 161041
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.161041').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.161041 
- obs record registered
- next record: 213499
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.213499').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.213499 
- obs record registered
- next record: 268606
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.268606').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.268606 
- obs record registered
- next record: 321942
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.321942').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.321942 
- obs record registered
- next record: 376318
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.376318').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.376318 
- obs record registered
- next record: 429745
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.429745').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.429745 
- obs record registered
- next record: 480238
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.480238').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.480238 
- obs record registered
- next record: 534353
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.534353').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.534353 
- obs record registered
- next record: 587556
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.587556').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.587556 
- obs record registered
- next record: 642546
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.642546').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.642546 
- obs record registered
- next record: 696672
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.696672').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.696672 
- obs record registered
- next record: 748004
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.748004').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.748004 
- obs record registered
- next record: 798522
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.798522').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.798522 
- obs record registered
- next record: 849366
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.849366').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.849366 
- obs record registered
- next record: 900431
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.900431').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.900431 
- obs record registered
- next record: 955333
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.955333').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.955333 
- obs record registered
- next record: 1008606
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1008606').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1008606 
- obs record registered
- next record: 1062216
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1062216').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1062216 
- obs record registered
- next record: 1116153
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1116153').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1116153 
- obs record registered
- next record: 1169750
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1169750').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1169750 
- obs record registered
- next record: 1220569
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1220569').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1220569 
- obs record registered
- next record: 1274715
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1274715').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1274715 
- obs record registered
- next record: 1325734
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1325734').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1325734 
- obs record registered
- next record: 1378235
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1378235').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1378235 
- obs record registered
- next record: 1430796
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1430796').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1430796 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
- obs record registered
- next record: 1484617
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1484617').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1484617 
- obs record registered
- next record: 1539402
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1539402').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1539402 
- obs record registered
- next record: 1591441
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1591441').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1591441 
- obs record registered
- next record: 1645914
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.yaml.1645914').to_json" > /local/4759290[3].master15.delcatty.gent.vsc/74560_3_ini.yaml.buffer.json.1645914 
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- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_3_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-4 b/class4gl/c4gl_setup.o4759290-4
deleted file mode 100644
index 7fc0cea..0000000
--- a/class4gl/c4gl_setup.o4759290-4
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 4 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml
-0 1981-07-20 00:00:00
-1 1981-07-21 00:00:00
-2 1981-07-22 00:00:00
-3 1981-07-23 00:00:00
-4 1981-07-24 00:00:00
-5 1981-07-25 00:00:00
-6 1981-07-26 00:00:00
-7 1981-07-27 00:00:00
-8 1981-07-28 00:00:00
-9 1981-07-29 00:00:00
-10 1981-07-30 00:00:00
-11 1981-07-31 00:00:00
-12 1981-08-01 00:00:00
-13 1981-08-02 00:00:00
-14 1981-08-03 00:00:00
-15 1981-08-04 00:00:00
-16 1981-08-05 00:00:00
-17 1981-08-06 00:00:00
-18 1981-08-07 00:00:00
-19 1981-08-08 00:00:00
-20 1981-08-09 00:00:00
-21 1981-08-10 00:00:00
-22 1981-08-11 00:00:00
-23 1981-08-12 00:00:00
-24 1981-08-13 00:00:00
-25 1981-08-14 00:00:00
-26 1981-08-15 00:00:00
-27 1981-08-16 00:00:00
-28 1981-08-17 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-08-18 00:00:00
-30 1981-08-19 00:00:00
-31 1981-08-20 00:00:00
-32 1981-08-21 00:00:00
-33 1981-08-22 00:00:00
-34 1981-08-23 00:00:00
-35 1981-08-24 00:00:00
-36 1981-08-25 00:00:00
-37 1981-08-26 00:00:00
-38 1981-08-27 00:00:00
-39 1981-08-28 00:00:00
-40 1981-08-29 00:00:00
-41 1981-08-30 00:00:00
-42 1981-08-31 00:00:00
-43 1981-09-01 00:00:00
-44 1981-09-02 00:00:00
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-46 1981-09-04 00:00:00
-47 1981-09-05 00:00:00
-48 1981-09-06 00:00:00
-49 1981-09-07 00:00:00
-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.4 
- obs record registered
- next record: 51731
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.51731').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.51731 
- obs record registered
- next record: 104580
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.104580').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.104580 
- obs record registered
- next record: 157565
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.157565').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.157565 
- obs record registered
- next record: 210613
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.210613').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.210613 
- obs record registered
- next record: 264470
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.264470').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.264470 
- obs record registered
- next record: 318729
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.318729').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.318729 
- obs record registered
- next record: 370870
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.370870').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.370870 
- obs record registered
- next record: 423492
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.423492').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.423492 
- obs record registered
- next record: 474750
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.474750').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.474750 
- obs record registered
- next record: 529842
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.529842').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.529842 
- obs record registered
- next record: 583680
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.583680').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.583680 
- obs record registered
- next record: 637895
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.637895').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.637895 
- obs record registered
- next record: 692631
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.692631').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.692631 
- obs record registered
- next record: 746761
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.746761').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.746761 
- obs record registered
- next record: 800591
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.800591').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.800591 
- obs record registered
- next record: 854394
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.854394').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.854394 
- obs record registered
- next record: 909045
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.909045').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.909045 
- obs record registered
- next record: 963060
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.963060').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.963060 
- obs record registered
- next record: 1015112
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1015112').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1015112 
- obs record registered
- next record: 1067515
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1067515').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1067515 
- obs record registered
- next record: 1121265
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1121265').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1121265 
- obs record registered
- next record: 1175359
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1175359').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1175359 
- obs record registered
- next record: 1227436
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1227436').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1227436 
- obs record registered
- next record: 1279990
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1279990').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1279990 
- obs record registered
- next record: 1333481
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1333481').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1333481 
- obs record registered
- next record: 1388277
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1388277').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1388277 
- obs record registered
- next record: 1440447
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1440447').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1440447 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
- obs record registered
- next record: 1492950
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1492950').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1492950 
- obs record registered
- next record: 1544819
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1544819').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1544819 
- obs record registered
- next record: 1597484
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1597484').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1597484 
- obs record registered
- next record: 1650523
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1650523').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1650523 
- obs record registered
- next record: 1703570
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1703570').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1703570 
- obs record registered
- next record: 1755369
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1755369').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1755369 
- obs record registered
- next record: 1808308
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1808308').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1808308 
- obs record registered
- next record: 1862932
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1862932').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1862932 
- obs record registered
- next record: 1917988
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1917988').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1917988 
- obs record registered
- next record: 1972660
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.1972660').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.1972660 
- obs record registered
- next record: 2027192
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2027192').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2027192 
- obs record registered
- next record: 2080750
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2080750').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2080750 
- obs record registered
- next record: 2134876
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2134876').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2134876 
- obs record registered
- next record: 2187060
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2187060').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2187060 
- obs record registered
- next record: 2240918
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2240918').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2240918 
- obs record registered
- next record: 2293459
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2293459').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2293459 
- obs record registered
- next record: 2345446
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2345446').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2345446 
- obs record registered
- next record: 2399559
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2399559').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2399559 
- obs record registered
- next record: 2453394
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2453394').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2453394 
- obs record registered
- next record: 2507446
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2507446').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2507446 
- obs record registered
- next record: 2560803
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2560803').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2560803 
- obs record registered
- next record: 2613551
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.yaml.2613551').to_json" > /local/4759290[4].master15.delcatty.gent.vsc/74560_4_ini.yaml.buffer.json.2613551 
- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_4_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-5 b/class4gl/c4gl_setup.o4759290-5
deleted file mode 100644
index 3ffddc2..0000000
--- a/class4gl/c4gl_setup.o4759290-5
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 5 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml
-0 1981-09-08 00:00:00
-1 1981-09-09 00:00:00
-2 1981-09-10 00:00:00
-3 1981-09-11 00:00:00
-4 1981-09-12 00:00:00
-5 1981-09-13 00:00:00
-6 1981-09-14 00:00:00
-7 1981-09-15 00:00:00
-8 1981-09-16 00:00:00
-9 1981-09-17 00:00:00
-10 1981-09-18 00:00:00
-11 1981-09-19 00:00:00
-12 1981-09-20 00:00:00
-13 1981-09-21 00:00:00
-14 1981-09-22 00:00:00
-15 1981-09-23 00:00:00
-16 1981-09-24 00:00:00
-17 1981-09-25 00:00:00
-18 1981-09-26 00:00:00
-19 1981-09-27 00:00:00
-20 1981-09-28 00:00:00
-21 1981-09-29 00:00:00
-22 1981-09-30 00:00:00
-23 1981-10-01 00:00:00
-24 1981-10-02 00:00:00
-25 1981-10-03 00:00:00
-26 1981-10-04 00:00:00
-27 1981-10-05 00:00:00
-28 1981-10-06 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-10-07 00:00:00
-30 1981-10-08 00:00:00
-31 1981-10-09 00:00:00
-32 1981-10-10 00:00:00
-33 1981-10-11 00:00:00
-34 1981-10-12 00:00:00
-35 1981-10-13 00:00:00
-36 1981-10-14 00:00:00
-37 1981-10-15 00:00:00
-38 1981-10-16 00:00:00
-39 1981-10-17 00:00:00
-40 1981-10-18 00:00:00
-41 1981-10-19 00:00:00
-42 1981-10-20 00:00:00
-43 1981-10-21 00:00:00
-44 1981-10-22 00:00:00
-45 1981-10-23 00:00:00
-46 1981-10-24 00:00:00
-47 1981-10-25 00:00:00
-48 1981-10-26 00:00:00
-49 1981-10-27 00:00:00
-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.4 
- obs record registered
- next record: 53791
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.53791').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.53791 
- obs record registered
- next record: 108418
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.108418').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.108418 
- obs record registered
- next record: 160595
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.160595').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.160595 
- obs record registered
- next record: 213556
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.213556').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.213556 
- obs record registered
- next record: 267801
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.267801').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.267801 
- obs record registered
- next record: 322353
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.322353').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.322353 
- obs record registered
- next record: 376117
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.376117').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.376117 
- obs record registered
- next record: 428437
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.428437').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.428437 
- obs record registered
- next record: 482412
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.482412').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.482412 
- obs record registered
- next record: 534383
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.534383').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.534383 
- obs record registered
- next record: 587814
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.587814').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.587814 
- obs record registered
- next record: 639583
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.639583').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.639583 
- obs record registered
- next record: 692536
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.692536').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.692536 
- obs record registered
- next record: 746174
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.746174').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.746174 
- obs record registered
- next record: 798143
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.798143').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.798143 
- obs record registered
- next record: 850877
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.850877').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.850877 
- obs record registered
- next record: 905196
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.905196').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.905196 
- obs record registered
- next record: 959041
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.959041').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.959041 
- obs record registered
- next record: 1010607
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1010607').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1010607 
- obs record registered
- next record: 1063046
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1063046').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1063046 
- obs record registered
- next record: 1118074
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1118074').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1118074 
- obs record registered
- next record: 1173117
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1173117').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1173117 
- obs record registered
- next record: 1225841
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1225841').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1225841 
- obs record registered
- next record: 1276984
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1276984').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1276984 
- obs record registered
- next record: 1329882
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1329882').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1329882 
- obs record registered
- next record: 1384174
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1384174').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1384174 
- obs record registered
- next record: 1438389
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1438389').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1438389 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
- obs record registered
- next record: 1492962
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1492962').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1492962 
- obs record registered
- next record: 1544189
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1544189').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1544189 
- obs record registered
- next record: 1598345
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1598345').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1598345 
- obs record registered
- next record: 1651761
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1651761').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1651761 
- obs record registered
- next record: 1705208
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1705208').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1705208 
- obs record registered
- next record: 1759539
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1759539').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1759539 
- obs record registered
- next record: 1812463
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1812463').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1812463 
- obs record registered
- next record: 1865446
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1865446').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1865446 
- obs record registered
- next record: 1918746
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1918746').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1918746 
- obs record registered
- next record: 1972414
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.1972414').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.1972414 
- obs record registered
- next record: 2025735
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2025735').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2025735 
- obs record registered
- next record: 2079164
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2079164').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2079164 
- obs record registered
- next record: 2132791
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2132791').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2132791 
- obs record registered
- next record: 2181910
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2181910').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2181910 
- obs record registered
- next record: 2233649
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2233649').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2233649 
- obs record registered
- next record: 2284947
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2284947').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2284947 
- obs record registered
- next record: 2339510
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2339510').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2339510 
- obs record registered
- next record: 2391981
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2391981').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2391981 
- obs record registered
- next record: 2443387
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2443387').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2443387 
- obs record registered
- next record: 2497540
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2497540').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2497540 
- obs record registered
- next record: 2552060
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2552060').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2552060 
- obs record registered
- next record: 2604997
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.yaml.2604997').to_json" > /local/4759290[5].master15.delcatty.gent.vsc/74560_5_ini.yaml.buffer.json.2604997 
- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_5_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-6 b/class4gl/c4gl_setup.o4759290-6
deleted file mode 100644
index bd1fb54..0000000
--- a/class4gl/c4gl_setup.o4759290-6
+++ /dev/null
@@ -1,1251 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 6 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml
-0 1981-10-28 00:00:00
-1 1981-10-29 00:00:00
-2 1981-10-30 00:00:00
-3 1981-10-31 00:00:00
-4 1981-11-01 00:00:00
-5 1981-11-02 00:00:00
-6 1981-11-03 00:00:00
-7 1981-11-04 00:00:00
-8 1981-11-05 00:00:00
-9 1981-11-06 00:00:00
-10 1981-11-07 00:00:00
-11 1981-11-08 00:00:00
-12 1981-11-09 00:00:00
-13 1981-11-10 00:00:00
-14 1981-11-11 00:00:00
-15 1981-11-12 00:00:00
-16 1981-11-13 00:00:00
-17 1981-11-14 00:00:00
-18 1981-11-15 00:00:00
-19 1981-11-16 00:00:00
-20 1981-11-17 00:00:00
-21 1981-11-18 00:00:00
-22 1981-11-19 00:00:00
-23 1981-11-20 00:00:00
-24 1981-11-21 00:00:00
-25 1981-11-22 00:00:00
-26 1981-11-23 00:00:00
-27 1981-11-24 00:00:00
-28 1981-11-25 00:00:00/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-
-29 1981-11-26 00:00:00
-30 1981-11-27 00:00:00
-31 1981-11-28 00:00:00
-32 1981-11-29 00:00:00
-33 1981-11-30 00:00:00
-34 1981-12-01 00:00:00
-35 1981-12-02 00:00:00
-36 1981-12-03 00:00:00
-37 1981-12-04 00:00:00
-38 1981-12-05 00:00:00
-39 1981-12-06 00:00:00
-40 1981-12-07 00:00:00
-41 1981-12-08 00:00:00
-42 1981-12-09 00:00:00
-43 1981-12-10 00:00:00
-44 1981-12-11 00:00:00
-45 1981-12-12 00:00:00
-46 1981-12-13 00:00:00
-47 1981-12-14 00:00:00
-48 1981-12-15 00:00:00
-49 1981-12-16 00:00:00
-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.yaml"...
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.4 
- obs record registered
- next record: 55140
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.55140').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.55140 
- obs record registered
- next record: 108893
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.108893').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.108893 
- obs record registered
- next record: 163443
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.163443').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.163443 
- obs record registered
- next record: 218397
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.218397').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.218397 
- obs record registered
- next record: 271582
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-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.yaml.1446248').to_json" > /local/4759290[6].master15.delcatty.gent.vsc/74560_6_ini.yaml.buffer.json.1446248 /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
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-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_6_ini.pkl) for station 74560
diff --git a/class4gl/c4gl_setup.o4759290-7 b/class4gl/c4gl_setup.o4759290-7
deleted file mode 100644
index ee5acc9..0000000
--- a/class4gl/c4gl_setup.o4759290-7
+++ /dev/null
@@ -1,469 +0,0 @@
-C4GLJOB_c4gl_path_lib
-C4GLJOB_error_handling
-C4GLJOB_exec
-C4GLJOB_first_YYYYMMDD
-C4GLJOB_last_YYYYMMDD
-C4GLJOB_path_experiments
-C4GLJOB_path_forcing
-C4GLJOB_runtime
-C4GLJOB_split_by
-C4GLJOB_station_id
-C4GLJOB_subset_experiments
-C4GLJOB_subset_forcing
-Executing: python /user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py --global_chunk_number 7 --c4gl_path_lib=/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl --error_handling=dump_on_success --first_YYYYMMDD=19810101 --last_YYYYMMDD=19820101 --path_experiments=/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA/ --path_forcing=/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/ --runtime=from_afternoon_profile --split_by=50 --station_id=74560 --subset_experiments=ini --subset_forcing=morning
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/data_global.py:275: UserWarning: omitting pressure field p and advection
-  warnings.warn('omitting pressure field p and advection')
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/times.py:132: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  enable_cftimeindex)
-/apps/gent/CO7/sandybridge/software/xarray/0.10.8-intel-2018a-Python-3.6.4/lib/python3.6/site-packages/xarray-0.10.8-py3.6.egg/xarray/coding/variables.py:66: SerializationWarning: Unable to decode time axis into full numpy.datetime64 objects, continuing using dummy cftime.datetime objects instead, reason: dates out of range
-  return self.func(self.array[key])
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-Initializing global data
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-setting KGC as KGC from /user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-setting wg as SMsurf from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-setting w2 as SMroot from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-setting EF as EF from /user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-setting fW as fW from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fB as fB from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fH as fH from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc is already in the library.
-setting fTC as fTC from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-setting z0m as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc is already in the library.
-setting z0h as Band1 from /user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-setting wsat as wsat from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wsat.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-setting Ts as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-Warning: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc is already in the library.
-setting Tsoil as stl1 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl1_3hourly_xarray/stl1*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-setting T2 as stl2 from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/stl2_3hourly_xarray/stl2*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-setting cc as tcc from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/tcc_3hourly_xarray/tcc*_3hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-setting wfc as wfc from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wfc.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-setting wwilt as wwp from /user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/wwp.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-setting cveg as fv from /user/data/gent/gvo000/gvo00090/EXT/data/MOD44B/fv.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-setting DSMW as DSMW from /user/data/gent/gvo000/gvo00090/EXT/data/DSMW/FAO_DSMW_DP.nc
-calculating texture
-calculating texture type
-calculating soil parameter
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-setting LAIpixel as LAI from /user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-setting advt_x as advt_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-setting advt_y as advt_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-setting advq_x as advq_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-setting advq_y as advq_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-setting advu_x as advu_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-setting advu_y as advu_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-setting advv_x as advv_x from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-setting advv_y as advv_y from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-setting sp as sp from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-setting wp as w from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-setting t as t from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-setting q as q from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-setting u as u from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc
-opening: /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-setting v as v from /user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc
-getting a list of stations
-defining all_stations_select
-Selecting station by ID
-station numbers included in the whole batch (all chunks): [74560]
-Creating daily timeseries from 1981-01-01 00:00:00  to  1982-01-01 00:00:00
-start looping over chunk
-Writing to:  /user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml
-0 1981-12-17 00:00:00
-1 1981-12-18 00:00:00
-2 1981-12-19 00:00:00
-3 1981-12-20 00:00:00
-4 1981-12-21 00:00:00
-5 1981-12-22 00:00:00
-6 1981-12-23 00:00:00
-7 1981-12-24 00:00:00
-8 1981-12-25 00:00:00
-9 1981-12-26 00:00:00
-10 1981-12-27 00:00:00
-11 1981-12-28 00:00:00
-12 1981-12-29 00:00:00
-13 1981-12-30 00:00:00
-14 1981-12-31 00:00:00
-15 1982-01-01 00:00:00
-pkl file does not exist. I generate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml"...
-refetch_records flag is True. I regenerate "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl" from "/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.yaml".../user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1471: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head['z'] = pd.Series(np.array([2.,self.pars.h ,self.pars.h ]))
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1487: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame.
-Try using .loc[row_indexer,col_indexer] = value instead
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  air_ap_head[column] = ml_mean[column]
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/setup/setup_era.py:246: SettingWithCopyWarning: 
-A value is trying to be set on a copy of a slice from a DataFrame
-
-See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
-  mode=air_ap_mode)
-/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/class4gl.py:1509: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
-  air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
- next record: 4
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.4').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.4 
- obs record registered
- next record: 53111
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.53111').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.53111 
- obs record registered
- next record: 106374
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.106374').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.106374 
- obs record registered
- next record: 159575
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.159575').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.159575 
- obs record registered
- next record: 214479
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.214479').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.214479 
- obs record registered
- next record: 265342
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.265342').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.265342 
- obs record registered
- next record: 320174
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.320174').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.320174 
- obs record registered
- next record: 372684
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.372684').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.372684 
- obs record registered
- next record: 423846
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.423846').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.423846 
- obs record registered
- next record: 476614
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.476614').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.476614 
- obs record registered
- next record: 529909
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.529909').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.529909 
- obs record registered
- next record: 581984
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.581984').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.581984 
- obs record registered
- next record: 634675
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.634675').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.634675 
- obs record registered
- next record: 688831
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.688831').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.688831 
- obs record registered
- next record: 743330
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.743330').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.743330 
- obs record registered
- next record: 797329
-ruby -rjson -ryaml -e "puts YAML.load_file('/local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.yaml.797329').to_json" > /local/4759290[7].master15.delcatty.gent.vsc/74560_7_ini.yaml.buffer.json.797329 
- obs record registered
-writing table file (/user/data/gent/gvo000/gvo00090/vsc42247/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA//74560_7_ini.pkl) for station 74560
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 444d87b..8d91763 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -196,7 +196,7 @@
     records_afternoon.set_index(['STNID','dates'],inplace=True)
     ini_index_dates = records_morning.set_index(['STNID','dates']).index
     records_afternoon = records_afternoon.loc[ini_index_dates]
-    records_afternoon.index = records_morning.index
+    records_afternoon.index = records_morning.inde= run_station_chunkx
 
 experiments = args.experiments.strip(' ').split(' ')
 for expname in experiments:
@@ -233,7 +233,7 @@
             print('starting station chunk number: '\
                   +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
 
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            records_morning_station_chunk = records_morning_station.query('STNID == '+str(current_station.name)+' and chunk == '+str(run_station_chunk)) #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():

From 6fbe86b9e9ac529e21448bb4f34b07e7c2b4ef97 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 19 Sep 2018 15:06:49 +0200
Subject: [PATCH 079/129] Allways sort chunk files in the right way.

---
 class4gl/interface_functions.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 0c0cf32..1de341d 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -397,9 +397,15 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                 end_of_chunks = False
                 station_list_files = glob.glob(path_yaml+'/'+format(STNID,'05d')+'_*_'+subset+'.yaml')
                 station_list_files.sort()
+                chunks = []
                 for station_path_file in station_list_files:
-                    fn = station_path_file.split('/')[-1]
-                    chunk = int(fn.split('_')[1])
+                #    fn = station_path_file.split('/')[-1]
+                    chunks.append(int(fn.split('_')[1]))
+
+                # sort according to chunk number
+                chunks.sort()
+                for chunk in chunks:
+                    fn = glob.glob(path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml')
                     dictfnchunks.append(dict(fn=fn,chunk=chunk))
 
                 # while not end_of_chunks:

From 1f7203595afd97a39ed9a3e1b0c8553407875d2e Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 19 Sep 2018 15:19:05 +0200
Subject: [PATCH 080/129] Allways sort chunk files in the right way.

---
 class4gl/interface_functions.py     | 4 ++--
 class4gl/simulations/simulations.py | 1 +
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 1de341d..a1a2ad8 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -399,13 +399,13 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                 station_list_files.sort()
                 chunks = []
                 for station_path_file in station_list_files:
-                #    fn = station_path_file.split('/')[-1]
+                    fn = station_path_file.split('/')[-1]
                     chunks.append(int(fn.split('_')[1]))
 
                 # sort according to chunk number
                 chunks.sort()
                 for chunk in chunks:
-                    fn = glob.glob(path_yaml+'/'+format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml')
+                    fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
                     dictfnchunks.append(dict(fn=fn,chunk=chunk))
 
                 # while not end_of_chunks:
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 8d91763..ea6b9ba 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -172,6 +172,7 @@
                               subset=args.subset_forcing,
                               refetch_records=False,
                               )
+stop
 
 # note that if runtime is an integer number, we don't need to get the afternoon
 # profiles. 

From dbbe06c08703137818cdcc2365614994db2b1def Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 20 Sep 2018 22:17:57 +0200
Subject: [PATCH 081/129] matplotlib was loaded in a wrong way

---
 class4gl/simulations/batch_simulations.pbs |  2 +-
 class4gl/simulations/update_yaml_old.py    | 20 +++++++++-----------
 2 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index 54e3168..92e496c 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -7,7 +7,7 @@
 #PBS -m a
 #PBS -N c4gl_sim
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python Ruby
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 
diff --git a/class4gl/simulations/update_yaml_old.py b/class4gl/simulations/update_yaml_old.py
index 18b3071..4428cc1 100644
--- a/class4gl/simulations/update_yaml_old.py
+++ b/class4gl/simulations/update_yaml_old.py
@@ -94,8 +94,8 @@
         raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
 
 
-    if not (int(args.split_by) > 0) :
-            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    # if not (int(args.split_by) > 0) :
+    #         raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
 
     run_station_chunk = None
     print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
@@ -156,9 +156,6 @@
 # os.system('mkdir -p "'+backupdir+'"')
 
 
-
-
-
 for istation,current_station in run_stations.iterrows():
     records_forcing_station = records_forcing.query('STNID == ' +\
                                                     str(current_station.name))
@@ -205,15 +202,15 @@
 
             fn_forcing = \
                     args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                    args.subset_forcing+'.yaml'
             file_forcing = \
                 open(fn_forcing,'r')
             fn_experiment = args.path_experiments+'/'+format(current_station.name,'05d')+'_'+\
-                     args.subset_forcing+'.yaml'
+                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
             file_experiment = \
                 open(fn_experiment,'w')
             fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
-                     args.subset_forcing+'.pkl'
+                     str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
 
             # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
             #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
@@ -235,11 +232,14 @@
                   str(len(records_forcing_station_chunk) )+\
                   ' (station total: ',str(len(records_forcing_station)),')')  
             
-        
                 c4gli_forcing = get_record_yaml(file_forcing, 
                                                 record_forcing.index_start, 
                                                 record_forcing.index_end,
                                                 mode=args.mode)
+                seltropo = (c4gli_forcing.air_ac.p > c4gli_forcing.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                profile_tropo = c4gli_forcing.air_ac[seltropo]
+                mean_advt_tropo = np.mean(profile_tropo.advt_x +profile_tropo.advt_y )
+                c4gli_forcing.update(source='era-interim',pars={'advt_tropo':mean_advt_tropo})
                 
                 #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
                 
@@ -249,8 +249,6 @@
                         globaldata, 
                         only_keys=args.global_keys.strip(' ').split(' ')
                     )
-        
-
 
                 c4gli_forcing.dump(file_experiment)
                     

From 64e1c8bbe4590b240732d7470f8acfa3a235aff0 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 20 Sep 2018 22:19:18 +0200
Subject: [PATCH 082/129] matplotlib was loaded in a wrong way

---
 class4gl/setup/batch_setup_era.pbs | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/setup/batch_setup_era.pbs b/class4gl/setup/batch_setup_era.pbs
index 7735a7a..1ee9b9b 100644
--- a/class4gl/setup/batch_setup_era.pbs
+++ b/class4gl/setup/batch_setup_era.pbs
@@ -7,7 +7,7 @@
 #PBS -m a
 #PBS -N c4gl_setup
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray matplotlib Pysolar PyYAML netcdf4-python Ruby
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 

From 3a3259ffd51ada6c911b1a2127b0e6473b3773a3 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Fri, 21 Sep 2018 17:33:30 +0200
Subject: [PATCH 083/129] fix in interface_multi so that correct chunk is
 loaded in case of next_record()

---
 class4gl/class4gl.py                    |   2 +
 class4gl/interface/interface.py         |  62 ++++---
 class4gl/interface/interface_koeppen.py |   8 +-
 class4gl/interface_multi.py             |  22 +++
 class4gl/simulations/simulations.py     |   9 +-
 class4gl/simulations/update_yaml_old.py | 227 ++++++++++++------------
 6 files changed, 191 insertions(+), 139 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 7afe040..7095194 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -99,6 +99,8 @@ def __init__(self):
          'dqdt':'kg/kg/h',
          'BR': '-',
          'EF': '-',
+         'advt_x': 'K/s',
+         'advt_y': 'K/s',
 }
 
 class class4gl_input(object):
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 2e1c1be..8ef0038 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -1,4 +1,3 @@
-
 import numpy as np
 
 import pandas as pd
@@ -18,6 +17,7 @@
 parser.add_argument('--show_control_parameters',default=True)
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--figure_filename_2',default=None)
+parser.add_argument('--experiments_labels',default=None)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -91,6 +91,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     return np.sqrt(rmse_temp)
 
 
+if args.experiments_labels is None:
+    keylabels = args.experiments.strip().split(' ')
+else:
+    keylabels = args.experiments_labels.strip().split(';')
 
 
 
@@ -134,7 +138,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     symbols = ['*','x','+']
     dias = {}
     
-    for varkey in ['h','theta','q']:                                                    
+    varkeys = ['h','theta','q']
+    for varkey in varkeys:                                                    
         axes[varkey] = fig.add_subplot(2,3,i)                                       
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
@@ -342,7 +347,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
         sns.set_style('whitegrid')
         #sns.set()
-        fig = pl.figure(figsize=(12,8))
+        fig = pl.figure(figsize=(11,7))
         i = 1
         axes = {}
         data_all = pd.DataFrame()
@@ -364,22 +369,22 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         data_all = pd.DataFrame()
 
         tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
-        tempdatamodstats["source"] = "soundings"
-        tempdatamodstats["source_index"] = "soundings"
+        tempdatamodstats["source"] = "Soundings"
+        tempdatamodstats["source_index"] = "Soundings"
 
         ini_ref = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
         tempdataini_this = pd.DataFrame(ini_ref.copy())
 
         tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
         tempdatamodstats['STNID']= tempdataini_this.STNID
-        tempdatamodstats['source']= "soundings"
-        tempdatamodstats['source_index']= "soundings"
+        tempdatamodstats['source']= "Soundings"
+        tempdatamodstats['source_index']= "Soundings"
         tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
         #print('hello')
 
         tempdataini = pd.DataFrame(ini_ref)
-        tempdataini["source"] = "soundings"
-        tempdataini["source_index"] = "soundings"
+        tempdataini["source"] = "Soundings"
+        tempdataini["source_index"] = "Soundings"
         tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
         #print('hello2')
 
@@ -390,21 +395,22 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #print(data_all.shape)
 
             
-        for key in list(args.experiments.strip().split(' ')):
+        for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
+            keylabel = keylabels[ikey]
 
             tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
             tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
             tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
             tempdatamodstats['STNID']= tempdataini_this.STNID
-            tempdatamodstats['source']= key
-            tempdatamodstats['source_index']= key
+            tempdatamodstats['source']= keylabel
+            tempdatamodstats['source_index']= keylabel
             tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
             #print('hello')
 
 
             tempdataini = pd.DataFrame(ini_ref.copy())
-            tempdataini["source"] = key 
-            tempdataini["source_index"] = key
+            tempdataini["source"] = keylabel
+            tempdataini["source_index"] = keylabel
             tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
     
 
@@ -429,22 +435,25 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         for varkey in ['h','theta','q']:
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            data_all['advt_tropo'] = data_input['advt_tropo']
             #print(data_input.shape)
             #print(data_all.shape)
         #print('hello6')
         #print(data_all.columns)
         #print('hello7')
+        i = 1
         for varkey in ['h','theta','q']:
-            input_keys =['wg','cc','advt']
+            input_keys =['wg','advt_tropo']
             for input_key in input_keys:
                 varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
 
                 #print('hello8')
                 #print(data_input.shape)
                 #print(data_all.shape)
+                units['advt_tropo'] = 'K/s'
                 input_key_full = input_key + "["+units[input_key]+"]"
-                data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2)
-                data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=10,precision=2,)
+                data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2)
+                data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2,)
                 #print('hello9')
                 #print(data_input.shape)
                 #print(data_all.shape)
@@ -462,7 +471,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 #print('hello10')
                 
                 sns.set(style="ticks", palette="pastel")
-                ax = fig.add_subplot(3,2,i)
+                ax = fig.add_subplot(3,len(input_keys),i)
                 #sns.violinplot(x=input_key_full,y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
                 
                 #ax.set_title(input_key_full)
@@ -476,15 +485,28 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                      ax.get_legend().set_visible(False)
                 #     plt.legend('off')
                 if i >= 5:
+                    #ax.set_xticklabels(labels=['['+str(i)+','+str(i+1)+'[' for i in list(range(0,7))]+['[7,8]'])
+
                     ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=45.,ha='right')
                 else:
                     ax.set_xticklabels([])
                     ax.set_xlabel('')
 
-                if np.mod(i,len(input_keys)) == 0:
+                if np.mod(i,len(input_keys)) != 0:
                     ax.set_yticklabels([])
                     ax.set_ylabel('')
 
+                if varkey == 'q':
+                    ticks = ticker.FuncFormatter(lambda x, pos:
+                                                 '{0:g}'.format(x*1000.))
+                    #ax.xaxis.set_major_formatter(ticks)
+                    ax.yaxis.set_major_formatter(ticks)
+
+                    ax.set_ylabel(latex['d'+varkey+'dt']+' ['+r'$10^{-3} \times $'+units['d'+varkey+'dt']+']')        
+                else:
+                    ax.set_ylabel(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')        
+
+
                 for j,artist in enumerate(ax.artists):
                     if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
                         # Set the linecolor on the artist to the facecolor, and set the facecolor to None
@@ -521,7 +543,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 #sns.despine(offset=10, trim=True)
                 i +=1
         fig.tight_layout()
-        fig.subplots_adjust( bottom=0.18,left=0.09,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
+        fig.subplots_adjust( bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.05,hspace=0.05,)
         if args.figure_filename_2 is not None:
             fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
         fig.show()
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index 9a71bf8..1cf134d 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -328,11 +328,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         ikey = 0
         key = list(args.experiments.strip().split(' '))[ikey]
         keylabel = keylabels[ikey]
-        cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
-        clearsky = (cc < 0.05)
+        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        # clearsky = (cc < 0.05)
     
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
     
     
         nbins=40       
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 9509387..907c0f0 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -364,6 +364,9 @@ def update_station(self):
         self.update_record()
 
     def next_record(self,event=None,jump=1):
+        
+        old_chunk =  self.frames['profiles']['current_record_chunk']
+
         with suppress(StopIteration):
             (self.frames['profiles']['STNID'] , \
             self.frames['profiles']['current_record_chunk'] , \
@@ -382,6 +385,25 @@ def next_record(self,event=None,jump=1):
 
         for key in self.frames['profiles'].keys():
             self.frames['stats'][key] = self.frames['profiles'][key]
+
+        # chunk file has changed! So we need to open it!
+        if self.frames['profiles']['current_record_chunk'] != old_chunk:
+
+            STNID = self.frames['profiles']['STNID']
+            chunk = self.frames['profiles']['current_record_chunk']
+
+
+
+            if 'current_station_file_ini' in self.frames['profiles'].keys():
+                self.frames['profiles']['current_station_file_ini'].close()
+            self.frames['profiles']['current_station_file_ini'] = \
+                open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+
+            if 'current_station_file_mod' in self.frames['profiles'].keys():
+                self.frames['profiles']['current_station_file_mod'].close()
+            self.frames['profiles']['current_station_file_mod'] = \
+                open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+
         self.update_record()
 
     def prev_record(self,event=None):
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index ea6b9ba..4b81d34 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -247,8 +247,13 @@
                                                     record_morning.index_start, 
                                                     record_morning.index_end,
                                                     mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+
+                    # add tropospheric parameters on advection and subsidence
+                    seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_morning.air_ac[seltropo]
+                    for var in ['t','q','u','v',]:
+                        mean_adv_tropo = np.mean(profile_tropo['adv'+var+'_x']+profile_tropo['adv'+var+'_y'] )
+                        c4gli_morning.update(source='era-interim',pars={'adv'+var+'_tropo':mean_adv_tropo})
                     
                     
                     if args.runtime == 'from_afternoon_profile':
diff --git a/class4gl/simulations/update_yaml_old.py b/class4gl/simulations/update_yaml_old.py
index 4428cc1..aafed46 100644
--- a/class4gl/simulations/update_yaml_old.py
+++ b/class4gl/simulations/update_yaml_old.py
@@ -87,7 +87,6 @@
                                          refetch_records=False,
                                          )
 
-os.system('mkdir -p '+args.path_experiments)
 # only run a specific chunck from the selection
 if args.global_chunk_number is not None:
     if args.station_chunk_number is not None:
@@ -156,120 +155,122 @@
 # os.system('mkdir -p "'+backupdir+'"')
 
 
-for istation,current_station in run_stations.iterrows():
-    records_forcing_station = records_forcing.query('STNID == ' +\
-                                                    str(current_station.name))
-
-    records_forcing_station_chunk = records_forcing.query('STNID == ' +\
-                                                    str(current_station.name)+\
-                                                   '& chunk == '+str(run_station_chunk))
-    print('lenrecords_forcing_station: ',len(records_forcing_station))
-    print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
-    print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
+for EXP in args.experiments.strip().split(" "):
+    os.system('mkdir -p '+args.path_experiments+'/'+EXP+'/')
+    for istation,current_station in run_stations.iterrows():
+        records_forcing_station = records_forcing.query('STNID == ' +\
+                                                        str(current_station.name))
     
-    # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
-    #     print("warning: outside of profile number range for station "+\
-    #           str(current_station)+". Skipping chunk number for this station.")
-    if len(records_forcing_station_chunk) == 0:
-        print("warning: outside of profile number range for station "+\
-              str(current_station)+". Skipping chunk number for this station.")
-    else:
-        # normal case
-        if ((int(args.split_by) > 0) or \
-            (os.path.isfile(args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                 str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'))):
-            fn_forcing = \
-                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            file_forcing = \
-                open(fn_forcing,'r')
-            fn_experiment = args.path_experiments+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            file_experiment = \
-                open(fn_experiment,'w')
-            fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-
-            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+        records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+                                                        str(current_station.name)+\
+                                                       '& chunk == '+str(run_station_chunk))
+        print('lenrecords_forcing_station: ',len(records_forcing_station))
+        print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
+        print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
+        
+        # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
+        #     print("warning: outside of profile number range for station "+\
+        #           str(current_station)+". Skipping chunk number for this station.")
+        if len(records_forcing_station_chunk) == 0:
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
         else:
-            print("\
-Warning. We are choosing chunk 0 without specifying it in filename.    \
- No-chunk naming will be removed in the future."\
-                 )
-
-            fn_forcing = \
-                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                    args.subset_forcing+'.yaml'
-            file_forcing = \
-                open(fn_forcing,'r')
-            fn_experiment = args.path_experiments+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            file_experiment = \
-                open(fn_experiment,'w')
-            fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-
-            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-            #          args.subset_forcing+'.pkl'
-
-        onerun = False
-        print('starting station chunk number: '\
-              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-
-        #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-
-        # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
-        #                                                 str(current_station.name)+\
-        #                                                '& chunk == '+str(run_station_chunk))
-        isim = 0
-        for (STNID,chunk,index),record_forcing in records_forcing_station_chunk.iterrows():
-                print('starting '+str(isim+1)+' out of '+\
-                  str(len(records_forcing_station_chunk) )+\
-                  ' (station total: ',str(len(records_forcing_station)),')')  
-            
-                c4gli_forcing = get_record_yaml(file_forcing, 
-                                                record_forcing.index_start, 
-                                                record_forcing.index_end,
-                                                mode=args.mode)
-                seltropo = (c4gli_forcing.air_ac.p > c4gli_forcing.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                profile_tropo = c4gli_forcing.air_ac[seltropo]
-                mean_advt_tropo = np.mean(profile_tropo.advt_x +profile_tropo.advt_y )
-                c4gli_forcing.update(source='era-interim',pars={'advt_tropo':mean_advt_tropo})
-                
-                #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
+            # normal case
+            if ((int(args.split_by) > 0) or \
+                (os.path.isfile(args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'))):
+                fn_forcing = \
+                        args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                        str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_forcing = \
+                    open(fn_forcing,'r')
+                fn_experiment = args.path_experiments+'/'+EXP+'/'+format(current_station.name,'05d')+'_'+\
+                         str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_experiment = \
+                    open(fn_experiment,'w')
+                fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                         str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+    
+                # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+                #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+                #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+            else:
+                print("\
+    Warning. We are choosing chunk 0 without specifying it in filename.    \
+     No-chunk naming will be removed in the future."\
+                     )
+    
+                fn_forcing = \
+                        args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+                        args.subset_forcing+'.yaml'
+                file_forcing = \
+                    open(fn_forcing,'r')
+                fn_experiment = args.path_experiments+'/'+EXP+'/'+format(current_station.name,'05d')+'_'+\
+                         str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_experiment = \
+                    open(fn_experiment,'w')
+                fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
+                         str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+    
+                # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+                #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+                #          args.subset_forcing+'.pkl'
+    
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+    
+            #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+    
+            # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+            #                                                 str(current_station.name)+\
+            #                                                '& chunk == '+str(run_station_chunk))
+            isim = 0
+            for (STNID,chunk,index),record_forcing in records_forcing_station_chunk.iterrows():
+                    print('starting '+str(isim+1)+' out of '+\
+                      str(len(records_forcing_station_chunk) )+\
+                      ' (station total: ',str(len(records_forcing_station)),')')  
                 
-                if args.global_keys is not None:
-                    print(args.global_keys.strip(' ').split(' '))
-                    c4gli_forcing.get_global_input(
-                        globaldata, 
-                        only_keys=args.global_keys.strip(' ').split(' ')
-                    )
-
-                c4gli_forcing.dump(file_experiment)
+                    c4gli_forcing = get_record_yaml(file_forcing, 
+                                                    record_forcing.index_start, 
+                                                    record_forcing.index_end,
+                                                    mode=args.mode)
+                    seltropo = (c4gli_forcing.air_ac.p > c4gli_forcing.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_forcing.air_ac[seltropo]
+                    mean_advt_tropo = np.mean(profile_tropo.advt_x +profile_tropo.advt_y )
+                    c4gli_forcing.update(source='era-interim',pars={'advt_tropo':mean_advt_tropo})
                     
+                    #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
                     
-                onerun = True
-                isim += 1
-
-
-        file_forcing.close()
-        file_experiment.close()
-
-        if onerun:
-            # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
-            # if os.path.isfile(fn_forcing_pkl):
-            #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
-            # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-            # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-            records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
-                                                       args.path_experiments,\
-                                                       getchunk = int(run_station_chunk),\
-                                                       subset=args.subset_forcing,
-                                                       refetch_records=True,
-                                                       )
-
+                    if args.global_keys is not None:
+                        print(args.global_keys.strip(' ').split(' '))
+                        c4gli_forcing.get_global_input(
+                            globaldata, 
+                            only_keys=args.global_keys.strip(' ').split(' ')
+                        )
+    
+                    c4gli_forcing.dump(file_experiment)
+                        
+                        
+                    onerun = True
+                    isim += 1
+    
+    
+            file_forcing.close()
+            file_experiment.close()
+    
+            if onerun:
+                # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
+                # if os.path.isfile(fn_forcing_pkl):
+                #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
+                # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+                # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+                records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
+                                                           args.path_experiments+'/'+EXP+'/',\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset=args.subset_forcing,
+                                                           refetch_records=True,
+                                                           )
+    

From 4a41cd59241f4b4d7a3a79dd59d0ee387e3d3fe7 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 24 Sep 2018 12:48:12 +0200
Subject: [PATCH 084/129] fix station lat lon in setup_era.py.

---
 class4gl/setup/setup_era.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 96ce88f..8c7e967 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -189,8 +189,10 @@
     print(iDT,DT)
     c4gli = class4gl_input(debug_level=logging.INFO)
     c4gli.update(source='STNID'+format(STNID,'05d'),\
-                 pars=dict(latitude = float(run_station.latitude), \
+                 pars=dict(latitude  = float(run_station.latitude), \
                            longitude = float(run_station.longitude),\
+                           latitude  = float(run_station.latitude), \
+                           lon       = 0.,\
                            STNID=int(STNID)))
 
     lSunrise, lSunset = GetSunriseSunset(c4gli.pars.latitude,0.,DT)

From cddbd892d962649ca26484188ec28b63119b4699 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 24 Sep 2018 12:51:11 +0200
Subject: [PATCH 085/129] fix station lat lon in setup_era.py.

---
 class4gl/setup/setup_era.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 8c7e967..4e58c1d 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -191,9 +191,12 @@
     c4gli.update(source='STNID'+format(STNID,'05d'),\
                  pars=dict(latitude  = float(run_station.latitude), \
                            longitude = float(run_station.longitude),\
-                           latitude  = float(run_station.latitude), \
+                           lat       = float(run_station.latitude), \
+                           # Note the difference between longitude and lon. The
+                           # lon variable should always be zero because we are
+                           # always working in solar time for running CLASS
                            lon       = 0.,\
-                           STNID=int(STNID)))
+                           STNID     = int(STNID)))
 
     lSunrise, lSunset = GetSunriseSunset(c4gli.pars.latitude,0.,DT)
 

From b00c3acc484144aa85c7957ab914c12679700ebd Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 24 Sep 2018 14:31:53 +0200
Subject: [PATCH 086/129] remove stop statement.

---
 class4gl/simulations/simulations.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 4b81d34..bdb890f 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -172,7 +172,6 @@
                               subset=args.subset_forcing,
                               refetch_records=False,
                               )
-stop
 
 # note that if runtime is an integer number, we don't need to get the afternoon
 # profiles. 

From 5dd62f0bd18f316329c484917b2f991d966e0f0d Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 26 Sep 2018 11:36:13 +0200
Subject: [PATCH 087/129] fix simulations.py and implelemt vizualization of 3d
 fields in interface_multi.py

---
 class4gl/interface/interface.py     |   5 +-
 class4gl/interface_multi.py         | 206 ++++++++++++++++++++--------
 class4gl/simulations/simulations.py |   2 +-
 3 files changed, 154 insertions(+), 59 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 8ef0038..8c42180 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -1,3 +1,4 @@
+
 import numpy as np
 
 import pandas as pd
@@ -435,7 +436,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         for varkey in ['h','theta','q']:
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
-            data_all['advt_tropo'] = data_input['advt_tropo']
+            
+        data_input['advt_tropo'] = - data_input['advt_tropo']
+        data_all['advt_tropo'] = data_input['advt_tropo']
             #print(data_input.shape)
             #print(data_all.shape)
         #print('hello6')
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 907c0f0..b9da41c 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -603,47 +603,77 @@ def plot(self):
 
 
         """ buttons definitions """
+        button_height = 0.055
+        button_hspace = 0.005
+        button_width  = 0.095
+        button_wspace = 0.005
+        buttons_upper = 0.28
+        buttons_left = 0.25
+
+        button_types = ['dataset','datetime','level','station','record']
         
-        label = 'bprev_dataset'
-        axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous dataset')
-        btns[label].on_clicked(self.prev_dataset)
+        for ibutton_type,button_type in enumerate(button_types):
+            label='bprev'+button_type
+            axes[label] = fig.add_axes([
+                buttons_left,\
+                buttons_upper-ibutton_type*(button_height+button_hspace),\
+                button_width,\
+                button_height\
+                                                     ])
+            btns[label] = Button(axes[label], 'Previous '+button_type)
+            btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+
+            label='bnext'+button_type
+            axes[label] = fig.add_axes([
+                buttons_left+button_width+button_wspace,\
+                buttons_upper-ibutton_type*(button_height+button_hspace),\
+                button_width,\
+                button_height\
+                                                     ])
+            btns[label] = Button(axes[label], 'Next '+button_type)
+            btns[label].on_clicked(getattr(self, 'next_'+button_type))
 
-        label = 'bnext_dataset'
-        axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next dataset')
-        btns[label].on_clicked(self.next_dataset)
+        
+        # label = 'bprev_dataset'
+        # axes[label] = fig.add_axes([0.25,0.28,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Previous dataset')
+        # btns[label].on_clicked(self.prev_dataset)
+
+        # label = 'bnext_dataset'
+        # axes[label] = fig.add_axes([0.35,0.28,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Next dataset')
+        # btns[label].on_clicked(self.next_dataset)
 
-        label = 'bprev_datetime'
-        axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous datetime')
-        btns[label].on_clicked(self.prev_datetime)
+        # label = 'bprev_datetime'
+        # axes[label] = fig.add_axes([0.25,0.20,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Previous datetime')
+        # btns[label].on_clicked(self.prev_datetime)
 
-        label = 'bnext_datetime'
-        axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next datetime')
-        btns[label].on_clicked(self.next_datetime)
+        # label = 'bnext_datetime'
+        # axes[label] = fig.add_axes([0.35,0.20,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Next datetime')
+        # btns[label].on_clicked(self.next_datetime)
 
 
-        label = 'bprev_station'
-        axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous station')
-        btns[label].on_clicked(self.prev_station)
+        # label = 'bprev_station'
+        # axes[label] = fig.add_axes([0.25,0.12,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Previous station')
+        # btns[label].on_clicked(self.prev_station)
 
-        label = 'bnext_station'
-        axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next station')
-        btns[label].on_clicked(self.next_station)
+        # label = 'bnext_station'
+        # axes[label] = fig.add_axes([0.35,0.12,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Next station')
+        # btns[label].on_clicked(self.next_station)
 
-        label = 'bprev_record'
-        axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Previous record')
-        btns[label].on_clicked(self.prev_record)
+        # label = 'bprev_record'
+        # axes[label] = fig.add_axes([0.25,0.04,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Previous record')
+        # btns[label].on_clicked(self.prev_record)
 
-        label = 'bnext_record'
-        axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
-        btns[label] = Button(axes[label], 'Next record')
-        btns[label].on_clicked(self.next_record)
+        # label = 'bnext_record'
+        # axes[label] = fig.add_axes([0.35,0.04,0.10,0.075])
+        # btns[label] = Button(axes[label], 'Next record')
+        # btns[label].on_clicked(self.next_record)
 
 
         # self.nstatsview = nstatsview
@@ -831,15 +861,68 @@ def prev_dataset(self,event=None):
         ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
         self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
 
-
     def sel_dataset(self,inputkey):
         self.frames['worldmap']['inputkey'] = inputkey
         self.frames['stats']['inputkey'] = self.frames['worldmap']['inputkey'] # this is used for showing the percentiles per station in color.
         self.goto_datetime_worldmap(
             self.frames['profiles']['current_record_ini'].datetime.to_pydatetime(),
             'after')# get nearest datetime of the current dataset to the profile
+
+        print('seldata0')
+        if 'level' not in self.frames['worldmap'].keys():
+            levels = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev']
+            self.frames['worldmap']['level'] = np.max(levels)
+            print('seldata1')
+
+            minlev = np.min(levels)
+            maxlev = np.max(levels)
+            curlev = self.frames['worldmap']['level']
+            curlev = np.max([curlev,np.min(levels)])
+            curlev = np.min([curlev,np.max(levels)])
+            print('seldata2')
+
+            self.frames['worldmap']['level'] = curlev
+            print('seldata3')
+
+
+        print('seldata4')
+        self.sel_level(self.frames['worldmap']['level'])
+
+
+
+    def sel_level(self,level):
+
+        if 'lev' not in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims):
+            raise ValueError('lev dimension not in dataset '+self.frames['worldmap']['inputkey'])
+
+        print('seldata5')
+
+
+        if level > (np.max(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
+            raise ValueError('Level '+str(level)+' exceed those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
+        if level < (np.min(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
+            raise ValueError('Level '+str(level)+' is lower than those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
+        print('seldata6')
+        self.frames['worldmap']['level'] = level
+
+        print(level)
         if "fig" in self.__dict__.keys():
             self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
+
+        print('seldata7')
+
+    def next_level(self,event=None,jump=1):
+        if 'lev' not in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims.keys()):
+            raise ValueError('lev dimension not in dataset'+self.frames['worldmap']['inputkey'])
+        levels =  self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev']
+        level = self.frames['worldmap']['level']
+        level =  ((level + jump - min(levels)) % (max(levels)-min(levels))) + min(levels)
+        self.sel_level(level)
+
+    def prev_level(self,event=None):
+        self.next_level(jump=-1)
+
+        #self.frames['worldmap']['level'] = level: 
        
     # def prev_station(self,event=None):
     #     self.istation = (self.istation - 1) % self.stations.shape[0]
@@ -869,11 +952,14 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
         if self.globaldata is not None:
             if (only is None) or ('worldmap' in only):
                 globaldata = self.globaldata
+                print('hello0')
                 if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
                     globaldata.datasets[frames['worldmap']['inputkey']].browse_page(time=frames['worldmap']['DT'])
                     datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page.isel(time = frames['worldmap']['iDT'])
                 else:
                     datasetxr = globaldata.datasets[frames['worldmap']['inputkey']].page
+                if 'lev' in datasetxr.dims:
+                    datasetxr = datasetxr.isel(lev=self.frames['worldmap']['level'])
                 keystotranspose = ['lat','lon']
                 for key in dict(datasetxr.dims).keys():
                     if key not in keystotranspose:
@@ -881,16 +967,18 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
                 datasetxr = datasetxr.transpose(*keystotranspose)
                 datasetxr = datasetxr.sortby('lat',ascending=False)
+                print('hello1')
 
                 lonleft = datasetxr['lon'].where(datasetxr.lon > 180.,drop=True) 
                 lonleft = lonleft - 360.
+                print('hello2')
                 lonright = datasetxr['lon'].where(datasetxr.lon <= 180.,drop=True) 
                 label = 'worldmap'
                 axes[label].clear()
                 axes[label].lon = xr.concat([lonleft,lonright],'lon').values
                 axes[label].lat = np.sort(globaldata.datasets[frames['worldmap']['inputkey']].page.variables['lat'].values)[::-1] #sortby('lat',ascending=False).values
+                print('hello3')
 
-        if (only is None) or ('worldmap' in only):
             #if 'axmap' not in self.__dict__ :
             #    self.axmap = self.fig.add_axes([0.39,0.5,0.34,0.5])
             #else:
@@ -917,36 +1005,40 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
            #if 'time' in list(dict(self.datasets[self.axes['worldmap'].focus['key']].variables[self.axes['worldmap'].focus['key']].dims).keys()):
 
 
-            fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
-            fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+                fieldleft =  datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon > 180.,drop=True) 
+                fieldright = datasetxr[frames['worldmap']['inputkey']].where(datasetxr.lon <= 180.,drop=True) 
+                print('hello4')
 
-            field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
-            if 'lev' in field.dims:
-                field = field.isel(lev=-1)
+                field =xr.concat([fieldleft,fieldright],'lon') #.sortby('lat',ascending=False).values
 
-            #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
-            axes[label].axis('off')
+                #np.concatenate([viewframe.datasets['cc']['cc'].page.isel(time=0).where(viewframe.datasets['cc'].lon > 180).values,viewframe.datasets['cc']['cc'].isel(time=0).where(viewframe.datasets['cc'].lon <= 180).values],axis=1)
+                axes[label].axis('off')
+                print('hello5')
 
-            from matplotlib import cm
-            axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
-            
-            title=frames['worldmap']['inputkey']
-            if globaldata is not None: 
-                if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
-                    title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
-            axes[label].set_title(title)
-
-            label ='worldmap_colorbar'
-            axes[label].clear()
-            axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
+                from matplotlib import cm
+                axes[label].fields[label] = axes[label].imshow(field[:,:],interpolation='none',cmap = cm.viridis )
+                
+                print('hello6')
+                title=frames['worldmap']['inputkey']
+                if globaldata is not None: 
+                    if 'time' in globaldata.datasets[frames['worldmap']['inputkey']].page.variables[frames['worldmap']['inputkey']].dims:
+                        title = title+' ['+pd.to_datetime(frames['worldmap']['DT']).strftime("%Y/%m/%d %H:%M") +'UTC]'
+                axes[label].set_title(title)
+                print('hello7')
+
+                label ='worldmap_colorbar'
+                axes[label].clear()
+                axes[label].fields[label] = fig.colorbar(axes['worldmap'].fields['worldmap'],cax=axes[label],orientation='horizontal',label=frames['worldmap']['inputkey']+' ['+self.units[frames['worldmap']['inputkey']]+']')
 
 
-            # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
-            # x,y = self.gmap(lons,lats)
-            # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
-            # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+                # lons, lats = np.meshgrid(axes[label].lon,axes[label].lat)
+                # x,y = self.gmap(lons,lats)
+                # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
+                # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
 
         if (self.path_obs is not None) and \
+           (self.frames['worldmap']['inputkey'] in self.frames['stats']['records_all_stations_ini_pct'].keys()) and \
+           (self.path_obs is not None) and \
            ((only is None) or ('stats' in only) or ('stats_lightupdate' in only)):
 
             statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index bdb890f..92f0f01 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -196,7 +196,7 @@
     records_afternoon.set_index(['STNID','dates'],inplace=True)
     ini_index_dates = records_morning.set_index(['STNID','dates']).index
     records_afternoon = records_afternoon.loc[ini_index_dates]
-    records_afternoon.index = records_morning.inde= run_station_chunkx
+    records_afternoon.index = records_morning.index
 
 experiments = args.experiments.strip(' ').split(' ')
 for expname in experiments:

From 857173881ec63389869e3753b4492eb91eff1bb6 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 2 Oct 2018 10:34:19 +0200
Subject: [PATCH 088/129] add switch for calculating tropospheric variables

---
 class4gl/simulations/simulations.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 92f0f01..3746cd4 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -20,6 +20,7 @@
 parser.add_argument('--last_station_row')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=None)
 parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
                                                       # to initialize with.
                                                       # Most common options are
@@ -248,11 +249,17 @@
                                                     mode='ini')
 
                     # add tropospheric parameters on advection and subsidence
-                    seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                    profile_tropo = c4gli_morning.air_ac[seltropo]
-                    for var in ['t','q','u','v',]:
-                        mean_adv_tropo = np.mean(profile_tropo['adv'+var+'_x']+profile_tropo['adv'+var+'_y'] )
-                        c4gli_morning.update(source='era-interim',pars={'adv'+var+'_tropo':mean_adv_tropo})
+                    # (for diagnosis)
+
+                    if args.diag_tropo is not None:
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
                     
                     
                     if args.runtime == 'from_afternoon_profile':

From 5e1c6b0d9da85f96f453e795572db4283c6ba4f4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 3 Oct 2018 10:10:32 +0200
Subject: [PATCH 089/129] put warning messages instead of info messages when
 input is missing

---
 class4gl/class4gl.py | 113 +++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 109 insertions(+), 4 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 7095194..204990b 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -1271,6 +1271,104 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
             #             air_ach=pd.DataFrame({'wrho':list(wrho)}))
 
 
+    # def get_idx_in_dataset(self,
+    #                        globaldata,
+    #                        latspan = 0.5):
+    #                        lonspan = 0.5):
+    #     """ 
+    #     purpose:
+    #         get the xarray indices that are representative between the starting and
+    #         stopping time of the class simulations
+
+    #     input:
+    #         self: definition of the class input
+    #         globaldata: book of class4gl global dataset
+    #         key: key variable in the global dataset
+    #         latspan: the span of the lat coordinate
+    #         lonspan: the span of the lon coordinate
+
+
+    #     output:
+    #         itimes: time coordinates during of the class simulatios
+    #         lats: 
+    #         lons:
+    #         """
+
+    #     # first, we browse to the correct file that has the current time
+    #     if 'time' in list(globaldata.datasets[key].page[key].dims):
+    #         globaldata.datasets[key].browse_page(time=classdatetime)
+    #     
+    #     if (globaldata.datasets[key].page is not None):
+    #         # find longitude and latitude coordinates
+    #         ilats = (np.abs(globaldata.datasets[key].page.lat -
+    #                         self.pars.latitude) < latspan)
+    #         # In case we didn't find any latitude in the allowed range, we take the closest one.
+    #         if len(ilats) == 0:
+    #             ilats = np.where(\
+    #                      globaldata.datasets[key].page.lat.isin(
+    #                       globaldata.datasets[key].page.lat.sel(lat=self.pars.latitude)\
+    #                      ))[0]
+    #         ilons = (np.abs(globaldata.datasets[key].page.lon -
+    #                         self.pars.longitude) < lonspan)
+    #         # In case we didn't find any longitude in the allowed range, we take the closest one.
+    #         if len(ilon) == 0:
+    #             ilon = np.where(\
+    #                      globaldata.datasets[key].page.lon.isin(
+    #                       globaldata.datasets[key].page.lon.sel(lon=self.pars.longitude)\
+    #                      ))[0]
+    #         
+    #         # if we have a time dimension, then we look up the required timesteps during the class simulation
+    #         if 'time' in list(globaldata.datasets[key].page[key].dims):
+
+    #             DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
+    #             
+    #             idatetime = np.where((DIST) == np.min(DIST))[0][0]
+    #             #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
+    #             if key not in ['t','u','v','q']:
+    #                 if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
+    #                     idatetime += 1
+    #             
+    #             DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime_stop))
+    #             idatetimeend = np.where((DIST) == np.min(DIST))[0][0]
+    #             #print('idatetimeend',idatetimeend,globaldata.datasets[key].variables['time'].values[idatetime],classdatetimeend)
+    #             if ((globaldata.datasets[key].page.variables['time'].values[idatetimeend] > classdatetime_stop)):
+    #                 idatetimeend -= 1
+    #             idatetime = np.min((idatetime,idatetimeend))
+    #             #for gleam, we take the previous day values
+
+    #             # in case of soil temperature, we take the exact
+    #             # timing (which is the morning)
+    #             if key in ['t','u','v','q']:
+    #                 idatetimeend = idatetime
+    #             
+    #             itimes = range(idatetime,idatetimeend+1)
+    #             #print(key,'itimes',itimes)
+
+
+    #             # In case we didn't find any correct time, we take the
+    #             # closest one.
+    #             if len(itimes) == 0:
+
+
+    #                 classdatetimemean = \
+    #                     np.datetime64(self.pars.datetime_daylight + \
+    #                     dt.timedelta(seconds=int(self.pars.runtime/2.)
+    #                                 ))
+
+    #                 dstimes = globaldata.datasets[key].page.time
+    #                 time = dstimes.sel(time=classdatetimemean,method='nearest')
+    #                 itimes = (globaldata.datasets[key].page.time ==
+    #                           time)
+    #                 
+    #         else:
+    #             # we don't have a time coordinate so it doesn't matter
+    #             # what itimes is
+    #             itimes = 0
+
+    #         #multiplication by 1 is a trick to remove the array()-type in case of zero dimensions (single value).
+    #       return itimes,ilats,ilons
+
+
     def query_source(self,var):
         """ 
         purpose:
@@ -1368,16 +1466,17 @@ def check_source_globaldata(self):
                                          ignore_keys=[])
             if not source_ok:
                 source_globaldata_ok = False
+                self.logger.warning('something was wrong with the profiles')
         
             # Additional check: we exclude desert-like
             if ((self.pars.cveg is None) or pd.isnull(self.pars.cveg)):
                 source_globaldata_ok = False
-                self.logger.info('cveg  is invalid: ('+str(self.pars.cveg)+')')
+                self.logger.warning('cveg  is invalid: ('+str(self.pars.cveg)+')')
             if ((self.pars.LAI is None) or pd.isnull(self.pars.LAI)):
                 source_globaldata_ok = False
-                self.logger.info('LAI  is invalid: ('+str(self.pars.LAI)+')')
+                self.logger.warning('LAI  is invalid: ('+str(self.pars.LAI)+')')
             elif self.pars.cveg < 0.02:
-                self.logger.info('cveg  is too low: ('+str(self.pars.cveg)+')')
+                self.logger.warning('cveg  is too low: ('+str(self.pars.cveg)+')')
                 source_globaldata_ok = False
 
         return source_globaldata_ok
@@ -1632,13 +1731,19 @@ def get_lifted_index(self,timestep=-1):
 
 
 
-def blh(HAGL,THTV,WSPD,RiBc = 0.5,RiBce = 0.25):
+def blh(HAGL,THTV,WSPD,RiBc = 0.31,RiBce = 0.08):
     """ Calculate mixed-layer height from temperature and wind speed profile
 
         Input:
             HAGL: height coordinates [m]
             THTV: virtual potential temperature profile [K]
             WSPD: wind speed profile [m/s]
+            RIBc: critical Richardson Number. 
+                According to Zhang et al., 2014 (GMD), it should  equal to 0.24
+                for strongly stable boundary layers, 0.31 for weakly stable
+                boundary layers, and 0.39 for unstable boundary layers. By
+                default, it is set to the average of the three cases and an
+                error RiBce value that comprises all values.
 
         Output:
             BLH: best-guess mixed-layer height

From 3b639535fdbcefc55a825cf87c1ac6b0e6865231 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 3 Oct 2018 11:51:03 +0200
Subject: [PATCH 090/129] fix era profile input in case of missing (advection)
 values

---
 class4gl/class4gl.py           | 160 +++++++++++++++++----------------
 class4gl/setup/setup_global.py |  18 +++-
 2 files changed, 97 insertions(+), 81 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 204990b..24a4076 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -1484,6 +1484,10 @@ def check_source_globaldata(self):
     def mixed_layer_fit(self,air_ap,source,mode):
         """ 
             Purpose: 
+                make a profile fit and write it to the air_ap section of the
+                class4gl_input object (self).
+            Input:
+                air_ap: input profile
 
 
         """
@@ -1500,18 +1504,18 @@ def mixed_layer_fit(self,air_ap,source,mode):
 
 
         # Therefore, determine the sounding that are valid for 'any' column 
-        is_valid = ~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)
-        #is_valid = (air_ap.z >= 0)
+        # is_valid = ~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)
+        is_valid = (air_ap.z >= 0)
         # # this is an alternative pipe/numpy method
         # (~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)).pipe(np.where)[0]
         valid_indices = air_ap.index[is_valid].values
-        #print(valid_indices)
+        print(valid_indices)
 
 
         hvalues = {}
         if len(valid_indices) > 0:
             #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            hvalues['h_b'] ,hvalues['h_u'],hvalues['h_l']  = blh(air_ap.z,air_ap.thetav,np.sqrt(air_ap.u**2. + air_ap.u**2.))
+            hvalues['h_b'] ,hvalues['h_u'],hvalues['h_l']  = blh(air_ap.z,air_ap.thetav,np.sqrt(air_ap.u**2. + air_ap.v**2.))
             
             hvalues['h_b']  = np.max((hvalues['h_b'] ,10.))
             hvalues['h_u']  = np.max((hvalues['h_u'] ,10.)) #upper limit of mixed layer height
@@ -1526,10 +1530,11 @@ def mixed_layer_fit(self,air_ap,source,mode):
             hvalues['h_l']  =np.nan
             hvalues['h_e']  =np.nan
             hvalues['h']    =np.nan
+
         self.update(source='fit_from_'+source,pars=hvalues)
 
         if np.isnan(self.pars.h ):
-            self.pars.Ps  = nan
+            self.pars.Ps  = np.nan
 
         mlvalues = {}
         if ~np.isnan(self.pars.h ):
@@ -1731,78 +1736,6 @@ def get_lifted_index(self,timestep=-1):
 
 
 
-def blh(HAGL,THTV,WSPD,RiBc = 0.31,RiBce = 0.08):
-    """ Calculate mixed-layer height from temperature and wind speed profile
-
-        Input:
-            HAGL: height coordinates [m]
-            THTV: virtual potential temperature profile [K]
-            WSPD: wind speed profile [m/s]
-            RIBc: critical Richardson Number. 
-                According to Zhang et al., 2014 (GMD), it should  equal to 0.24
-                for strongly stable boundary layers, 0.31 for weakly stable
-                boundary layers, and 0.39 for unstable boundary layers. By
-                default, it is set to the average of the three cases and an
-                error RiBce value that comprises all values.
-
-        Output:
-            BLH: best-guess mixed-layer height
-            BLHu: upper limit of mixed-layer height
-            BLHl: lower limit of mixed-layer height
-
-    """
-    
-    #initialize error BLH
-    BLHe = 0.
-    eps = 2.#security limit
-    iTHTV_0 = np.where(~np.isnan(THTV))[0]
-    if len(iTHTV_0) > 0:
-        iTHTV_0 = iTHTV_0[0]
-        THTV_0 = THTV[iTHTV_0]
-    else:
-        THTV_0 = np.nan
-
-    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
-    
-    
-    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
-    #RiB - RiBc = 0
-    
-    #best guess of BLH
-    
-    #print("RiB: ",RiB)
-    #print("RiBc: ",RiBc)
-    
-    
-    
-    BLHi = np.where(RiB > RiBc)[0]
-    if len(BLHi ) > 0:
-        BLHi = BLHi[0]
-        #print("BLHi: ",BLHi)
-        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-        
-        # possible error is calculated as the difference height levels used for the interpolation
-        BLHu = np.max([BLH,HAGL[BLHi]-eps])
-        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
-        # calculate an alternative BLH based on another critical Richardson number (RiBce):
-        BLHi =np.where(RiB > RiBce)[0]
-        if len(BLHi ) > 0:    
-            BLHi = BLHi[0]
-                
-            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
-            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
-            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
-            
-            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
-            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
-        
-        else:
-            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-
-    else:
-        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
-        
-    return BLH,BLHu,BLHl
 
 
 
@@ -1890,6 +1823,79 @@ def get_lifted_index(startp,startt,startqv,pres,theta,endp=50000.):
     #print(endtemp)
     return endtempenv - endtemp
 
+def blh(HAGL,THTV,WSPD,RiBc = 0.31,RiBce = 0.08):
+    """ Calculate mixed-layer height from temperature and wind speed profile
+
+        Input:
+            HAGL: height coordinates [m]
+            THTV: virtual potential temperature profile [K]
+            WSPD: wind speed profile [m/s]
+            RIBc: critical Richardson Number. 
+                According to Zhang et al., 2014 (GMD), it should  equal to 0.24
+                for strongly stable boundary layers, 0.31 for weakly stable
+                boundary layers, and 0.39 for unstable boundary layers. By
+                default, it is set to the average of the three cases and an
+                error RiBce value that comprises all values.
+
+        Output:
+            BLH: best-guess mixed-layer height
+            BLHu: upper limit of mixed-layer height
+            BLHl: lower limit of mixed-layer height
+
+    """
+    
+    #initialize error BLH
+    BLHe = 0.
+    eps = 2.#security limit
+    iTHTV_0 = np.where(~np.isnan(THTV))[0]
+    if len(iTHTV_0) > 0:
+        iTHTV_0 = iTHTV_0[0]
+        THTV_0 = THTV[iTHTV_0]
+    else:
+        THTV_0 = np.nan
+
+    RiB = 9.81/THTV_0 * ( THTV - THTV_0) * HAGL / np.clip(WSPD,a_min=0.1,a_max=None)**2.
+
+    
+    
+    #RiB = 9.81/THTV_0 * ( THTV[i-1] +  (HGHT[i] - HGHT[i-1])/ - THTV_0) * HAGL / WSPD**2
+    #RiB - RiBc = 0
+    
+    #best guess of BLH
+    
+    #print("RiB: ",RiB)
+    #print("RiBc: ",RiBc)
+    
+    
+    
+    BLHi = np.where(RiB > RiBc)[0]
+    if len(BLHi ) > 0:
+        BLHi = BLHi[0]
+        #print("BLHi: ",BLHi)
+        BLH = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+        
+        # possible error is calculated as the difference height levels used for the interpolation
+        BLHu = np.max([BLH,HAGL[BLHi]-eps])
+        BLHl = np.min([BLH,HAGL[BLHi-1]+eps])
+        # calculate an alternative BLH based on another critical Richardson number (RiBce):
+        BLHi =np.where(RiB > RiBce)[0]
+        if len(BLHi ) > 0:    
+            BLHi = BLHi[0]
+                
+            BLHa = (HAGL[BLHi] - HAGL[BLHi-1])/(RiB[BLHi] -RiB[BLHi-1]) * (RiBc - RiB[BLHi-1]) + HAGL[BLHi-1]
+            BLHu = np.max([BLHu,HAGL[BLHi]-eps])
+            BLHl = np.min([BLHl,HAGL[BLHi-1]+eps])
+            
+            BLHu = np.max([BLHu,BLH + abs(BLH-BLHa)])
+            BLHl = np.min([BLHl,BLH - abs(BLH-BLHa)])
+        
+        else:
+            BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+
+    else:
+        BLH,BLHu,BLHl = np.nan, np.nan,np.nan
+        
+    return BLH,BLHu,BLHl
 
 class class4gl(model):
     """ the extension of the 'class model' class """
diff --git a/class4gl/setup/setup_global.py b/class4gl/setup/setup_global.py
index 08adbb4..b5396a5 100644
--- a/class4gl/setup/setup_global.py
+++ b/class4gl/setup/setup_global.py
@@ -175,9 +175,14 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
                 logic = dict()
                 logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+
+                # Sounding should have taken place after 3 hours before sunrise.
+                # Note that the actual simulation only start at sunrise
+                # (specified by ldatetime_daylight), so the ABL cooling af the time
+                # before sunrise is ignored by the simulation.
                 logic['daylight'] = \
-                    ((c4gli.pars.ldatetime_daylight - 
-                      c4gli.pars.ldatetime).total_seconds()/3600. <= 4.)
+                    ((c4gli.pars.ldatetime - 
+                      c4gli.pars.lSunrise).total_seconds()/3600. >= -3.)
                 
                 logic['springsummer'] = (c4gli.pars.theta > 278.)
                 
@@ -245,10 +250,15 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
                         logic_afternoon['afternoon'] = \
                             (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        # the sounding should have taken place before 2 hours
+                        # before sunset. This is to minimize the change that a
+                        # stable boundary layer (yielding very low mixed layer
+                        # heights) is formed which can not be represented by
+                        # class.
                         logic_afternoon['daylight'] = \
                           ((c4gli_afternoon.pars.ldatetime - \
-                            c4gli_afternoon.pars.ldatetime_daylight \
-                           ).total_seconds()/3600. <= 0.)
+                            c4gli_afternoon.pars.lSunset \
+                           ).total_seconds()/3600. <= -2.)
 
 
                         le3000_afternoon = \

From 182d94e28e56901960489a0623a0d723a66e63e8 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 3 Oct 2018 12:01:21 +0200
Subject: [PATCH 091/129] fix wind speed in blh calculation

---
 class4gl/class4gl.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 24a4076..9da9ba7 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -1505,6 +1505,11 @@ def mixed_layer_fit(self,air_ap,source,mode):
 
         # Therefore, determine the sounding that are valid for 'any' column 
         # is_valid = ~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)
+        
+        if len(~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)) == 0.:
+            self.logger.warning('Warning, not all profile input is valid!  Please check input fields!', air_ap)
+
+
         is_valid = (air_ap.z >= 0)
         # # this is an alternative pipe/numpy method
         # (~np.isnan(air_ap).any(axis=1) & (air_ap.z >= 0)).pipe(np.where)[0]

From 92192ad12b3da78b115243923ea36feb59d4e658 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 3 Oct 2018 12:16:45 +0200
Subject: [PATCH 092/129] check input source in get_era.py

---
 class4gl/setup/setup_era.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 4e58c1d..1674d80 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -250,6 +250,8 @@
                          source=air_ap_input_source,
                          mode=air_ap_mode)
 
+    if not c4gli.check_source_globaldata():
+        print('Warning: some input sources appear invalid')
 
     c4gli.dump(file_ini)
 

From 36b0d2eba54b5e5a809703f59552e299d290ae79 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 3 Oct 2018 16:50:01 +0200
Subject: [PATCH 093/129] full update

---
 class4gl/era_advection.py | 160 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 160 insertions(+)
 create mode 100644 class4gl/era_advection.py

diff --git a/class4gl/era_advection.py b/class4gl/era_advection.py
new file mode 100644
index 0000000..a6c8e8d
--- /dev/null
+++ b/class4gl/era_advection.py
@@ -0,0 +1,160 @@
+
+
+    self.get_idx_in_dataset(globaldata)
+    xrin = globaldata['t'].page['t']
+
+    # 3. prepare artificial xarray input datasets which will allow to make gradient calculations along the W-E directions with xarray on the fly with just one rule
+    # 3.1 we make 'left' and 'right' datasets which will be substracted for calculating gradients
+    # dataset of ilon = 1,2,3...
+    xrleft = xrin.sel(lon=xrin.lon[:-25])
+    # dataset of ilon = 0,1,2...
+    xrright = xrin.sel(lon=xrin.lon[25:])
+    
+    # 3.2 The output will be on a staggered grid with the lon coordinate to the half-level calculated just hereafer in 3.3. 
+    #     Still, we will need to full-level longitude values in the xarray dataset for calculating the grid spacing for the gradients.
+    xrright['flon'] = (('lon',), xrright.lon.values)
+    xrleft['flon'] = (('lon',), xrleft.lon.values)
+    
+    
+    # 3.3 In order to make xarray doing the calculation advection correctly, the 'left' and 'right' values that we need on each grid cell requires equal underlying longitude coordinate values. 
+    xrleft.lon.values = (xrleft.lon.values+xrright.lon.values)/2.
+    xrright.lon.values = xrleft.lon.values
+
+
+    # 4. We do similar preparations for S-N direction. Please note that the advection results for S-N and W-E direction are on different grids, that are also different from the original grid.
+    xrbottom = xrin.sel(lat=xrin.lat[:-25])
+    xrtop = xrin.sel(lat=xrin.lat[25:])
+    xrtop['flat'] = (('lat',), xrtop.lat.values)
+    xrbottom['flat'] = (('lat',), xrbottom.lat.values)
+    xrbottom.lat.values = (xrbottom.lat.values+xrtop.lat.values)/2.
+    xrtop.lat.values = xrbottom.lat.values
+    
+    
+    dia_earth = 40000000.
+
+    # for input variables (COSMO naming)
+    VARS_COSMO = ['QV','U','V','T']
+    # for output variables (ECMWF naming)
+    vars_ECMWF = ['q','u','v','t']
+
+
+    # some netcdf polishing: add units and description to netcdf output. 
+    units = dict(
+          advq_x='kg kg-1 s-1', advq_y='kg kg-1 s-1',
+          advt_x='K s-1',       advt_y='K s-1',
+          advu_x='m s-2',       advu_y='m s-2',
+          advv_x='m s-2',       advv_y='m s-2',
+          divU_x='s-1',         divU_y='s-1',
+                )
+    long_names = dict(
+          advq_x='zonal advection of specific humidity',        
+          advt_x='zonal advection of heat',                     
+          advu_x='zonal advection of zonal wind component',     
+          advv_x='zonal advection of meridional wind component',
+          divU_x='horizontal wind divergence in the zonal direction',
+          advq_y='meridional advection of specific humidity',
+          advt_y='meridional advection of heat',                            
+          advu_y='meridional advection of zonal wind component',            
+          advv_y='meridional advection of meridional wind component',
+          divU_y='horizontal wind divergence in the meridional direction',
+                )
+    #print((xrtop.flat - xrbottom.flat)/360.*dia_earth)
+    # 5. loop over each variable
+
+    # make the selections
+    xrleft_sel = xrleft.isel(time=itimes,lat=ilats,lon=ilons)
+    xrright_sel = xrright.isel(time=itimes,lat=ilats,lon=ilons)
+    xrtop_sel = xrtop.isel(time=itimes,lat=ilats,lon=ilons)
+    xrbottom_sel = xrbottom.isel(time=itimes,lat=ilats,lon=ilons)
+
+    for ivar,var in enumerate(vars_ECMWF):
+        VAR = VARS_COSMO[ivar]
+    
+
+        dims = globaldata.datasets[key].page[key].dims
+        namesmean = list(dims)
+        namesmean.remove('lev')
+        idxmean = [dims.index(namemean) for namemean in namesmean]
+        # over which dimensions we take a mean:
+        dims = globaldata.datasets[key].page[key].dims
+        namesmean = list(dims)
+        namesmean.remove('lev')
+        idxmean = [dims.index(namemean) for namemean in namesmean]
+        #6. actual calculation for the W-E direction
+        #######################################################
+        print('calculation of advection')
+        #######################################################
+
+        if var == 't':
+            self.update(source='era-interim_calc',pars={'adv'+var+'_x':\
+
+
+        ( - (xrright_sel.p**(Rdcp) * xrright_sel.u*xrright[VAR] -
+             xrleft_sel.p**(Rdcp) * xrleft_sel.u*rleft[VAR]) /\
+                ((xrright.flon - xrleft.flon) /360.*dia_earth *np.cos(xrright.lat/180.*np.pi)) /\
+                ((xrright.p**(Rdcp)+xrleft.p**(Rdcp))/2.)
+
+
+
+        self.update(source='era-interim_calc',pars={'adv'+var+'_x':\
+                               (- (xrright_sel.u*xrright_sel[var] - 
+                                   xrleft_sel.u * xrleft_sel[var]) 
+                                  /\
+                                  ((xrright_sel.flon - xrleft_sel.flon) /360.*dia_earth 
+                                   *np.cos(xrright_sel.lat/180.*np.pi))).mean(axis=tuple(idxmean)).values *1.\
+        self.update(source='era-interim_calc',pars={'adv'+var+'_x':\
+                                +\
+                               (- (xrtop_sel.u*xrtop_sel[var] - 
+                                   xrbottom_sel.u * xrbottom_sel[var]) 
+                                  /\
+                                  ((xrtop_sel.flon - xrbottom_sel.flon)\
+                                   /360.*dia_earth).mean(axis=tuple(idxmean)).values *1.
+                                                   })
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+          # over which dimensions we take a mean:
+          dims = globaldata.datasets[key].page[key].dims
+          namesmean = list(dims)
+          namesmean.remove('lev')
+          idxmean = [dims.index(namemean) for namemean in namesmean]
+          
+          value = \
+          globaldata.datasets[key].page[key].isel(time=itimes,
+                                                  lat=ilats,lon=ilons).mean(axis=tuple(idxmean)).values * 1.
+
+          # Ideally, source should be equal to the datakey of globaldata.library 
+          # or globaldata.datasets (eg., DSMW, IGBP-DIS, ERA-INTERIM etc.) 
+          #  but therefore the globaldata class requires a revision to make this work
+          self.update(source='globaldata',air_ac=pd.DataFrame({key:list(value)})) 
+

From 345e33363846ce718df26e05f870183c6b44c20d Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 24 Oct 2018 18:46:02 +0200
Subject: [PATCH 094/129] lots of updates, including load performance increase
 with unified pkl data

---
 class4gl/class4gl.py                          |    3 +-
 class4gl/interface/interface.py               |    8 +-
 class4gl/interface/interface_koeppen.py       |   87 +-
 class4gl/interface/interface_new_koeppen.py   | 1056 +++++++++++++++++
 class4gl/interface/interface_stations.py      |  121 +-
 class4gl/interface/test.test                  |  276 +++++
 class4gl/interface_functions.py               |  241 ++--
 class4gl/interface_multi.py                   |   52 +-
 class4gl/model.py                             |   14 +-
 ...up_global.py => batch_setup_global_old.py} |    0
 class4gl/setup/batch_setup_igra.pbs           |   30 +
 class4gl/setup/batch_setup_igra.py            |  187 +++
 .../{simulations => setup}/batch_update.py    |   73 +-
 class4gl/setup/setup_bllast.py                |    4 +-
 class4gl/setup/setup_global_afternoon.py      |    4 +-
 class4gl/setup/setup_goamazon.py              |    4 +-
 class4gl/setup/setup_humppa.py                |    4 +-
 .../setup/{setup_global.py => setup_igra.py}  |   48 +-
 class4gl/setup/setup_igra_pkl.py              |  359 ++++++
 class4gl/setup/update_setup.py                |  327 +++++
 class4gl/simulations/batch_simulations.py     |    2 +-
 class4gl/simulations/simulations.py           |   21 +-
 class4gl/simulations/simulations_iter.py      |  266 +++--
 .../simulations/simulations_iter_bowen.py     |  475 ++++++++
 .../{update_yaml.py => simulations_veg.py}    |  177 ++-
 class4gl/simulations/simulations_wwilt_wfc.py |   75 +-
 class4gl/simulations/update_yaml_old.py       |  276 -----
 27 files changed, 3515 insertions(+), 675 deletions(-)
 create mode 100644 class4gl/interface/interface_new_koeppen.py
 create mode 100644 class4gl/interface/test.test
 rename class4gl/setup/{batch_setup_global.py => batch_setup_global_old.py} (100%)
 create mode 100644 class4gl/setup/batch_setup_igra.pbs
 create mode 100644 class4gl/setup/batch_setup_igra.py
 rename class4gl/{simulations => setup}/batch_update.py (71%)
 rename class4gl/setup/{setup_global.py => setup_igra.py} (91%)
 create mode 100644 class4gl/setup/setup_igra_pkl.py
 create mode 100644 class4gl/setup/update_setup.py
 create mode 100644 class4gl/simulations/simulations_iter_bowen.py
 rename class4gl/simulations/{update_yaml.py => simulations_veg.py} (64%)
 delete mode 100644 class4gl/simulations/update_yaml_old.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 9da9ba7..e0a307a 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -913,7 +913,6 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
         # without lev argument), so that we can check afterwards whether the
         # data is well-fetched or not.
 
-
         for key in keys:
             if not ((key in globaldata.datasets) and \
                 (globaldata.datasets[key].page is not None) and \
@@ -967,7 +966,7 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                             idatetime = np.min((idatetime,idatetimeend))
                             #for gleam, we take the previous day values
 
-                            # in case of soil temperature, we take the exact
+                            # in case of soil temperature or profile temperature, we take the exact
                             # timing (which is the morning)
                             if key in ['t','u','v','q']:
                                 idatetimeend = idatetime
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 8c42180..b895ea4 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -135,8 +135,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     axes = {}         
     axes_taylor = {}         
     
-    colors = ['r','g','b','m']
-    symbols = ['*','x','+']
+    colors = ['r','g','b','m','y','c']
+    symbols = ['*','x','+','o']
     dias = {}
     
     varkeys = ['h','theta','q']
@@ -437,7 +437,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
             
-        data_input['advt_tropo'] = - data_input['advt_tropo']
+        data_input['advt_tropo'] = data_input['advt_tropo'] * 3600.
         data_all['advt_tropo'] = data_input['advt_tropo']
             #print(data_input.shape)
             #print(data_all.shape)
@@ -453,7 +453,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 #print('hello8')
                 #print(data_input.shape)
                 #print(data_all.shape)
-                units['advt_tropo'] = 'K/s'
+                units['advt_tropo'] = 'K/h'
                 input_key_full = input_key + "["+units[input_key]+"]"
                 data_all[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2)
                 data_input[input_key_full] = pd.cut(x=data_input[input_key].values,bins=8,precision=2,)
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index 1cf134d..ec77bb8 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -1,4 +1,3 @@
-
 import numpy as np
 
 import pandas as pd
@@ -128,24 +127,48 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       refetch_records=False
                     )
 
-
 key = args.experiments.strip(' ').split(' ')[0]
 xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
 koeppenlookuptable = pd.DataFrame()
 koeppenlookuptable['KGCID'] = pd.Series(xrkoeppen['KGCID'])
 
 
-kgccolors = {
-    'Dfa':['navy','white'],
-    'Cfb':['green','white']       ,
-    'BSk':['tan','black']      ,
-    'Csb':['lightgreen','black'] ,     
-    'Cfa':['darkgreen','white']  ,    
-    'BWh':['orange','black']      ,
-    'Aw' :['pink','black'],
-    'Dwc':['rebeccapurple','white'] ,    
-    'Dfb':['darkviolet','white']    , 
-}
+
+
+KGCID=    ['Af', 'Am', 'As', 'Aw', 'BSh', 'BSk', 'BWh', 'BWk', 'Cfa', 'Cfb','Cfc', 'Csa', 'Csb', 'Csc', 'Cwa','Cwb', 'Cwc', 'Dfa', 'Dfb', 'Dfc','Dfd', 'Dsa', 'Dsb', 'Dsc', 'Dsd','Dwa', 'Dwb', 'Dwc', 'Dwd', 'EF','ET', 'Ocean'] 
+KGCcolors=["#960000", "#FF0000", "#FF6E6E", "#FFCCCC", "#CC8D14", "#CCAA54", "#FFCC00", "#FFFF64", "#007800", "#005000", "#003200", "#96FF00", "#00D700", "#00AA00", "#BEBE00", "#8C8C00", "#5A5A00", "#550055", "#820082", "#C800C8", "#FF6EFF", "#646464", "#8C8C8C", "#BEBEBE", "#E6E6E6", "#6E28B4", "#B464FA", "#C89BFA", "#C8C8FF", "#6496FF", "#64FFFF", "#F5FFFF"]
+
+def brightness(rrggbb):
+    """ W3C brightness definition
+        input:
+            hexadecimal color in the format:
+            #RRGGBB
+        output: value between 0 and 1
+    """
+    print(rrggbb)
+    rr = int(rrggbb[1:3],16)/int('FF',16)
+    gg = int(rrggbb[3:5],16)/int('FF',16)
+    bb = int(rrggbb[5:7],16)/int('FF',16)
+    #rr = math.floor(rrggbb/10000.)
+    #gg = math.floor((rrggbb - rr*10000.)/100.)
+    #bb = rrggbb - rr*10000 - gg*100
+    return (rr * 299. + gg * 587. + bb * 114.) / 1000.
+
+kgccolors = {}
+for iKGC,KGCname in enumerate(KGCID):
+    kgccolors[KGCname] = [KGCcolors[iKGC],'white' if (brightness(KGCcolors[iKGC])<0.5) else 'black']
+
+# kgccolors = {
+#     'Dfa':['navy','white'],
+#     'Cfb':['green','white']       ,
+#     'BSk':['tan','black']      ,
+#     'Csb':['lightgreen','black'] ,     
+#     'Cfa':['darkgreen','white']  ,    
+#     'BWh':['orange','black']      ,
+#     'Aw' :['pink','black'],
+#     'Dwc':['rebeccapurple','white'] ,    
+#     'Dfb':['darkviolet','white']    , 
+# }
 kgcnames = {
     'Dfa':'snow \n fully humid \n hot summer',
     'Cfb':'green'       ,
@@ -181,12 +204,20 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
     c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
 
+print('sort the climate classes according to the amount ')
 koeppenlookuptable['amount'] = ""
+
+exclude_koeppen = ['Dfc','Cwb']
+
 for ikoeppen,koeppen in koeppenlookuptable.iterrows():
-    print(ikoeppen,':',koeppen)
-    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
-    print(np.sum(kgc_select))
-    koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
+
+    if koeppen['KGCID'] not in exclude_koeppen:
+        print(ikoeppen,':',koeppen)
+        kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
+        print(np.sum(kgc_select))
+        koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
+    else:
+        koeppenlookuptable.iloc[ikoeppen]['amount'] = 0
 
 koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
 koeppenlookuptable = koeppenlookuptable[:9]
@@ -288,6 +319,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
             mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
             obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+
+            print ('filtering classes (showing bad performance)', exclude_koeppen,' from results!')
+            filter_classes = ~(c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(exclude_koeppen))
+            mod = mod.loc[filter_classes]
+            obs = obs.loc[filter_classes]
             x, y = obs.values,mod.values
             print(key,len(obs.values))
     
@@ -331,9 +367,15 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
         # clearsky = (cc < 0.05)
     
+        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+    
         mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
         obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
-    
+        print ('filtering classes (showing bad performance)', exclude_koeppen,' from results!')
+        filter_classess = ~(c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(exclude_koeppen))
+        mod = mod.loc[filter_classes]
+        obs = obs.loc[filter_classes]
     
         nbins=40       
         x, y = obs.values,mod.values
@@ -522,6 +564,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         data_all = pd.DataFrame()
 
         tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        
+
         tempdatamodstats["source"] = "soundings"
         tempdatamodstats["source_index"] = "soundings"
 
@@ -553,6 +597,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
             tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
             tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+
             tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
             tempdatamodstats['STNID']= tempdataini_this.STNID
             tempdatamodstats['source']= keylabel
@@ -577,6 +622,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             #print('hello5')
 
 
+            print ('filtering classes (showing bad performance)', exclude_koeppen,' from results!')
             # data[varkey] = tempdatamodstats['d'+varkey+'dt']
             data_all = pd.concat([data_all,tempdatamodstats],axis=0)
             data_input = pd.concat([data_input, tempdataini],axis=0)
@@ -589,6 +635,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
             data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
             data_all['KGCname'] = data_input['KGCname']
+
+
+
+
             #print(data_input.shape)
             #print(data_all.shape)
         # xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
@@ -609,6 +659,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             #print('hello9')
             #print(data_input.shape)
             #print(data_all.shape)
+            print ('Excluding extreme values from the classes plots')
             qvalmax = data_all[varkey_full].quantile(0.999)
             qvalmin = data_all[varkey_full].quantile(0.001)
             select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
new file mode 100644
index 0000000..a7bd245
--- /dev/null
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -0,0 +1,1056 @@
+import numpy as np
+import pandas as pd
+import sys
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False)
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--show_control_parameters',default=True)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--figure_filename_2',default=None)
+parser.add_argument('--experiments_labels',default=None)
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+import xarray as xr
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+if args.experiments_labels is None:
+    keylabels = args.experiments.strip().split(' ')
+else:
+    keylabels = args.experiments_labels.strip().split(';')
+
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if bool(args.load_globaldata):
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = {}
+for key in args.experiments.strip(' ').split(' '):
+    
+    c4gldata[key] = c4gl_interface_soundings( \
+                      args.path_experiments+'/'+key+'/',\
+                      args.path_forcing+'/',\
+                      globaldata,\
+                      refetch_records=False
+                    )
+
+sns.reset_orig()
+key = args.experiments.strip(' ').split(' ')[0]
+xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
+koeppenlookuptable = pd.DataFrame()
+koeppenlookuptable['KGCID'] = pd.Series(xrkoeppen['KGCID'])
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    xlim = axis.get_xlim()
+    ylim = axis.get_ylim()
+    x_vals = np.array([np.min([xlim[0],ylim[0]]),np.max([xlim[1],ylim[1]])])
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+KGCID=    ['Af', 'Am', 'As', 'Aw', 'BSh', 'BSk', 'BWh', 'BWk', 'Cfa', 'Cfb','Cfc', 'Csa', 'Csb', 'Csc', 'Cwa','Cwb', 'Cwc', 'Dfa', 'Dfb', 'Dfc','Dfd', 'Dsa', 'Dsb', 'Dsc', 'Dsd','Dwa', 'Dwb', 'Dwc', 'Dwd', 'EF','ET', 'Ocean'] 
+KGCcolors=["#960000", "#FF0000", "#FF6E6E", "#FFCCCC", "#CC8D14", "#CCAA54", "#FFCC00", "#FFFF64", "#007800", "#005000", "#003200", "#96FF00", "#00D700", "#00AA00", "#BEBE00", "#8C8C00", "#5A5A00", "#550055", "#820082", "#C800C8", "#FF6EFF", "#646464", "#8C8C8C", "#BEBEBE", "#E6E6E6", "#6E28B4", "#B464FA", "#C89BFA", "#C8C8FF", "#6496FF", "#64FFFF", "#F5FFFF"]
+
+def brightness(rrggbb):
+    """ W3C brightness definition
+        input:
+            hexadecimal color in the format:
+            #RRGGBB
+        output: value between 0 and 1
+    """
+    print(rrggbb)
+    rr = int(rrggbb[1:3],16)/int('FF',16)
+    gg = int(rrggbb[3:5],16)/int('FF',16)
+    bb = int(rrggbb[5:7],16)/int('FF',16)
+    #rr = math.floor(rrggbb/10000.)
+    #gg = math.floor((rrggbb - rr*10000.)/100.)
+    #bb = rrggbb - rr*10000 - gg*100
+    return (rr * 299. + gg * 587. + bb * 114.) / 1000.
+
+kgccolors = {}
+for iKGC,KGCname in enumerate(KGCID):
+    kgccolors[KGCname] = [KGCcolors[iKGC],'white' if (brightness(KGCcolors[iKGC])<0.5) else 'black']
+
+# kgccolors = {
+#     'Dfa':['navy','white'],
+#     'Cfb':['green','white']       ,
+#     'BSk':['tan','black']      ,
+#     'Csb':['lightgreen','black'] ,     
+#     'Cfa':['darkgreen','white']  ,    
+#     'BWh':['orange','black']      ,
+#     'Aw' :['pink','black'],
+#     'Dwc':['rebeccapurple','white'] ,    
+#     'Dfb':['darkviolet','white']    , 
+# }
+kgcnames = {
+    'Dfa':'snow \n fully humid \n hot summer',
+    'Cfb':'green'       ,
+    'BSk':'4'      ,
+    'Csb':'5'      ,
+    'Cfa':'darkgreen' ,     
+    'BWh':'6'      ,
+    'Aw' :'7'     ,
+    'Dwc':'8'     ,
+    'Dfb':'9'     ,
+    #'Dfa':'',
+}
+for KGCID in list(pd.Series(xrkoeppen['KGCID'])):
+    if KGCID not in kgcnames.keys():
+        kgcnames[KGCID] = KGCID
+    if KGCID not in kgccolors.keys():
+        kgccolors[KGCID] = ['k','k']
+
+
+koeppenlookuptable['color'] = ""
+koeppenlookuptable['textcolor'] = ""
+koeppenlookuptable['name'] = ""
+for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+    print(ikoeppen)
+    print(koeppen.KGCID)
+    print(kgccolors[koeppen.KGCID])
+    koeppenlookuptable['color'].loc[ikoeppen] = kgccolors[koeppen.KGCID][0]
+    koeppenlookuptable['textcolor'].loc[ikoeppen] = kgccolors[koeppen.KGCID][1]
+    koeppenlookuptable['name'].loc[ikoeppen] = kgcnames[koeppen.KGCID]
+
+
+
+c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
+    c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
+
+print('sort the climate classes according to the amount ')
+koeppenlookuptable['amount'] = ""
+
+#exclude_koeppen = ['Dfc','Cwb']
+
+for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+
+    print(ikoeppen,':',koeppen)
+    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
+    print(np.sum(kgc_select))
+    koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
+
+koeppenlookuptable = koeppenlookuptable[koeppenlookuptable.amount >= 200]
+koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
+# koeppenlookuptable = koeppenlookuptable[:9]
+include_koeppen = list(koeppenlookuptable.KGCID)
+
+
+if args.make_figures:
+    fig = plt.figure(figsize=(11,7))   #width,height
+    i = 1                                                                           
+    axes = {}         
+    axes_taylor = {}         
+    
+    colors = ['r','g','b','m','y','purple','orange','sienna','navy']
+    symbols = ['*','x','+']
+    dias = {}
+
+
+    i = 1
+    for varkey in ['h','theta','q']:                                                    
+        dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        axes[varkey] = fig.add_subplot(2,3,i)                                       
+        #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+    
+        #print(obs.std())
+        #if i == 2:
+        dias[varkey]._ax.axis["left"].label.set_text(\
+            "Normalized standard deviation")
+        if i == 1:
+            axes[varkey].annotate('Normalized standard deviation',\
+                        xy= (0.05,0.27),
+                        color='black',
+                        rotation=90.,
+                        xycoords='figure fraction',
+                        weight='normal',
+                        fontsize=10.,
+                        horizontalalignment='center',
+                        verticalalignment='center' ,
+                        #bbox={'edgecolor':'black',
+                        #      'boxstyle':'circle',
+                        #      'fc':koeppen.color,
+                        #      'alpha':1.0}
+                       )
+
+
+
+            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # Q95 = obs.quantile(0.95)
+        # Q95 = obs.quantile(0.90)
+        # Add RMS contours, and label them
+        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        #dia._ax.set_title(season.capitalize())
+    
+        dias[varkey].add_grid()
+    
+    
+        #dia.ax.plot(x99,y99,color='k')
+        i += 1
+    
+    i = 1
+    for varkey in ['h','theta','q']:                                                    
+        #for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
+            # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+            # clearsky = (cc < 0.05)
+            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+
+            print ('filtering classes that have sufficient samples: ', include_koeppen)
+            filter_classes = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
+            mod = mod.loc[filter_classes]
+            obs = obs.loc[filter_classes]
+            x, y = obs.values,mod.values
+            print(key,len(obs.values))
+    
+            STD_OBS = obs.std()
+            #scores
+            PR = pearsonr(mod,obs)[0]
+            RMSE = rmse(obs,mod)                                               
+            BIAS = np.mean(mod) - np.mean(obs)
+            STD = mod.std()
+            
+            # fit = np.polyfit(x,y,deg=1)
+            # axes[varkey].plot(x, fit[0] * x + fit[1],\
+            #                   color=colors[ikey],alpha=0.8,lw=2,\
+            #                   label=key+", "+\
+            #                               'R = '+str(round(PR,3))+', '+\
+            #                               'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
+            #                               'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
+            # axes[varkey].legend(fontsize=5)
+            
+            # print(STD)
+            # print(PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,\
+                           marker='o',ls='', mfc='white',mec='black',
+                           zorder=-100,
+                           ms=10.*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                                np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                           # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                           # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                           )
+            dias[varkey].add_sample(STD/STD_OBS, PR,\
+                           marker='o',ls='', mfc='none',mec='black',
+                           zorder=700,
+                           ms=10.*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                                np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                           # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                           # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                           )
+            dias[varkey].add_sample(STD/STD_OBS, PR,\
+                           marker='o',ls='', mfc='none',mec='black',
+                           zorder=700,
+                           ms=1.
+                           # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                           # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                           )
+            # dias[varkey].add_sample(STD/STD_OBS, PR,\
+            #                    annotate='All', alpha=1.,color='black',weight='bold',fontsize=5.,\
+            #                 zorder=100,\
+            #                         bbox={'edgecolor':'black','boxstyle':'circle','alpha':0.0}\
+            #                 )
+    
+        # put ticker position, see
+        # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+        # dia.ax.axis['bottom'].
+        # dia.ax.axis['left'].
+        # dia.ax.axis['left'].
+    
+        i += 1
+
+    i = 1
+    for varkey in ['h','theta','q']:                                                    
+
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
+            icolor = 0
+            for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+                print(ikoeppen,':',koeppen)
+                kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
+                koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
+                koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
+    
+                #axes[varkey].scatter(koeppen_obs,koeppen_mod,marker=symbols[ikoeppen],color=colors[ikey])
+                         #  label=key+", "+\
+                         #                    'R = '+str(round(PR[0],3))+', '+\
+                         #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                         #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+    
+    
+    
+            # # pl.scatter(obs,mod,label=key+", "+\
+            # #                              'R = '+str(round(PR[0],3))+', '+\
+            # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+            # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+                
+                dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                               pearsonr(koeppen_mod,koeppen_obs)[0],
+                               marker='o',linewidth=0.5,
+                                        mfc=koeppen.color,mec='black',#koeppen.color,
+                                        zorder=300+icolor,
+                               ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                               # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                               # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                               )
+                dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                               pearsonr(koeppen_mod,koeppen_obs)[0],
+                               marker='o',linewidth=0.5,
+                                        mfc=koeppen.color,mec='black',#koeppen.color,
+                                        zorder=301+icolor, ms=1
+                               # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                               # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                               )
+
+
+                # dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                #                pearsonr(koeppen_mod,koeppen_obs)[0],
+                #                         marker='o',linewidth=0.5, mfc='none',mec=str(koeppen.color),
+                #                         zorder=600+icolor,
+                #                ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                #                # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                #                # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                #                )
+
+                icolor += 1
+    
+            latex = {}
+            latex['dthetadt'] =  r'$d \theta / dt $'
+            latex['dqdt'] =      r'$d q / dt $'
+            latex['dhdt'] =      r'$d h / dt $'
+    
+            axes[varkey].set_xlabel('Observed')     
+
+
+            if varkey == 'q':
+                units_final = r'[$g\, kg^{-1}\, h^{-1}$]'
+            elif varkey == 'theta':
+                units_final = r'[$K\, h^{-1}$]'
+            elif varkey == 'h':
+                units_final = r'[$m\, h^{-1}$]'
+
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12.)                                     
+        if i==1:                                    
+            axes[varkey].set_ylabel('Modelled')                                            
+        abline(1,0,axis=axes[varkey])
+        i +=1
+
+    
+
+    i = 1
+    for varkey in ['h','theta','q']:                                                    
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        keylabel = keylabels[ikey]
+        # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
+        # clearsky = (cc < 0.05)
+    
+        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+    
+        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        print ('filtering classes that have sufficient samples: ', include_koeppen)
+        filter_classess = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
+        mod = mod.loc[filter_classes]
+        obs = obs.loc[filter_classes]
+    
+        nbins=40       
+        x, y = obs.values,mod.values
+        
+        xi, yi = np.mgrid[x.min():x.max():nbins*1j, y.min():y.max():nbins*1j]
+        zi = np.zeros_like(xi)*np.nan       
+        for ibin in range(nbins):
+            xmin = x.min() + ibin * (x.max() - x.min())/nbins
+            xmax = xmin + (x.max() - x.min())/nbins
+            in_bin = ((x >= xmin) & (x < xmax))
+            ybin = y[in_bin]
+            xbin = x[in_bin]
+            if len(ybin) > 20:
+                k = kde.gaussian_kde((ybin))
+                zi[ibin] = k(np.vstack([yi[ibin].flatten()]))
+        zi = zi/np.sum(zi,axis=1)[:,np.newaxis]
+        zi_int = zi.cumsum(axis=1) 
+                     #  label=key+", "+\
+                     #                    'R = '+str(round(PR[0],3))+', '+\
+                     #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                     #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+        axes[varkey].contour(xi, yi, zi_int.reshape(xi.shape),levels=[0.25,0.5,0.75] ,
+                colors=['darkred','lightgreen','darkred'],linewidths=[1,2,1])
+        axes[varkey].contourf(xi, yi, zi_int.reshape(xi.shape),levels=[0.25,0.75] ,
+                colors=['darkred'],alpha=0.5,)
+
+        # if varkey == 'q':
+        nani = np.concatenate([xi[zi != np.nan],yi[zi != np.nan]])
+        # axes[varkey].set_ylim((np.percentile(nani,20),np.percentile(nani,80)))
+        # #axes[varkey].set_ylim((nani.min(),nani.max()))
+        # print(varkey,(nani.min(),nani.max()))
+    
+    
+        latex = {}
+        latex['dthetadt'] =  r'$d \theta / dt $'
+        latex['dqdt'] =      r'$d q / dt $'
+        latex['dhdt'] =      r'$d h / dt $'
+    
+        axes[varkey].set_xlabel('observations')     
+
+        if varkey == 'q':
+            units_final = r'[$\mathrm{g\, kg^{-1}\, h^{-1}}$]'
+        elif varkey == 'theta':
+            units_final = r'[$\mathrm{K\, h^{-1}}$]'
+        elif varkey == 'h':
+            units_final = r'[$\mathrm{m\, h^{-1}}$]'
+
+        if varkey == 'q':
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=15)        
+        elif varkey == 'theta':
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=15)
+        elif varkey == 'h':
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=15)
+       #  c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname']
+
+        PR = pearsonr(mod,obs)[0]
+        RMSE = rmse(obs,mod)                                               
+        BIAS = np.mean(mod) - np.mean(obs)
+        STD = mod.std()
+    
+        
+        axes[varkey].scatter(obs,mod, label='All',s=0.1,alpha=0.14,color='k')
+
+
+
+        #axes[varkey].legend(fontsize=5)
+
+        #trans = ax.get_xaxis_transform() # x in data untis, y in axes fraction
+        if varkey == 'q':
+            annotate_text = \
+                           'RMSE = '+format((RMSE*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+ '\n'+\
+                           'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
+                           r'$R$ = '+format(PR,'0.2f')
+        elif varkey == 'h':
+            annotate_text = \
+                            'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                            'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                            r'$R$ = '+format(PR,'0.2f')
+        else:
+            annotate_text = \
+                            'RMSE = '+format(RMSE,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                            'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                            r'$R$ = '+format(PR,'0.2f')
+
+
+        ann = axes[varkey].annotate(annotate_text, xy=(0.05, .98 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='left', verticalalignment='top' 
+        )
+
+        if varkey == 'q':
+            print('get_xlim not working well...STRANGE')
+            limits =  [np.percentile(nani,1),np.percentile(nani,99)]
+        else:
+            limits =  [np.percentile(nani,1.0),np.percentile(nani,99.0)]
+
+        axes[varkey].set_xlabel('Observed')     
+        if i==1:                                    
+            axes[varkey].set_ylabel('Modelled')                                            
+        abline(1,0,axis=axes[varkey])
+
+
+        i +=1
+
+        #axes[varkey].axis('equal')
+        axes[varkey].set_aspect('equal')
+        axes[varkey].set_xlim(limits)
+        axes[varkey].set_ylim(limits)
+
+        # To specify the number of ticks on both or any single axes
+        # plt.locator_params(axis='x', nbins=6)
+        #plt.locator_params( nbins=10)
+        axes[varkey].xaxis.set_major_locator(ticker.MaxNLocator(4))
+        axes[varkey].yaxis.set_major_locator(ticker.MaxNLocator(4))
+        # axes[varkey].xaxis.set_major_locator(ticker.MultipleLocator(5))
+        # axes[varkey].yaxis.set_major_locator(ticker.MultipleLocator(5))
+
+        if varkey == 'q':
+            ticks = ticker.FuncFormatter(lambda x, pos:
+                                         '{0:g}'.format(x*1000.))
+            axes[varkey].xaxis.set_major_formatter(ticks)
+            axes[varkey].yaxis.set_major_formatter(ticks)
+
+        #     # axes[varkey].set_xticklabels(labels=ax.get_xticklabels()*1000.)
+        #     # axes[varkey].set_yticklabels(labels=ax.get_yticklabels()*1000.)
+    
+    
+    # # legend for different forcing simulations (colors)
+    # ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # for ikey,key in enumerate(args.experiments.strip().split(' ')):
+    #     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+    #     leg.append(leg1)
+    # ax.axis('off')
+    # #leg1 =
+    # ax.legend(leg,list(args.experiments.strip().split(' ')),loc=2,fontsize=10)
+    
+    
+    # # legend for different stations (symbols)
+    # ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # isymbol = 0
+    # for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+    #     leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+    #     leg.append(leg1)
+    #     isymbol += 1
+    # 
+    # # symbol for all stations
+    # leg1, = ax.plot([],'ko',markersize=10)
+    # leg.append(leg1)
+    
+    # ax.axis('off')
+    # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    
+    
+    fig.subplots_adjust(top=0.95,bottom=0.09,left=0.08,right=0.94,hspace=0.35,wspace=0.29)
+    
+    
+    #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+    # figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/global_eval_report_cs.png'
+    # fig.savefig(figfn,dpi=200); print("Image file written to:", figfn)
+    
+    if args.figure_filename is not None:
+        fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+    fig.show()  
+
+    koeppenlookuptable = koeppenlookuptable.sort_index()
+    if bool(args.show_control_parameters):
+
+
+        pkmn_type_colors = [
+                                            '#A0A0A0',  # Poison
+                                            '#78C850',  # Grass
+                                            '#F08030',  # Fire
+                                            '#6890F0',  # Water
+                                            '#F08030',  # Fire
+                                            '#C03028',  # Fighting
+                                            '#F85888',  # Psychic
+                                            '#A8B820',  # Bug
+                                            '#A8A878',  # Normal
+                                            '#F8D030',  # Electric
+                                            '#E0C068',  # Ground
+                                            '#EE99AC',  # Fairy
+                                            '#B8A038',  # Rock
+                                            '#705898',  # Ghost
+                                            '#98D8D8',  # Ice
+                                            '#7038F8',  # Dragon
+                                           ]
+
+
+
+        #sns.set()
+        #fig = pl.figure(figsize=(11,7))
+        i = 1
+        #axes = {}
+        data_all = pd.DataFrame()
+        data_input = pd.DataFrame()
+        
+        
+        
+        # #for varkey in ['theta','q']:     
+        # EF =\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR/(1.+\
+        #     c4gldata[key].frames['stats']['records_all_stations_ini'].BR)
+        # EF[EF<0] = np.nan
+        # EF[EF>1] = np.nan
+        
+        # c4gldata[key].frames['stats']['records_all_stations_ini']['EF'] = EF
+        
+        ikey = 0
+        key = list(args.experiments.strip().split(' '))[ikey]
+        data_all = pd.DataFrame()
+
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        
+
+        tempdatamodstats["source"] = "soundings"
+        tempdatamodstats["source_index"] = "soundings"
+
+        ini_ref = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+        tempdataini_this = pd.DataFrame(ini_ref.copy())
+
+        tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+        tempdatamodstats['STNID']= tempdataini_this.STNID
+        tempdatamodstats['source']= "soundings"
+        tempdatamodstats['source_index']= "soundings"
+        tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+        #print('hello')
+
+        tempdataini = pd.DataFrame(ini_ref)
+        tempdataini["source"] = "soundings"
+        tempdataini["source_index"] = "soundings"
+        tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+        #print('hello2')
+
+
+        data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+        data_input = pd.concat([data_input,tempdataini],axis=0)
+        #print(data_input.shape)
+        #print(data_all.shape)
+
+            
+        for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
+            keylabel = keylabels[ikey]
+
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
+
+            tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
+            tempdatamodstats['STNID']= tempdataini_this.STNID
+            tempdatamodstats['source']= keylabel
+            tempdatamodstats['source_index']= keylabel
+            tempdatamodstats.set_index(['source_index','STNID','dates'],inplace=True)
+            #print('hello')
+
+
+            tempdataini = pd.DataFrame(ini_ref.copy())
+            tempdataini["source"] = keylabel
+            tempdataini["source_index"] = keylabel
+            tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
+    
+
+            #print('hello2')
+            index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
+            #print('hello3')
+
+            tempdataini = tempdataini.loc[index_intersect]
+            #print('hello4')
+            tempdatamodstats = tempdatamodstats.loc[index_intersect]
+            #print('hello5
+
+            # data[varkey] = tempdatamodstats['d'+varkey+'dt']
+            data_all = pd.concat([data_all,tempdatamodstats],axis=0)
+            data_input = pd.concat([data_input, tempdataini],axis=0)
+            #print(data_input.shape)
+            #print(data_all.shape)
+
+        data_input.cc = data_input.cc.clip(0.,+np.inf)
+
+        for varkey in ['h','theta','q']:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+            data_all = data_all.rename(columns={'d'+varkey+'dt':varkey_full})
+            data_all['KGCname'] = data_input['KGCname']
+
+
+
+
+            #print(data_input.shape)
+            #print(data_all.shape)
+        # xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
+        # lookuptable = pd.Series(xrkoeppen['KGCID'])
+        # data_all['KGCname'] = data_input['KGC'].map(lookuptable)
+        #print('hello6')
+        #print(data_all.columns)
+        #print('hello7')
+
+        
+
+
+        varkeys = ['h','theta','q']
+        for varkey in varkeys:
+            #input_keys =['wg','cc']
+            #for input_key in input_keys:
+            varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+
+            #print('hello8')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            #input_key_full = input_key + "["+units[input_key]+"]"
+            #print('hello9')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            print ('Excluding extreme values from the classes plots')
+            qvalmax = data_all[varkey_full].quantile(0.999)
+            qvalmin = data_all[varkey_full].quantile(0.001)
+            select_data = (data_all[varkey_full] >= qvalmin) & (data_all[varkey_full] < qvalmax)
+            #print('hello11')
+            data_all = data_all[select_data]
+            #print('hello12')
+            data_input = data_input[select_data.values]
+
+            data_input = data_input[data_all.KGCname.isin(list(koeppenlookuptable.KGCID))]
+            data_all = data_all[data_all.KGCname.isin(list(koeppenlookuptable.KGCID))]
+            #print('hello13')
+            #print(data_input.shape)
+            #print(data_all.shape)
+            #print('hello10')
+            
+
+        #sns.set(style="ticks", palette="deep")
+
+        
+        exppairs = {'obs|ref'     :['soundings','GLOBAL_ADV'],
+                    'fcap|wilt'   :['GLOBAL_ADV_FC'  ,'GLOBAL_ADV_WILT'],
+                    'allveg|noveg':['GLOBAL_ADV_VMAX','GLOBAL_ADV_VMIN']
+                   }
+        current_palette = sns.color_palette('deep')
+        exppalettes = {'obs|ref'     :['white','grey'],
+                    'fcap|wilt'   :[current_palette[0],current_palette[3]],
+                       'allveg|noveg':[current_palette[2],current_palette[8]]
+                   }
+
+        data_all['expname'] = ""
+        print('making alternative names for legends')
+        expnames = {'soundings':'obs',\
+                    'GLOBAL_ADV':'ref',\
+                    'GLOBAL_ADV_WILT':'dry',\
+                    'GLOBAL_ADV_FC':'wet',\
+                    'GLOBAL_ADV_VMIN':'noveg',\
+                    'GLOBAL_ADV_VMAX':'fullveg',\
+                   }
+        for expname_orig,expname in expnames.items():
+            data_all['expname'][data_all['source'] == expname_orig] = expname
+
+        data_all['exppair'] = ""
+        for exppairname,exppair in exppairs.items():
+            data_all['exppair'][  data_all['source'].isin(exppair)  ] = exppairname
+
+        icolor = 0
+        
+        fig, axes = plt.subplots(nrows=len(varkeys)*len(koeppenlookuptable), \
+                                 ncols=len(exppairs), \
+                                 figsize=(8, 13), #width, height
+                                 sharex='col',
+                                 #gridspec_kw=dict(height_ratios=(1, 3), 
+                                 gridspec_kw=dict(hspace=0.20,wspace=0.08,top=0.94,bottom=0.06,left=0.15,right=0.99))
+
+        data_all['d'+varkey+'dt ['+units[varkey]+'/h]'] *= 1000.  
+
+        icol = 0
+        irow = 0
+        sns.set_style('whitegrid')
+        for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+            for exppairname,exppair in exppairs.items():
+                for varkey in varkeys:
+                    varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+                    ax = axes[irow,icol]
+
+                 #   axes[i] = fig.add_subplot(len(varkeys)*len(koeppenlookuptable),len(exppairs),icolor)
+            #sns.violinplot(x='KGC',y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+            
+            #ax.set_title(input_key_full)
+                    current_data = data_all[(data_all['exppair'] == exppairname) & (data_all['KGCname']  == koeppen.KGCID)]
+                    sns.violinplot(y='exppair', x=varkey_full,
+                                        hue="expname",split=True,
+                             palette=exppalettes[exppairname],
+                            # palette=["m", "g",'r','b'],
+                             linewidth=1.0,inner='quart',
+                                        data=current_data,sym='',legend=False,ax=ax)
+                    ax.legend("")
+                    ax.legend_.draw_frame(False)
+                    ax.set_yticks([])
+                    ax.set_ylabel("")
+
+                    # if varkey == 'q':
+                    #     ticks = ticker.FuncFormatter(lambda x, pos:
+                    #                                  '{0:g}'.format(x*1000.))
+                    #     ax.xaxis.set_major_formatter(ticks)
+
+                    if varkey == 'q':
+                        title_final = r'$dq/dt$'
+                        xlabel_final = r'[$\mathrm{g\, kg^{-1}\, h^{-1}}$]'
+                    elif varkey == 'theta':
+                        title_final = r'$d\theta/dt$'
+                        xlabel_final = r'[$\mathrm{K\, h^{-1}}$]'
+                    elif varkey == 'h':
+                        title_final = r'$dh/dt$'
+                        xlabel_final = r'[$\mathrm{m\, h^{-1}}$]'
+
+
+                    ax.set_xlabel("")
+                    #sns.despine(left=True, right=True, bottom=False, top=False)
+                    if irow == (len(varkeys)*len(koeppenlookuptable)-1):
+                        #ax.set_frame_on(False)
+
+                        ax.set_xlabel(xlabel_final)
+                        ax.tick_params(top='off', bottom='on', left='off',
+                                        right='off', labelleft='off',
+                                        labeltop='off',
+                                        labelbottom='on'
+                                      )
+                        ax.spines['top'].set_visible(False)
+                        ax.spines['bottom'].set_visible(True)
+                        ax.spines['left'].set_visible(True)
+                        ax.spines['right'].set_visible(True)
+                        #sns.despine(left=True, right=True, bottom=True, top=False)
+                    elif irow == 0:
+                        ax.set_title(title_final,fontsize=17.)
+                        ax.tick_params(top='off', bottom='off', left='off',
+                                        right='off', labelleft='off',
+                                        labelbottom='off')
+                        #ax.set_frame_on(False)
+                        # ax.spines['left'].set_visible(True)
+                        # ax.spines['right'].set_visible(True)
+                        ax.spines['top'].set_visible(True)
+                        ax.spines['bottom'].set_visible(False)
+                        ax.spines['left'].set_visible(True)
+                        ax.spines['right'].set_visible(True)
+                        #sns.despine(left=True, right=True, bottom=False, top=True)
+                        #ax.axis("off")
+                    elif np.mod(irow,len(exppairs)) == 0:
+                        ax.tick_params(top='off', bottom='off', left='off',
+                                        right='off', labelleft='off',
+                                        labelbottom='off')
+                        #ax.set_frame_on(False)
+                        # ax.spines['left'].set_visible(True)
+                        # ax.spines['right'].set_visible(True)
+                        ax.spines['top'].set_visible(True)
+                        ax.spines['bottom'].set_visible(False)
+                        ax.spines['left'].set_visible(True)
+                        ax.spines['right'].set_visible(True)
+                        #sns.despine(left=True, right=True, bottom=False, top=True)
+                        #ax.axis("off")
+                    elif np.mod(irow,len(exppairs)) == 2:
+                        ax.tick_params(top='off', bottom='on', left='off',
+                                        right='off', labelleft='off',
+                                        labelbottom='off')
+                        #ax.set_frame_on(False)
+                        # ax.spines['left'].set_visible(True)
+                        # ax.spines['right'].set_visible(True)
+                        ax.spines['top'].set_visible(False)
+                        ax.spines['bottom'].set_visible(True)
+                        ax.spines['left'].set_visible(True)
+                        ax.spines['right'].set_visible(True)
+                        #sns.despine(left=True, right=True, bottom=False, top=True)
+                        #ax.axis("off")
+                    else:
+                        ax.tick_params(top='off', bottom='off', left='off',
+                                        right='off', labelleft='off',
+                                        labelbottom='off')
+                        #ax.set_frame_on(False)
+                        #ax.spines['left'].set_visible(True)
+                        #ax.spines['right'].set_visible(True)
+                        ax.spines['top'].set_visible(False)
+                        ax.spines['bottom'].set_visible(False)
+                        ax.spines['left'].set_visible(True)
+                        ax.spines['right'].set_visible(True)
+                        #ax.axis("off")
+                    icol +=1
+                irow +=1
+                icol=0
+
+        idx = 0
+        for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+            ax.annotate(koeppen.KGCID,
+                        xy= (0.01,0.09 + (1.-0.12)*(1. - (idx+.5)/len(koeppenlookuptable))),
+                        color=koeppen.textcolor, 
+                        family='monospace',
+                        xycoords='figure fraction',
+                        weight='bold',
+                        fontsize=8.,
+                        horizontalalignment='top',
+                        verticalalignment='left' ,
+                        bbox={'edgecolor':'black',
+                              'boxstyle':'circle',
+                              'fc':koeppen.color,
+                              'alpha':1.0}
+                       )
+            data_select = data_input[(data_input['source'] == 'soundings') & (data_input['KGCname']  == koeppen.KGCID)]
+            ax.annotate('#:   '+r'$'+str(koeppen.amount)+' '+'$'+'\n'+\
+                        'sm: '+r'$'+str(round(data_select.wg.mean(),2))+'$'+r'$\, \mathrm{m^{3}\, m^{-3}}$'+'\n'+\
+                        'vf: '+str(round(data_select.cveg.mean(),2))+'\n'+\
+                        'cc:  '+str(round(data_select.cc.mean(),2))+'\n'+\
+                        'lat: '+r'$'+str(int(data_select.latitude.mean()))+r'\, ^\circ$'+' \n',\
+                        xy= (0.01,0.015 + (1.-0.12)*(1. - (idx+.5)/len(koeppenlookuptable))),
+                        color='black',
+                        family='monospace',
+                        xycoords='figure fraction',
+                        weight='normal',
+                        fontsize=8.,
+                        horizontalalignment='top',
+                        verticalalignment='left' ,
+                        #bbox={'edgecolor':'black',
+                        #      'boxstyle':'circle',
+                        #      'fc':koeppen.color,
+                        #      'alpha':1.0}
+                       )
+            idx+=1
+
+
+
+
+            # if i ==1:
+            #      plt.legend(loc='upper right',fontsize=7.,frameon=True,framealpha=0.7)
+            # else:
+            #      ax.get_legend().set_visible(False)
+            # #     plt.legend('off')
+            # if i >= 3:
+            #     idx = 0
+            #     for ikoeppen,koeppen in koeppenlookuptable.iterrows():
+
+            #         ax.annotate(koeppen.KGCID,
+            #                     xy=((idx+.5)/len(koeppenlookuptable),-0.00),
+            #                     color=koeppen.textcolor, 
+            #                     xycoords='axes fraction',
+            #                     weight='bold',
+            #                     fontsize=8.,
+            #                     horizontalalignment='center',
+            #                     verticalalignment='center' ,
+            #                     bbox={'edgecolor':'black',
+            #                           'boxstyle':'circle',
+            #                           'fc':koeppen.color,
+            #                           'alpha':1.0}
+            #                    )
+            #         idx+=1
+            #     ax.set_xticklabels([])#labels=ax.get_xticklabels())
+            #     ax.set_xlabel('Köppen climate class')
+            # else:
+            #     ax.set_xticklabels([])
+            #     ax.set_xlabel('')
+
+            # # ax.set_yticklabels([])
+            # # ax.set_ylabel('')
+            # if varkey == 'q':
+            #     ticks = ticker.FuncFormatter(lambda x, pos:
+            #                                  '{0:g}'.format(x*1000.))
+            #     #ax.xaxis.set_major_formatter(ticks)
+            #     ax.yaxis.set_major_formatter(ticks)
+
+            #     ax.set_ylabel(latex['d'+varkey+'dt']+' ['+r'$10^{-3} \times $'+units['d'+varkey+'dt']+']')        
+            # else:
+            #     ax.set_ylabel(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')        
+
+
+
+
+            # for j,artist in enumerate(ax.artists):
+            #     if np.mod(j,len(list(args.experiments.strip().split(' ')))+1) !=0:
+            #         # Set the linecolor on the artist to the facecolor, and set the facecolor to None
+            #         #print(j,artist)
+            #         col = artist.get_facecolor()
+            #         #print(j,artist)
+            #         artist.set_edgecolor(col)
+            #         #print(j,artist)
+            #         artist.set_facecolor('None')
+            # 
+            #         # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)
+            #         # Loop over them here, and use the same colour as above
+            #         
+            #         for k in range(j*5,j*5+5):
+            #             line = ax.lines[k]
+            #             line.set_color(col)
+            #             line.set_mfc(col)
+            #             line.set_mec(col)
+            # 
+            # # Also fix the legend
+            # j = 0
+            # for legpatch in ax.get_legend().get_patches():
+            #     if j > 0:
+
+            #         col = legpatch.get_facecolor()
+            #         legpatch.set_edgecolor(col)
+            #         legpatch.set_facecolor('None')
+            #     j +=1
+
+
+
+
+
+            #ax.grid()
+            #sns.despine(offset=10, trim=True)
+        fig.tight_layout()
+        # fig.subplots_adjust(
+        #     bottom=0.12,left=0.15,top=0.99,right=0.99,wspace=0.10,hspace=0.05,)
+        if args.figure_filename_2 is not None:
+            fig.savefig(args.figure_filename_2,dpi=200); print("Image file written to:", args.figure_filename_2)
+
+            fig.savefig(args.figure_filename_2.replace('png','pdf')); print("Image file written to:", args.figure_filename_2)
+        fig.show()
+
+
+
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index c7ac908..4fe9218 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -1,3 +1,4 @@
+'''
 import numpy as np
 
 import pandas as pd
@@ -125,6 +126,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False
                     )
+'''
 
 if bool(args.make_figures):
     fig = plt.figure(figsize=(10,7))   #width,height
@@ -132,7 +134,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     axes = {}         
     axes_taylor = {}         
     
-    colors = ['r','g','b','m']
+    #colors = ['r','g','b','m']
+    colors = ['k']
     symbols = ['*','x','+']
     dias = {}
     
@@ -144,11 +147,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
         STD_OBS = obs.std()
         dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
-        if i == 2:
-            dias[varkey]._ax.axis["left"].label.set_text(\
-                "Standard deviation (model) / Standard deviation (observations)")
-            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+        dias[varkey]._ax.axis["left"].label.set_text(\
+            "Normalized standard deviation")
+        # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+        # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
         #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
         # Q95 = obs.quantile(0.95)
         # Q95 = obs.quantile(0.90)
@@ -176,13 +178,72 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             STD = mod.std()
             
             fit = np.polyfit(x,y,deg=1)
-            axes[varkey].plot(x, fit[0] * x + fit[1],\
-                              color=colors[ikey],alpha=0.8,lw=2,\
-                              label=key+", "+\
-                                          'R = '+str(round(PR,3))+', '+\
-                                          'RMSE = '+str(round(RMSE,5))+units['d'+varkey+'dt']+', '+\
-                                          'BIAS = '+str(round(BIAS,5))+units['d'+varkey+'dt'] )
-            axes[varkey].legend(fontsize=5)
+
+            if varkey == 'q':
+                axes[varkey].plot(x, fit[0] * x + fit[1],\
+                                  color=colors[ikey],alpha=0.8,lw=2,\
+                                  label=key+", "+\
+                           'RMSE = '+format((RMSE*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+ '\n'+\
+                           'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
+                           r'$R$ = '+format(PR,'0.2f') )
+
+
+            elif varkey == 'h':
+                axes[varkey].plot(x, fit[0] * x + fit[1],\
+                                  color=colors[ikey],alpha=0.8,lw=2,\
+                                  label=key+", "+\
+                            'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                            'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                            r'$R$ = '+format(PR,'0.2f'))
+            else: #theta
+                axes[varkey].plot(x, fit[0] * x + fit[1],\
+                                  color=colors[ikey],alpha=0.8,lw=2,\
+                                  label=key+", "+\
+                            'RMSE = '+format(RMSE,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                            'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                            r'$R$ = '+format(PR,'0.2f'))
+
+            if varkey == 'q':
+                annotate_text = \
+                               'RMSE = '+format((RMSE*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+ '\n'+\
+                               'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
+                               r'$R$ = '+format(PR,'0.2f')
+                ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='right', verticalalignment='bottom' ,
+        bbox={'edgecolor':'black',
+                          'fc':'white',  
+                              'boxstyle':'square',
+                              'alpha':0.8}
+                                       )
+            elif varkey == 'h':
+                annotate_text = \
+                                'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                                'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+                                r'$R$ = '+format(PR,'0.2f')
+                ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='right', verticalalignment='bottom' ,
+        bbox={'edgecolor':'black',
+                          'fc':'white',  
+                              'boxstyle':'square',
+                              'alpha':0.8}
+                                       )
+            else:
+                annotate_text = \
+                                'RMSE = '+format(RMSE,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                                'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+                                r'$R$ = '+format(PR,'0.2f')
+
+                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .98 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='left', verticalalignment='top' ,
+        bbox={'edgecolor':'black',
+                          'fc':'white',  
+                              'boxstyle':'square',
+                              'alpha':0.8}
+                                       )
+
+
+
+
             
             # print(STD)
             # print(PR)
@@ -223,16 +284,22 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
                 
                 dias[varkey].add_sample(station_mod.std()/station_obs.std(),
-                               pearsonr(station_mod,station_obs)[0],annotate=symbols[istation],
+                               pearsonr(station_mod,station_obs)[0],#annotate=symbols[istation],
                                marker=symbols[istation], ms=5, ls='',
-                               #mfc='k', mec='k', # B&W
-                               mfc=colors[ikey], mec=colors[ikey], # Colors
+                               mfc='k', mec='k', # B&W
+                               #mfc=colors[ikey], mec=colors[ikey], # Colors
                                label=key)
                 istation += 1
     
+            if varkey == 'q':
+                units_final = r'[$g\, kg^{-1}\, h^{-1}$]'
+            elif varkey == 'theta':
+                units_final = r'[$K\, h^{-1}$]'
+            elif varkey == 'h':
+                units_final = r'[$m\, h^{-1}$]'
     
             axes[varkey].set_xlabel('observations')     
-            axes[varkey].set_title(latex['d'+varkey+'dt']+' ['+units['d'+varkey+'dt']+']')                                     
+            axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12)                                     
         if i==0:                                    
             axes[varkey].set_ylabel('model')                                            
         abline(1,0,axis=axes[varkey])
@@ -240,19 +307,19 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     
     
-    # legend for different forcing simulations (colors)
-    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
-    leg = []
-    for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-        leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
-        leg.append(leg1)
-    ax.axis('off')
-    #leg1 =
-    ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
+    # # legend for different forcing simulations (colors)
+    # ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    # leg = []
+    # for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+    #     leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
+    #     leg.append(leg1)
+    # ax.axis('off')
+    # #leg1 =
+    # ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
     
     
     # legend for different stations (symbols)
-    ax = fig.add_axes([0.25,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
     leg = []
     isymbol = 0
     for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
diff --git a/class4gl/interface/test.test b/class4gl/interface/test.test
new file mode 100644
index 0000000..969d1d3
--- /dev/null
+++ b/class4gl/interface/test.test
@@ -0,0 +1,276 @@
+---
+# CLASS4GL input; format version: 0.1
+index: 4
+pars:
+  Ammax298: [2.2, 1.7]
+  CO2: 400.8322760704748
+  CO22_h: null
+  CO2comp298: [68.5, 4.3]
+  CO2tend: 0.0
+  Cm: 0.014889170302298154
+  Cs: 0.012038840098238213
+  Cw: 0.0016
+  E0: 53300.0
+  G: 47.29774110009936
+  H: 190.5623007925413
+  Kx: [0.7, 0.7]
+  L: -6.853794630136477
+  LE: 214.88314861335857
+  LEliq: 0.0
+  LEpot: 319.34762471715567
+  LEref: 243.84639777941322
+  LEsoil: 43.588158263249724
+  LEveg: 171.29499035010883
+  Lwin: 307.8635861165938
+  Lwout: 426.33896646668103
+  M: 0.0
+  P_h: 82175.50598724303
+  Q: 452.7431905059985
+  Q10Am: [2.0, 2.0]
+  Q10CO2: [1.5, 1.5]
+  Q10gm: [2.0, 2.0]
+  R10: 0.23
+  RH_h: 1.674122332852863
+  Rib: -6.70652127177839
+  Swin: 739.103621663443
+  Swout: 167.8850508073574
+  T1Am: [281.0, 286.0]
+  T1gm: [278.0, 286.0]
+  T2: 284.68364969889325
+  T2Am: [311.0, 311.0]
+  T2gm: [301.0, 309.0]
+  T2m: 282.3123067089991
+  T_h: 274.8936229768578
+  Ts: 294.47523957923886
+  Tsoil: 286.4586732910864
+  ac: 0.0
+  ad: [0.07, 0.15]
+  advCO2: 0.0
+  advt: 1.0355662748843815e-05
+  alpha0: [0.017, 0.014]
+  c3c4: c3
+  c_beta: 0
+  dCO2: -22.832276070474784
+  dCO2tend: 0.0
+  dFz: 0.0
+  divU: 0.0
+  dq: -0.003052156485017018
+  dtcur: 60.0
+  dtheta: 0.1
+  dthetav: -0.4365906447159773
+  dtmax: 1598.8514968081563
+  du: -0.4229086217862608
+  dv: 0.28673977724043115
+  dz: 50.0
+  dz_h: 50
+  dztend: -0.1395839151513645
+  e: 1388.9934836777713
+  e2m: 952.6963178543036
+  esat: 1731.8968600367527
+  esat2m: 1160.2806224411495
+  f0: [0.89, 0.85]
+  firsttime: true
+  gammaCO2: 0.0
+  gammaq: -2.2753052840179227e-05
+  gammatheta: 0.002144498648676304
+  gammau: 0.028469046551934885
+  gammav: 0.017160662703889926
+  gm298: [7.0, 17.5]
+  gmin: [0.00025, 0.00025]
+  h: 1383.2363985770135
+  lcl: 428.232209487189
+  ls_type: js
+  mair: 28.9
+  mco2: 44.0
+  nuco2q: 1.6
+  q: 0.008771106059366231
+  q2_h: null
+  q2m: 0.006016011266044435
+  qsat: 0.010936445146628022
+  qsurf: 0.011939539970569558
+  ra: 38.47545242647453
+  rs: 49.409679791439665
+  sp: 99023.24348958333
+  substep: false
+  substeps: 0
+  sw_ac: [adv]
+  sw_ap: true
+  sw_cu: false
+  sw_fixft: false
+  sw_lit: false
+  sw_ml: true
+  test: 0.0
+  testing: 0.0
+  theta: 288.39566185252
+  thetasurf: 294.472849715022
+  thetav: 289.93868670419346
+  thetavsurf: 296.61753063426823
+  time: 12.116666666666667
+  tsteps: 360
+  u: 0.817076520336863
+  u2m: 0.8170765203368628
+  ustar: 0.26343099197090714
+  uw: -0.026264240506899815
+  v: 0.04516931051262895
+  v2m: 0.045169310512628943
+  vw: -0.0014519296605725784
+  wCO2: 0.0
+  wCO2A: 0
+  wCO2M: 0
+  wCO2R: 0
+  wCO2e: 0.0
+  wf: 0.0
+  wg: 0.35105639595370375
+  wmax: 0.55
+  wmin: 0.005
+  wq: 7.162771620445285e-05
+  wqM: 0.0
+  wqe: 0.0
+  wstar: 1.9991113910017524
+  wtheta: 0.15801185803693307
+  wthetae: -0.0
+  wthetav: 0.17070715833083436
+  wthetave: -0.03414143166616687
+  z0h: 0.0
+  z0m: 0.0
+  zeta: -20.182052034282645
+  zlcl: 428.232209487189
+  zslz0m: 480.2904085186834
+---
+# CLASS4GL input; format version: 0.1
+index: 2944
+pars:
+  Ammax298: [2.2, 1.7]
+  CO2: 400.8322760704748
+  CO22_h: null
+  CO2comp298: [68.5, 4.3]
+  CO2tend: 0.0
+  Cm: 0.014889170302298154
+  Cs: 0.012038840098238213
+  Cw: 0.0016
+  E0: 53300.0
+  G: 47.29774110009936
+  H: 190.5623007925413
+  Kx: [0.7, 0.7]
+  L: -6.853794630136477
+  LE: 214.88314861335857
+  LEliq: 0.0
+  LEpot: 319.34762471715567
+  LEref: 243.84639777941322
+  LEsoil: 43.588158263249724
+  LEveg: 171.29499035010883
+  Lwin: 307.8635861165938
+  Lwout: 426.33896646668103
+  M: 0.0
+  P_h: 82175.50598724303
+  Q: 452.7431905059985
+  Q10Am: [2.0, 2.0]
+  Q10CO2: [1.5, 1.5]
+  Q10gm: [2.0, 2.0]
+  R10: 0.23
+  RH_h: 1.674122332852863
+  Rib: -6.70652127177839
+  Swin: 739.103621663443
+  Swout: 167.8850508073574
+  T1Am: [281.0, 286.0]
+  T1gm: [278.0, 286.0]
+  T2: 284.68364969889325
+  T2Am: [311.0, 311.0]
+  T2gm: [301.0, 309.0]
+  T2m: 282.3123067089991
+  T_h: 274.8936229768578
+  Ts: 294.47523957923886
+  Tsoil: 286.4586732910864
+  ac: 0.0
+  ad: [0.07, 0.15]
+  advCO2: 0.0
+  advt: 1.0355662748843815e-05
+  alpha0: [0.017, 0.014]
+  c3c4: c3
+  c_beta: 0
+  dCO2: -22.832276070474784
+  dCO2tend: 0.0
+  dFz: 0.0
+  divU: 0.0
+  dq: -0.003052156485017018
+  dtcur: 60.0
+  dtheta: 0.1
+  dthetav: -0.4365906447159773
+  dtmax: 1598.8514968081563
+  du: -0.4229086217862608
+  dv: 0.28673977724043115
+  dz: 50.0
+  dz_h: 50
+  dztend: -0.1395839151513645
+  e: 1388.9934836777713
+  e2m: 952.6963178543036
+  esat: 1731.8968600367527
+  esat2m: 1160.2806224411495
+  f0: [0.89, 0.85]
+  firsttime: true
+  gammaCO2: 0.0
+  gammaq: -2.2753052840179227e-05
+  gammatheta: 0.002144498648676304
+  gammau: 0.028469046551934885
+  gammav: 0.017160662703889926
+  gm298: [7.0, 17.5]
+  gmin: [0.00025, 0.00025]
+  h: 1383.2363985770135
+  lcl: 428.232209487189
+  ls_type: js
+  mair: 28.9
+  mco2: 44.0
+  nuco2q: 1.6
+  q: 0.008771106059366231
+  q2_h: null
+  q2m: 0.006016011266044435
+  qsat: 0.010936445146628022
+  qsurf: 0.011939539970569558
+  ra: 38.47545242647453
+  rs: 49.409679791439665
+  sp: 99023.24348958333
+  substep: false
+  substeps: 0
+  sw_ac: [adv]
+  sw_ap: true
+  sw_cu: false
+  sw_fixft: false
+  sw_lit: false
+  sw_ml: true
+  test: 0.0
+  testing: 0.0
+  theta: 288.39566185252
+  thetasurf: 294.472849715022
+  thetav: 289.93868670419346
+  thetavsurf: 296.61753063426823
+  time: 12.116666666666667
+  tsteps: 360
+  u: 0.817076520336863
+  u2m: 0.8170765203368628
+  ustar: 0.26343099197090714
+  uw: -0.026264240506899815
+  v: 0.04516931051262895
+  v2m: 0.045169310512628943
+  vw: -0.0014519296605725784
+  wCO2: 0.0
+  wCO2A: 0
+  wCO2M: 0
+  wCO2R: 0
+  wCO2e: 0.0
+  wf: 0.0
+  wg: 0.35105639595370375
+  wmax: 0.55
+  wmin: 0.005
+  wq: 7.162771620445285e-05
+  wqM: 0.0
+  wqe: 0.0
+  wstar: 1.9991113910017524
+  wtheta: 0.15801185803693307
+  wthetae: -0.0
+  wthetav: 0.17070715833083436
+  wthetave: -0.03414143166616687
+  z0h: 0.0
+  z0m: 0.0
+  zeta: -20.182052034282645
+  zlcl: 428.232209487189
+  zslz0m: 480.2904085186834
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index a1a2ad8..fbc4db0 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -189,6 +189,7 @@ def __init__(self,path,suffix='ini',refetch_stations=True):
             self.table = self.get_stations(suffix=suffix)
             self.table.to_csv(self.file)
         
+        print(self.table.columns)
         self.table = self.table.set_index('STNID')
 
     def get_stations(self,suffix):
@@ -378,10 +379,11 @@ def __prev__(self):
 
 
 def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_records=False):
-
+    print(stations)
     records = pd.DataFrame()
     for STNID,station in stations.iterrows():
         dictfnchunks = []
+        pklchunks = []
         if getchunk is 'all':
 
             # we try the old single-chunk filename format first (usually for
@@ -390,6 +392,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
             if os.path.isfile(path_yaml+'/'+fn):
                 chunk = 0
                 dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                pklchunks.append(fn.replace('.yaml','.pkl'))
 
             # otherwise, we use the new multi-chunk filename format
             else:
@@ -407,6 +410,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                 for chunk in chunks:
                     fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
                     dictfnchunks.append(dict(fn=fn,chunk=chunk))
+                    pklchunks.append(fn.replace('.yaml','.pkl'))
 
                 # while not end_of_chunks:
                 #     fn = format(STNID,'05d')+'_'+str(chunk)+'_'+subset+'.yaml'
@@ -422,113 +426,146 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
         else:
             fn = format(STNID,'05d')+'_'+str(getchunk)+'_'+subset+'.yaml'
             dictfnchunks.append(dict(fn=fn,chunk=getchunk))
-            
-        if len(dictfnchunks) > 0:
-            for dictfnchunk in dictfnchunks:
-                yamlfilename = dictfnchunk['fn']
-                chunk = dictfnchunk['chunk']
-
-                #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
-                pklfilename = yamlfilename.replace('.yaml','.pkl')
-
-                #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
-                #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
-                generate_pkl = False
-                if not os.path.isfile(path_yaml+'/'+pklfilename): 
-                    print('pkl file does not exist. I generate "'+\
-                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
-                    generate_pkl = True
-                elif not (os.path.getmtime(path_yaml+'/'+yamlfilename) <  \
-                    os.path.getmtime(path_yaml+'/'+pklfilename)):
-                    print('pkl file older than yaml file, so I regenerate "'+\
-                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
-                    generate_pkl = True
-
-                if refetch_records:
-                    print('refetch_records flag is True. I regenerate "'+\
-                          path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
-                    generate_pkl = True
-                if not generate_pkl:
-                    records = pd.concat([records,pd.read_pickle(path_yaml+'/'+pklfilename)])
-                   # irecord = 0
-                else:
-                    with open(path_yaml+'/'+yamlfilename) as yaml_file:
-
-                        dictout = {}
-
-                        next_record_found = False
-                        end_of_file = False
-                        while (not next_record_found) and (not end_of_file):
-                            linebuffer = yaml_file.readline()
-                            next_record_found = (linebuffer == '---\n')
-                            end_of_file = (linebuffer == '')
-                        next_tell = yaml_file.tell()
-                        
-                        while not end_of_file:
-
-                            print(' next record:',next_tell)
-                            current_tell = next_tell
+
+        if (len(dictfnchunks) > 0):
+            load_from_unified_pkl = False    
+            pklfilename_unified = format(STNID,'05d')+'_'+subset+'.pkl'
+            if (getchunk is 'all') and (os.path.isfile(path_yaml+'/'+pklfilename_unified)):
+                load_from_unified_pkl = True
+                for dictfnchunk in dictfnchunks:
+                    yamlfilename = dictfnchunk['fn']
+                    chunk = dictfnchunk['chunk']
+                    pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+
+                    if \
+                       (pklfilename_unified in pklchunks) or \
+                       (not os.path.isfile(path_yaml+'/'+pklfilename)) or \
+                       (os.path.getmtime(path_yaml+'/'+yamlfilename) > os.path.getmtime(path_yaml+'/'+pklfilename_unified)) or\
+                       (os.path.getmtime(path_yaml+'/'+pklfilename) > os.path.getmtime(path_yaml+'/'+pklfilename_unified)):
+                        load_from_unified_pkl = False
+
+            if load_from_unified_pkl:
+                pklfilename_unified = format(STNID,'05d')+'_'+subset+'.pkl'
+                print('reading unified table file ('+path_yaml+'/'+pklfilename_unified+') for station '\
+                              +str(STNID))
+
+                records_station = pd.read_pickle(path_yaml+'/'+pklfilename_unified)
+            else:
+                records_station = pd.DataFrame()
+                for dictfnchunk in dictfnchunks:
+                    yamlfilename = dictfnchunk['fn']
+                    chunk = dictfnchunk['chunk']
+
+                    #pklfilename = path_yaml+'/'+format(STNID,'05d')+'_'+subset+'.pkl'
+                    pklfilename = yamlfilename.replace('.yaml','.pkl')
+
+                    #print(yamlfilename+": "+str(os.path.getmtime(yamlfilename)))
+                    #print(pklfilename+": "+str(os.path.getmtime(pklfilename)))
+                    generate_pkl = False
+                    if not os.path.isfile(path_yaml+'/'+pklfilename): 
+                        print('pkl file does not exist. I generate "'+\
+                              path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
+                        generate_pkl = True
+                    elif not (os.path.getmtime(path_yaml+'/'+yamlfilename) <  \
+                        os.path.getmtime(path_yaml+'/'+pklfilename)):
+                        print('pkl file older than yaml file, so I regenerate "'+\
+                              path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
+                        generate_pkl = True
+
+                    if refetch_records:
+                        print('refetch_records flag is True. I regenerate "'+\
+                              path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
+                        generate_pkl = True
+                    if not generate_pkl:
+                        records_station_chunk = pd.read_pickle(path_yaml+'/'+pklfilename)
+                        records_station = pd.concat([records_station,records_station_chunk])
+                       # irecord = 0
+                    else:
+                        with open(path_yaml+'/'+yamlfilename) as yaml_file:
+
+                            dictout = {}
+
                             next_record_found = False
-                            yaml_file.seek(current_tell)
-                            os.system('mkdir -p '+TEMPDIR)
-                            filebuffer = open(TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
-                            linebuffer = ''
-                            while ( (not next_record_found) and (not end_of_file)):
-                                filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                            end_of_file = False
+                            while (not next_record_found) and (not end_of_file):
                                 linebuffer = yaml_file.readline()
                                 next_record_found = (linebuffer == '---\n')
                                 end_of_file = (linebuffer == '')
-                            filebuffer.close()
-                            
                             next_tell = yaml_file.tell()
-                            index_start = current_tell
-                            index_end = next_tell
-
-                            
-                            if which('ruby') is None:
-                                raise RuntimeError ('ruby is not found. Aborting...')
-                            #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                            command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                            print(command)
                             
-                            os.system(command)
-                            #jsonoutput = subprocess.check_output(command,shell=True) 
-                            #print(jsonoutput)
-                            #jsonstream = io.StringIO(jsonoutput)
-                            jsonstream = open(TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
-                            record = json.load(jsonstream)
-                            dictouttemp = {}
-                            for key,value in record['pars'].items():
-                                # we don't want the key with columns that have none values
-                                if value is not None: 
-                                   regular_numeric_types =[ type(x) for x in[0,False,0.0]]
-                                   if (type(value) in regular_numeric_types):
-                                        dictouttemp[key] = value
-                                   elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                       #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                       dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
-                                       # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                       dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
-                            recordindex = record['index']
-                            dictouttemp['chunk'] = chunk
-                            dictouttemp['index_start'] = index_start
-                            dictouttemp['index_end'] = index_end
-                            os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
-                            for key,value in dictouttemp.items():
-                                if key not in dictout.keys():
-                                    dictout[key] = {}
-                                dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
-                            print(' obs record registered')
-                            jsonstream.close()
-                            os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
-                    records_station = pd.DataFrame.from_dict(dictout)
-                    records_station.index.set_names(('STNID','chunk','index'),inplace=True)
-                    print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
-                          +str(STNID))
-                    records_station.to_pickle(path_yaml+'/'+pklfilename)
-                    # else:
-                    #     os.system('rm '+pklfilename)
-                    records = pd.concat([records,records_station])
+                            while not end_of_file:
+
+                                print(' next record:',next_tell)
+                                current_tell = next_tell
+                                next_record_found = False
+                                yaml_file.seek(current_tell)
+                                os.system('mkdir -p '+TEMPDIR)
+                                filebuffer = open(TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+                                linebuffer = ''
+                                while ( (not next_record_found) and (not end_of_file)):
+                                    filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                    linebuffer = yaml_file.readline()
+                                    next_record_found = (linebuffer == '---\n')
+                                    end_of_file = (linebuffer == '')
+                                filebuffer.close()
+                                
+                                next_tell = yaml_file.tell()
+                                index_start = current_tell
+                                index_end = next_tell
+
+                                
+                                if which('ruby') is None:
+                                    raise RuntimeError ('ruby is not found. Aborting...')
+                                #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                                command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                                print(command)
+                                
+                                os.system(command)
+                                #jsonoutput = subprocess.check_output(command,shell=True) 
+                                #print(jsonoutput)
+                                #jsonstream = io.StringIO(jsonoutput)
+                                jsonstream = open(TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                                record = json.load(jsonstream)
+                                dictouttemp = {}
+                                for key,value in record['pars'].items():
+                                    # we don't want the key with columns that have none values
+                                    if value is not None: 
+                                       regular_numeric_types =[ type(x) for x in[0,False,0.0]]
+                                       if (type(value) in regular_numeric_types):
+                                            dictouttemp[key] = value
+                                       elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
+                                           #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
+                                           dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                           # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                           dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                                recordindex = record['index']
+                                dictouttemp['chunk'] = chunk
+                                dictouttemp['index_start'] = index_start
+                                dictouttemp['index_end'] = index_end
+                                os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                                for key,value in dictouttemp.items():
+                                    if key not in dictout.keys():
+                                        dictout[key] = {}
+                                    dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
+                                print(' obs record registered')
+                                jsonstream.close()
+                                os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                            records_station_chunk = pd.DataFrame.from_dict(dictout)
+                            records_station_chunk.index.set_names(('STNID','chunk','index'),inplace=True)
+                            print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
+                                  +str(STNID)+', chunk number '+str(chunk))
+                            records_station_chunk.to_pickle(path_yaml+'/'+pklfilename)
+                            records_station = pd.concat([records_station,records_station_chunk])
+                        # else:
+                        #     os.system('rm '+pklfilename)
+                if (getchunk == 'all') and (pklfilename_unified not in pklchunks):
+                    pklfilename_unified = format(STNID,'05d')+'_'+subset+'.pkl'
+                    print('writing unified table file ('+path_yaml+'/'+pklfilename_unified+') for station '\
+                                  +str(STNID))
+                    records_station.to_pickle(path_yaml+'/'+pklfilename_unified)
+
+            records = pd.concat([records,records_station])
     return records
 
 def stdrel(mod,obs,columns):
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index b9da41c..0409509 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -220,15 +220,37 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
             # investigated. In the meantime, we filter them
 
             if self.path_obs is not None:
-                valid = ((self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt > - 0.0020) & 
-                        ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                        ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+                print('exclude exceptional observations')
+                print('exclude unrealistic model output -> should be investigated!')
+                valid = (\
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt >  0.250) & 
+                         #(self.frames['stats']['records_all_stations_mod_stats'].dthetadt >  0.25000) & 
+                         #(self.frames['stats']['records_all_stations_mod_stats'].dthetadt <  1.8000) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt <  1.8000) & 
+                         #(self.frames['stats']['records_all_stations_mod_stats'].dhdt >  50.0000) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt >  40.0000) & 
+                         #(self.frames['stats']['records_all_stations_mod_stats'].dhdt <  350.) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt <  350.) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt >  -.00055) & 
+                         #(self.frames['stats']['records_all_stations_mod_stats'].dqdt >  -.00055) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt <  .0003) & 
+
+                         # filter 'extreme' model output -> should be investigated!
+                         (self.frames['stats']['records_all_stations_mod_stats'].dqdt <  .0006) & 
+                         (self.frames['stats']['records_all_stations_mod_stats'].dqdt >  -.0006) & 
+                         (self.frames['stats']['records_all_stations_mod_stats'].dthetadt >  .2) & 
+                         (self.frames['stats']['records_all_stations_mod_stats'].dthetadt <  2.) & 
+                         # (self.frames['stats']['records_all_stations_mod_stats'].dqdt <  .0003) & 
+                         # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
+                         # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
+                         ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
+                         ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
 
                 for key in self.frames['stats'].keys():
                     if (type(self.frames['stats'][key]) == pd.DataFrame) and \
                        (self.frames['stats'][key].index.names == indextype):
                         self.frames['stats'][key] = self.frames['stats'][key][valid]
-                print(str(len(valid) - np.sum(valid))+' soundings are filtered')
+                print("WARNING WARNING!: "+ str(len(valid) - np.sum(valid))+' soundings are filtered')
 
         self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
 
@@ -404,6 +426,12 @@ def next_record(self,event=None,jump=1):
             self.frames['profiles']['current_station_file_mod'] = \
                 open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
 
+            if self.path_obs is not None:
+                if 'current_station_file_afternoon' in self.frames['profiles'].keys():
+                    self.frames['profiles']['current_station_file_afternoon'].close()
+                self.frames['profiles']['current_station_file_afternoon'] = \
+                    open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_afternoon.yaml','r')
+
         self.update_record()
 
     def prev_record(self,event=None):
@@ -572,7 +600,7 @@ def plot(self):
 
         label = 'times'
                
-        axes[label] = fig.add_axes([0.30,0.90,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
+        axes[label] = fig.add_axes([0.30,0.87,0.30,0.10]) #[*left*, *bottom*, *width*,    *height*]
         # add pointers to the data of the axes
         axes[label].data = {}
         # add pointers to color fields (for maps and colorbars) in the axes
@@ -697,13 +725,13 @@ def plot(self):
         self.axes[label] = fig.add_axes([0.86,0.44,0.12,0.50], label=label)
 
         label = 'out:h'
-        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.10], label=label)
+        self.axes[label] = fig.add_axes([0.50,0.27,0.22,0.09], label=label)
 
         label = 'out:theta'
-        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.10], label=label)
+        self.axes[label] = fig.add_axes([0.50,0.17,0.22,0.09], label=label)
 
         label = 'out:q'
-        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.10], label=label)
+        self.axes[label] = fig.add_axes([0.50,0.07,0.22,0.09], label=label)
 
         label = 'SEB'
         self.axes[label] = fig.add_axes([0.77,0.07,0.22,0.30], label=label)
@@ -1142,9 +1170,9 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                                                xy=(x,y),\
                                                xytext=(0.05,0.05),\
                                                textcoords='axes fraction',\
-                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z)),\
+                                               bbox=dict(boxstyle="round",fc=self.statsviewcmap(z),edgecolor='black'),\
                                                color='white',\
-                                               arrowprops=dict(arrowstyle="->",linewidth=1.1))
+                                               arrowprops=dict(arrowstyle="->",linewidth=1.1,color='black'))
                 # self.axes['stats_'+key].data[key+'_current_record'] = \
                 #        self.axes['stats_'+key].scatter(x,y, c=z,\
                 #                 cmap=self.statsviewcmap,\
@@ -1273,9 +1301,9 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                                          xytext=(0.05,0.05),
                                          textcoords='axes fraction', 
                                          bbox=dict(boxstyle="round",
-                                         fc = cm.viridis(colorstation)),
+                                         fc = cm.viridis(colorstation),edgecolor='black'),
                                          arrowprops=dict(arrowstyle="->",
-                                                         linewidth=1.1),
+                                                         linewidth=1.1,color='black'),
                                          color='white' if colorstation < 0.5 else 'black')
                     #print('r9')
 
diff --git a/class4gl/model.py b/class4gl/model.py
index 3993200..2ae873f 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -933,7 +933,7 @@ def run_mixed_layer(self):
         htend_pre       = self.we + self.ws + self.wf - self.M
         
         #self.thetatend   = (self.wtheta - self.wthetae             ) / self.h + self.advtheta 
-        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h - self.advtheta
+        thetatend_pre = (self.wtheta - self.wthetae             ) / self.h + self.advtheta
         
  
         #print('thetatend_pre',thetatend_pre)
@@ -972,15 +972,15 @@ def run_mixed_layer(self):
         self.dthetatend = l_entrainment*dthetatend_pre + \
                         (1.-l_entrainment)*0.
         self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h - self.advtheta)
+                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
         self.htend = l_entrainment*htend_pre + \
                      (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
         #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
         #stop
 
 
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h - self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h - self.advCO2
+        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
 
 
         # self.qtend = l_entrainment*qtend_pre + \
@@ -1017,8 +1017,8 @@ def run_mixed_layer(self):
      
         # assume u + du = ug, so ug - u = du
         if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h - self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h - self.advv
+            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
   
             self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
             self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
@@ -1083,7 +1083,7 @@ def integrate_mixed_layer(self):
 
             # take into account advection for the whole profile
                 
-                self.air_ap[var] = self.air_ap[var] - self.dtcur * self.air_ap['adv'+var]
+                self.air_ap[var] = self.air_ap[var] + self.dtcur * self.air_ap['adv'+var]
 
             var = 'z'
             #print(self.air_ap[var])
diff --git a/class4gl/setup/batch_setup_global.py b/class4gl/setup/batch_setup_global_old.py
similarity index 100%
rename from class4gl/setup/batch_setup_global.py
rename to class4gl/setup/batch_setup_global_old.py
diff --git a/class4gl/setup/batch_setup_igra.pbs b/class4gl/setup/batch_setup_igra.pbs
new file mode 100644
index 0000000..ecf97f8
--- /dev/null
+++ b/class4gl/setup/batch_setup_igra.pbs
@@ -0,0 +1,30 @@
+#!/bin/bash 
+#
+#PBS -j oe
+#PBS -M hendrik.wouters@ugent.be
+#PBS -m b
+#PBS -m e
+#PBS -m a
+#PBS -N c4gl_setup
+
+module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+
+EXEC_ALL="python $C4GLJOB_exec --first_station_row $PBS_ARRAYID --last_station_row $PBS_ARRAYID"
+
+for var in $(compgen -v | grep C4GLJOB_ ); do
+    echo $var
+    if [ "$var" != "C4GLJOB_exec" ]
+    then
+    EXEC_ALL=$EXEC_ALL" --"`echo $var | cut -c9-`"="${!var}
+    fi
+done
+
+
+# EXEC_ALL="python $exec --global-chunk-number $PBS_ARRAYID \
+#                        --split-by $split_by \
+#                        --dataset $dataset \
+#                        --experiments $experiments"
+#                  #      --path-soundings $path_soundings \
+echo Executing: $EXEC_ALL
+$EXEC_ALL
+
diff --git a/class4gl/setup/batch_setup_igra.py b/class4gl/setup/batch_setup_igra.py
new file mode 100644
index 0000000..5a33bb9
--- /dev/null
+++ b/class4gl/setup/batch_setup_igra.py
@@ -0,0 +1,187 @@
+
+# -*- coding: utf-8 -*-
+
+import logging
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import importlib
+spam_loader = importlib.find_loader('Pysolar')
+found = spam_loader is not None
+if found:
+    import Pysolar
+    import Pysolar.util.GetSunriseSunset
+else:
+    import pysolar as Pysolar
+    GetSunriseSunset =  Pysolar.util.get_sunrise_sunset
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--pbs_string',default=' -l walltime=30:0:0')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+# parser.add_argument('--first_YYYYMMDD',default="19810101")
+# parser.add_argument('--last_YYYYMMDD',default="20180101")
+# parser.add_argument('--first_station_row')
+# parser.add_argument('--last_station_row')
+# parser.add_argument('--station_id') # run a specific station id
+# parser.add_argument('--latitude') # run a specific station id
+# parser.add_argument('--longitude') # run a specific station id
+# parser.add_argument('--error_handling',default='dump_on_success')
+# parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+# arser.add_argument('--runtime',default='from_afternoon_profile')
+
+# parser.add_argument('--split_by',default="-1")# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+EXP_DEFS  =\
+{
+  'ERA-INTERIM_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'ERA-INTERIM_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+# iniitialize global data
+# ===============================
+print("Initializing global data")
+# ===============================
+globaldata = data_global()
+globaldata.sources = {**globaldata.sources,**{
+    
+        "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_19830609-19830808_6hourly.nc",
+        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830609-19830808_6hourly.nc",
+        "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_19830609-19830808_6hourly.nc",
+        "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_19830609-19830808_6hourly.nc",
+    
+#        "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_19830209-19830410_6hourly.nc",
+ #       "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q*_6hourly.nc",
+ #       "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u*_6hourly.nc",
+ #       "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v*_6hourly.nc",
+        }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_input,refetch_stations=False)
+
+
+# # ===============================
+# print("Selecting station by ID")
+# # ===============================
+# stations_iter = stations_iterator(all_stations)
+# STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+# all_stations_select = pd.DataFrame([run_station])
+# print(run_station)
+
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+#  # these are all the stations that are supposed to run by the whole batch (all
+#  # chunks). We narrow it down according to the station(s) specified.
+#  if (args.latitude is not None) or (args.longitude is not None):
+#      print('custom coordinates not implemented yet, please ask developer.')
+#  elif args.station_id is not None:
+#      print("Selecting station by ID")
+#      stations_iter = stations_iterator(all_stations)
+#      STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+#      all_stations_select = pd.DataFrame([run_station])
+#  #     print("making a custom station according to the coordinates")
+#  # 
+#  #     STNID = 43.23
+#  else:
+all_stations_select = pd.DataFrame(all_stations.table)
+#      if args.last_station_row is not None:
+#          all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+#      if args.first_station_row is not None:
+#          all_stations_select = all_station_select.iloc[int(args.first_station):]
+
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+# dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d",)
+# dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d",)
+# # ===============================
+# print("Creating daily timeseries from", dtfirst," to ", dtlast)
+# # ===============================
+# DTS = [dtfirst + dt.timedelta(days=iday) for iday in \
+#        range(int((dtlast + dt.timedelta(days=1) -
+#                   dtfirst).total_seconds()/3600./24.))]
+# 
+# if int(args.split_by) != -1:
+#     totalchunks = len(all_stations_select)*math.ceil(len(DTS)/int(args.split_by))
+# else:
+totalchunks = len(all_stations_select)
+# 
+# print(totalchunks)
+
+#if args.cleanup_experiments:
+#    os.system("rm -R "+args.path_experiments+'/')
+
+# C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/setup/batch_setup_igra.pbs -t 0-'+\
+            str(totalchunks-1)+" -v "
+# propagate arguments towards the job script
+lfirst = True
+for argkey in args.__dict__.keys():
+    if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
+        # default values are specified in the simulation script, so
+        # excluded here
+        (args.__dict__[argkey] is not None)
+       ):
+        print(argkey)
+        print(args.__dict__[argkey])
+        if lfirst:
+            command +=' C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        else:
+            command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        lfirst=False
+
+print('Submitting array job: '+command)
+os.system(command)
diff --git a/class4gl/simulations/batch_update.py b/class4gl/setup/batch_update.py
similarity index 71%
rename from class4gl/simulations/batch_update.py
rename to class4gl/setup/batch_update.py
index d9ff78a..ab51951 100644
--- a/class4gl/simulations/batch_update.py
+++ b/class4gl/setup/batch_update.py
@@ -3,9 +3,9 @@
 """
 Usage:
 python batch_update.py --exec $CLASS4GL/simulations/update_yaml_old.py
---path_experiments $VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC/ --path_forcing
+--path_experiments $VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC/ --path_input
 $VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC_BACKUP_20180904/ --c4gl_path_lib
-$CLASS4GL --split_by 50 --global_keys "KGC" --subset_forcing ini --experiments
+$CLASS4GL --split_by 50 --global_keys "KGC" --subset_input morning --experiments
 "GLOBAL_NOAC"
 """
 
@@ -26,32 +26,32 @@
 parser.add_argument('--exec') # chunk simulation script
 parser.add_argument('--first_station_row')
 parser.add_argument('--last_station_row')
-parser.add_argument('--pbs_string',default=' -l walltime=:2:0:0')
+parser.add_argument('--pbs_string',default=' -l walltime=2:0:0')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling')
-parser.add_argument('--subset_forcing',default='morning') 
+parser.add_argument('--subset_input',default='morning') 
                                         # this tells which yaml subset
                                         # to initialize with.
                                         # Most common options are
                                         # 'morning' and 'ini'.
+parser.add_argument('--subset_output',default='morning') 
 
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
 parser.add_argument('--runtime')
 # delete folders of experiments before running them
-parser.add_argument('--cleanup_experiments',default=False)
-parser.add_argument('--experiments')
 parser.add_argument('--split_by',default=50)# station soundings are split
                                             # up in chunks
 
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--path_forcing') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--path_experiments') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_input') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 
 
 #arguments only used for update_yaml.py
 parser.add_argument('--path_dataset') 
 parser.add_argument('--global_keys') 
+parser.add_argument('--updates') 
 args = parser.parse_args()
 
 sys.path.insert(0, args.c4gl_path_lib)
@@ -71,16 +71,9 @@
 #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
 #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
 
-
-
-# #SET = 'GLOBAL'
-# SET = args.dataset
-
-# path_forcingSET = args.path_forcing+'/'+SET+'/'
-
-print("getting all stations from --path_forcing")
+print("getting all stations from --path_input")
 # these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
 
 print('defining all_stations_select')
 # these are all the stations that are supposed to run by the whole batch (all
@@ -102,8 +95,8 @@
 
 print("getting all records of the whole batch")
 all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,\
+                                         args.path_input,\
+                                         subset=args.subset_input,\
                                          refetch_records=False,\
                                         )
 
@@ -119,26 +112,28 @@
 #if sys.argv[1] == 'qsub':
 # with qsub
 
-print(args.experiments.strip().split(" "))
-
-for EXP in args.experiments.strip().split(" "):
-    if args.cleanup_experiments:
-        os.system("rm -R "+args.path_experiments+'/'+EXP)
-
-    #C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
-    command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
-                str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
-    # propagate arguments towards the job script
-    for argkey in args.__dict__.keys():
-        if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
-            # default values are specified in the simulation script, so
-            # excluded here
-            (args.__dict__[argkey] is not None)
-           ):
-                command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
-
-    print('Submitting array job for experiment '+EXP+': '+command)
-    os.system(command)
+
+
+#C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+            str(totalchunks-1)+" -v '"
+# propagate arguments towards the job script
+first = True
+for argkey in args.__dict__.keys():
+    if ((argkey not in ['pbs_string']) and \
+        # default values are specified in the simulation script, so
+        # excluded here
+        (args.__dict__[argkey] is not None)
+       ):
+        if first:
+            command +='C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        else:
+            command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+    first = False
+
+command = command+"'"
+print('Submitting array job: '+command)
+os.system(command)
 
 
     #os.system(command)
diff --git a/class4gl/setup/setup_bllast.py b/class4gl/setup/setup_bllast.py
index af8c8bb..0de6fc4 100644
--- a/class4gl/setup/setup_bllast.py
+++ b/class4gl/setup/setup_bllast.py
@@ -12,9 +12,9 @@
 import Pysolar
 import sys
 import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 
 
 globaldata = data_global()
diff --git a/class4gl/setup/setup_global_afternoon.py b/class4gl/setup/setup_global_afternoon.py
index 4a49a97..e8745dd 100644
--- a/class4gl/setup/setup_global_afternoon.py
+++ b/class4gl/setup/setup_global_afternoon.py
@@ -131,10 +131,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 if args.station_id is not None:
     df_stations = df_stations[df_stations.ID == int(args.station_id)]
 else:
-    if args.first_station_row is not None:
-        df_stations = df_stations[int(args.first_station_row):]
     if args.last_station_row is not None:
         df_stations = df_stations[:(int(args.last_station_row)+1)]
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
 
 STNlist = list(df_stations.iterrows())
 
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
index f9efe2c..3cc3c74 100644
--- a/class4gl/setup/setup_goamazon.py
+++ b/class4gl/setup/setup_goamazon.py
@@ -10,9 +10,9 @@
 import sys
 import pytz
 import glob
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 
 
 globaldata = data_global()
diff --git a/class4gl/setup/setup_humppa.py b/class4gl/setup/setup_humppa.py
index ff37628..b7bcc7c 100644
--- a/class4gl/setup/setup_humppa.py
+++ b/class4gl/setup/setup_humppa.py
@@ -8,9 +8,9 @@
 import Pysolar
 import sys
 import pytz
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
 from class4gl import class4gl_input, data_global,class4gl
-from interface_humppa import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 
 
 globaldata = data_global()
diff --git a/class4gl/setup/setup_global.py b/class4gl/setup/setup_igra.py
similarity index 91%
rename from class4gl/setup/setup_global.py
rename to class4gl/setup/setup_igra.py
index b5396a5..dd71324 100644
--- a/class4gl/setup/setup_global.py
+++ b/class4gl/setup/setup_igra.py
@@ -44,6 +44,7 @@
 parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 # parser.add_argument('--first_YYYYMMDD',default="19810101")
 # parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--startyear',default="1981")
 parser.add_argument('--first_station_row')
 parser.add_argument('--last_station_row')
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
@@ -119,22 +120,31 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 # get_valid_stations.py)
 # args.path_input = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
 
-df_stations = pd.read_fwf(fn_stations,names=['Country code',\
-                                               'ID',\
-                                               'Name',\
-                                               'latitude',\
-                                               'longitude',\
-                                               'height',\
-                                               'unknown',\
-                                               'startyear',\
-                                               'endyear'])
+# df_stations = pd.read_fwf(fn_stations,names=['Country code',\
+#                                                'ID',\
+#                                                'Name',\
+#                                                'latitude',\
+#                                                'longitude',\
+#                                                'height',\
+#                                                'unknown',\
+#                                                'startyear',\
+#                                                'endyear'])
+# 
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_input,refetch_stations=False)
+df_stations = all_stations.table
+df_stations.columns
+
 if args.station_id is not None:
-    df_stations = df_stations[df_stations.ID == int(args.station_id)]
+    df_stations = df_stations.query('STNID == '+args.station_id)
 else:
-    if args.first_station_row is not None:
-        df_stations = df_stations[int(args.first_station_row):]
     if args.last_station_row is not None:
         df_stations = df_stations[:(int(args.last_station_row)+1)]
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
 
 STNlist = list(df_stations.iterrows())
 
@@ -143,8 +153,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     one_run = False
 # for iSTN,STN in STNlist[5:]:  
     
-    fnout = args.path_output+"/"+format(STN['ID'],'05d')+"_morning.yaml"
-    fnout_afternoon = args.path_output+"/"+format(STN['ID'],'05d')+"_afternoon.yaml"
+    fnout = args.path_output+"/"+format(STN.name,'05d')+"_morning.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_afternoon.yaml"
     
 
     # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
@@ -152,11 +162,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         
     with open(fnout,'w') as fileout, \
          open(fnout_afternoon,'w') as fileout_afternoon:
-        wy_strm = wyoming(PATH=args.path_input, STNM=STN['ID'])
-        wy_strm.set_STNM(int(STN['ID']))
+        wy_strm = wyoming(PATH=args.path_input, STNM=STN.name)
+        wy_strm.set_STNM(int(STN.name))
 
         # we consider all soundings from 1981 onwards
-        wy_strm.find_first(year=1981)
+        wy_strm.find_first(year=int(args.startyear))
         #wy_strm.find(dt.datetime(2004,10,19,6))
         
         c4gli = class4gl_input(debug_level=logging.INFO)
@@ -258,7 +268,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                         logic_afternoon['daylight'] = \
                           ((c4gli_afternoon.pars.ldatetime - \
                             c4gli_afternoon.pars.lSunset \
-                           ).total_seconds()/3600. <= -2.)
+                           ).total_seconds()/3600. <= 1.)
 
 
                         le3000_afternoon = \
@@ -330,7 +340,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                     print('get profile failed')
                 
     if one_run:
-        STN.name = STN['ID']
+        #STN.name = STN.name
         all_records_morning = get_records(pd.DataFrame([STN]),\
                                       args.path_output,\
                                       subset='morning',
diff --git a/class4gl/setup/setup_igra_pkl.py b/class4gl/setup/setup_igra_pkl.py
new file mode 100644
index 0000000..8e795fe
--- /dev/null
+++ b/class4gl/setup/setup_igra_pkl.py
@@ -0,0 +1,359 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under args.path_output+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+#from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+# parser.add_argument('--first_YYYYMMDD',default="19810101")
+# parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--station_id') # run a specific station id
+# parser.add_argument('--error_handling',default='dump_on_success')
+# parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+
+
+# args.path_output = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+fn_stations = args.path_input+'/igra-stations.txt'
+
+
+#calculate the root mean square error
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+# args.path_input = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+# df_stations = pd.read_fwf(fn_stations,names=['Country code',\
+#                                                'ID',\
+#                                                'Name',\
+#                                                'latitude',\
+#                                                'longitude',\
+#                                                'height',\
+#                                                'unknown',\
+#                                                'startyear',\
+#                                                'endyear'])
+# 
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_input,refetch_stations=False)
+df_stations = all_stations.table
+df_stations.columns
+
+if args.station_id is not None:
+    df_stations = df_stations.query('STNID == '+args.station_id)
+else:
+    if args.last_station_row is not None:
+        df_stations = df_stations[:(int(args.last_station_row)+1)]
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
+
+STNlist = list(df_stations.iterrows())
+
+os.system('mkdir -p '+args.path_output)
+for iSTN,STN in STNlist:  
+    one_run = True
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = args.path_output+"/"+format(STN.name,'05d')+"_morning.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_afternoon.yaml"
+    
+
+    # # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    # #                   for EXP in experiments.keys()])
+    #     
+    # with open(fnout,'w') as fileout, \
+    #      open(fnout_afternoon,'w') as fileout_afternoon:
+    #     wy_strm = wyoming(PATH=args.path_input, STNM=STN.name)
+    #     wy_strm.set_STNM(int(STN.name))
+
+    #     # we consider all soundings from 1981 onwards
+    #     wy_strm.find_first(year=1981)
+    #     #wy_strm.find(dt.datetime(2004,10,19,6))
+    #     
+    #     c4gli = class4gl_input(debug_level=logging.INFO)
+    #     c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+    #     # so we continue as long as we can find a new sounding
+    #             
+    #     while wy_strm.current is not None:
+    #         
+    #         c4gli.clear()
+    #         try: 
+    #             c4gli.get_profile_wyoming(wy_strm)
+    #             #print(STN['ID'],c4gli.pars.datetime)
+    #             #c4gli.get_global_input(globaldata)
+
+    #             print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+    #             logic = dict()
+    #             logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+
+    #             # Sounding should have taken place after 3 hours before sunrise.
+    #             # Note that the actual simulation only start at sunrise
+    #             # (specified by ldatetime_daylight), so the ABL cooling af the time
+    #             # before sunrise is ignored by the simulation.
+    #             logic['daylight'] = \
+    #                 ((c4gli.pars.ldatetime - 
+    #                   c4gli.pars.lSunrise).total_seconds()/3600. >= -3.)
+    #             
+    #             logic['springsummer'] = (c4gli.pars.theta > 278.)
+    #             
+    #             # we take 3000 because previous analysis (ie., HUMPPA) has
+    #             # focussed towards such altitude
+    #             le3000 = (c4gli.air_balloon.z <= 3000.)
+    #             logic['10measurements'] = (np.sum(le3000) >= 10) 
+
+    #             leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+    #             logic['mlerrlow'] = (\
+    #                     (len(np.where(leh)[0]) > 0) and \
+    #                     # in cases where humidity is not defined, the mixed-layer
+    #                     # values get corr
+    #                     (not np.isnan(c4gli.pars.theta)) and \
+    #                     (rmse(c4gli.air_balloon.theta[leh] , \
+    #                           c4gli.pars.theta,filternan_actual=True) < 1.)\
+    #                           )
+    # 
+
+    #             logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+    #             
+    #             print('logic:', logic)
+    #             # the result
+    #             morning_ok = np.mean(list(logic.values()))
+    #             print(morning_ok,c4gli.pars.ldatetime)
+
+    #         except:
+    #             morning_ok =False
+    #             print('obtain morning not good')
+
+    #         # the next sounding will be used either for an afternoon sounding
+    #         # or for the morning sounding of the next day.
+    #         wy_strm.find_next()
+    #         # If the morning is ok, then we try to find a decent afternoon
+    #         # sounding
+    #         if morning_ok == 1.:
+    #             print('MORNING OK!')
+    #             # we get the current date
+    #             current_date = dt.date(c4gli.pars.ldatetime.year, \
+    #                                    c4gli.pars.ldatetime.month, \
+    #                                    c4gli.pars.ldatetime.day)
+    #             c4gli_afternoon.clear()
+    #             print('AFTERNOON PROFILE CLEARED')
+    #             try:
+    #                 c4gli_afternoon.get_profile_wyoming(wy_strm)
+    #                 print('AFTERNOON PROFILE OK')
+
+    #                 if wy_strm.current is not None:
+    #                     current_date_afternoon = \
+    #                                dt.date(c4gli_afternoon.pars.ldatetime.year, \
+    #                                        c4gli_afternoon.pars.ldatetime.month, \
+    #                                        c4gli_afternoon.pars.ldatetime.day)
+    #                 else:
+    #                     # a dummy date: this will be ignored anyway
+    #                     current_date_afternoon = dt.date(1900,1,1)
+
+    #                 # we will dump the latest afternoon sounding that fits the
+    #                 # minimum criteria specified by logic_afternoon
+    #                 print(current_date,current_date_afternoon)
+    #                 c4gli_afternoon_for_dump = None
+    #                 while ((current_date_afternoon == current_date) and \
+    #                        (wy_strm.current is not None)):
+    #                     logic_afternoon =dict()
+
+    #                     logic_afternoon['afternoon'] = \
+    #                         (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+    #                     # the sounding should have taken place before 2 hours
+    #                     # before sunset. This is to minimize the change that a
+    #                     # stable boundary layer (yielding very low mixed layer
+    #                     # heights) is formed which can not be represented by
+    #                     # class.
+    #                     logic_afternoon['daylight'] = \
+    #                       ((c4gli_afternoon.pars.ldatetime - \
+    #                         c4gli_afternoon.pars.lSunset \
+    #                        ).total_seconds()/3600. <= -2.)
+
+
+    #                     le3000_afternoon = \
+    #                         (c4gli_afternoon.air_balloon.z <= 3000.)
+    #                     logic_afternoon['5measurements'] = \
+    #                         (np.sum(le3000_afternoon) >= 5) 
+
+    #                     # we only store the last afternoon sounding that fits these
+    #                     # minimum criteria
+
+    #                     afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+    #                     print('logic_afternoon: ',logic_afternoon)
+    #                     print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+    #                     if afternoon_ok == 1.:
+    #                         # # doesn't work :(
+    #                         # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+    #                         
+    #                         # so we just create a new one from the same wyoming profile
+    #                         c4gli_afternoon_for_dump = class4gl_input()
+    #                         c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+    #                     wy_strm.find_next()
+    #                     c4gli_afternoon.clear()
+    #                     c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+    #                     if wy_strm.current is not None:
+    #                         current_date_afternoon = \
+    #                                dt.date(c4gli_afternoon.pars.ldatetime.year, \
+    #                                        c4gli_afternoon.pars.ldatetime.month, \
+    #                                        c4gli_afternoon.pars.ldatetime.day)
+    #                     else:
+    #                         # a dummy date: this will be ignored anyway
+    #                         current_date_afternoon = dt.date(1900,1,1)
+
+    #                     # Only in the case we have a good pair of soundings, we
+    #                     # dump them to disk
+    #                 if c4gli_afternoon_for_dump is not None:
+    #                     c4gli.update(source='pairs',pars={'runtime' : \
+    #                         int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+    #                              c4gli.pars.datetime_daylight).total_seconds())})
+    # 
+    # 
+    #                     print('ALMOST...')
+    #                     if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
+    #                             
+    #     
+    #                         c4gli.get_global_input(globaldata)
+    #                         print('VERY CLOSE...')
+    #                         if c4gli.check_source_globaldata() and \
+    #                             (c4gli.check_source(source='wyoming',\
+    #                                                check_only_sections='pars')):
+    #                             c4gli.dump(fileout)
+    #                             
+    #                             c4gli_afternoon_for_dump.dump(fileout_afternoon)
+    #                             
+    #                             
+    #                             # for keyEXP,dictEXP in experiments.items():
+    #                             #     
+    #                             #     c4gli.update(source=keyEXP,pars = dictEXP)
+    #                             #     c4gl = class4gl(c4gli)
+    #                             #     # c4gl.run()
+    #                             #     
+    #                             #     c4gl.dump(c4glfiles[key])
+    #                             
+    #                             print('HIT!!!')
+    #                             one_run = True
+    #             except:
+    #                 print('get profile failed')
+    #             
+    if one_run:
+        #STN.name = STN.name
+        all_records_morning = get_records(pd.DataFrame([STN]),\
+                                      args.path_output,\
+                                      subset='morning',
+                                      refetch_records=True,
+                                      )
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      args.path_output,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    # else:
+    #     os.system('rm '+fnout)
+    #     os.system('rm '+fnout_afternoon)
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/class4gl/setup/update_setup.py b/class4gl/setup/update_setup.py
new file mode 100644
index 0000000..a36b60a
--- /dev/null
+++ b/class4gl/setup/update_setup.py
@@ -0,0 +1,327 @@
+# -*- coding: utf-8 -*-
+
+""" 
+Purpose:
+    update variables in class4gl yaml files, eg., when you need new categorical
+    values in the table.
+
+
+"""
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import dateutil.parser
+
+import argparse
+
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--path_output')
+parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--mode',default='ini') # run a specific station id
+# this is the type of the yaml that needs to be updated. Can be 'ini' or 'mod'
+parser.add_argument('--updates')
+parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
+parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+                                                      # to update in the yaml
+                                                      # dataset.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+parser.add_argument('--split_by',default=-1)# station soundings are split
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--global_keys') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# iniitialize global data
+globaldata = data_global()
+if 'era_profiles' in args.updates.strip().split(" "):
+    globaldata.sources = {**globaldata.sources,**{
+            "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
+            "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
+            "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
+            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    # if not (int(args.split_by) > 0) :
+    #         raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            #chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+
+            chunks_current_station = len(all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
+            print('chunks_current_station',chunks_current_station)
+
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk =all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique()[int(args.global_chunk_number) - totalchunks ]
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching current records')
+records_input = get_records(run_stations,\
+                              args.path_input,\
+                              subset=args.subset_input,
+                              refetch_records=False,
+                              )
+
+# if args.timestamp is None:
+#     backupdir = args.path_input+'/'+dt.datetime.now().isoformat()+'/'
+# else: 
+#     backupdir = args.path_input+'/'+args.timestamp+'/'
+# print('creating backup dir: '+backupdir)
+# os.system('mkdir -p "'+backupdir+'"')
+
+
+os.system('mkdir -p '+args.path_output)
+
+for istation,current_station in run_stations.iterrows():
+    records_input_station = records_input.query('STNID == ' +\
+                                                    str(current_station.name))
+
+    records_input_station_chunk = records_input_station.query('STNID == ' +\
+                                                    str(current_station.name)+\
+                                                   '& chunk == '+str(run_station_chunk))
+    print('lenrecords_input_station_chunk: ',len(records_input_station_chunk))
+    print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
+    print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
+    
+    # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
+    #     print("warning: outside of profile number range for station "+\
+    #           str(current_station)+". Skipping chunk number for this station.")
+    if len(records_input_station_chunk) == 0:
+        print("warning: outside of profile number range for station "+\
+              str(current_station)+". Skipping chunk number for this station.")
+    else:
+        # normal case
+        if ((int(args.split_by) > 0) or \
+            (os.path.isfile(args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                 str(run_station_chunk)+'_'+args.subset_input+'.yaml'))):
+            fn_input = \
+                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                    str(run_station_chunk)+'_'+args.subset_input+'.yaml'
+            file_input = \
+                open(fn_input,'r')
+            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
+            file_output = \
+                open(fn_output,'w')
+            # fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+        else:
+            print("\
+Warning. We are choosing chunk 0 without specifying it in filename.    \
+ No-chunk naming will be removed in the future."\
+                 )
+
+            fn_input = \
+                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                    args.subset_input+'.yaml'
+            file_input = \
+                open(fn_input,'r')
+            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
+            file_output = \
+                open(fn_output,'w')
+            # fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          args.subset_forcing+'.pkl'
+
+        onerun = False
+        print('starting station chunk number: '\
+              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+        #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+        #                                                 str(current_station.name)+\
+        #                                                '& chunk == '+str(run_station_chunk))
+        isim = 0
+        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
+                print('starting '+str(isim+1)+' out of '+\
+                  str(len(records_input_station_chunk) )+\
+                  ' (station total: ',str(len(records_input_station)),')')  
+            
+                c4gli_output = get_record_yaml(file_input, 
+                                                record_input.index_start, 
+                                                record_input.index_end,
+                                                mode=args.mode)
+                if args.diag_tropo is not None:
+                    seltropo = (c4gli_input.air_ac.p > c4gli_input.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_input.air_ac[seltropo]
+                    for var in args.diag_tropo:
+                        if var[:3] == 'adv':
+                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                        else:
+                            print("warning: tropospheric variable "+var+" not recognized")
+                if 'era_profiles' in args.updates.strip().split(" "):
+                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
+
+                c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
+
+                cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+                Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+                Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+                R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
+                rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
+                dz = c4gli_output.air_ac.delpdgrav/rho
+                z = [dz.iloc[-1]/2.]
+                for idz in list(reversed(range(0,len(dz)-1,1))):
+                    z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
+                z = list(reversed(z))
+
+                theta = c4gli_output.air_ac.t * \
+                           (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
+                thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
+
+                
+                c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
+                                                                       'theta':list(theta),
+                                                                       'thetav':list(thetav),
+                                                                      }))
+                air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
+                air_ap_mode = 'b'
+                air_ap_input_source = c4gli_output.query_source('air_ac:theta')
+
+
+                c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
+                                     source=air_ap_input_source,
+                                     mode=air_ap_mode)
+
+                if not c4gli_output.check_source_globaldata():
+                    print('Warning: some input sources appear invalid')
+
+
+                
+                #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
+                
+                # if args.global_keys is not None:
+                #     print(args.global_keys.strip(' ').split(' '))
+                #     c4gli_forcing.get_global_input(
+                #         globaldata, 
+                #         only_keys=args.global_keys.strip(' ').split(' ')
+                #     )
+
+                c4gli_output.dump(file_output)
+                    
+                    
+                onerun = True
+                isim += 1
+
+
+        file_input.close()
+        file_output.close()
+
+        if onerun:
+            # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
+            # if os.path.isfile(fn_forcing_pkl):
+            #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
+            # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
+                                                       args.path_output+'/'+'/',\
+                                                       getchunk = int(run_station_chunk),\
+                                                       subset=args.subset_output,
+                                                       refetch_records=True,
+                                                       )
+
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index 9b6398e..5260a95 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -16,7 +16,7 @@
 parser.add_argument('--exec') # chunk simulation script
 parser.add_argument('--first_station_row')
 parser.add_argument('--last_station_row')
-parser.add_argument('--pbs_string',default=' -l walltime=:2:0:0')
+parser.add_argument('--pbs_string',default=' -l walltime=2:0:0')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling')
 parser.add_argument('--subset_forcing',default='morning') 
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 3746cd4..eb2c3b4 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -20,7 +20,7 @@
 parser.add_argument('--last_station_row')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling',default='dump_on_success')
-parser.add_argument('--diag_tropo',default=None)
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
 parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
                                                       # to initialize with.
                                                       # Most common options are
@@ -66,6 +66,7 @@
   'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
@@ -74,10 +75,6 @@
   'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
-
-# #SET = 'GLOBAL'
-# SET = args.dataset
-
 # ========================
 print("getting a list of stations")
 # ========================
@@ -206,6 +203,7 @@
 
     os.system('mkdir -p '+path_exp)
     for istation,current_station in run_stations.iterrows():
+        print(istation,current_station)
         records_morning_station = records_morning.query('STNID == '+str(current_station.name))
         if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
             print("warning: outside of profile number range for station "+\
@@ -234,7 +232,7 @@
             print('starting station chunk number: '\
                   +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
 
-            records_morning_station_chunk = records_morning_station.query('STNID == '+str(current_station.name)+' and chunk == '+str(run_station_chunk)) #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
@@ -247,14 +245,11 @@
                                                     record_morning.index_start, 
                                                     record_morning.index_end,
                                                     mode='ini')
-
-                    # add tropospheric parameters on advection and subsidence
-                    # (for diagnosis)
-
                     if args.diag_tropo is not None:
+                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
                         seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
                         profile_tropo = c4gli_morning.air_ac[seltropo]
-                        for var in diag_tropo:#['t','q','u','v',]:
+                        for var in args.diag_tropo:#['t','q','u','v',]:
                             if var[:3] == 'adv':
                                 mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
                                 c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
@@ -288,9 +283,9 @@
                             if not c4gli_morning.check_source_globaldata():
                                 print('Warning: some input sources appear invalid')
                             c4gl.run()
-                            print('run succesfull')
+                            print('run succesful')
                         except:
-                            print('run not succesfull')
+                            print('run not succesful')
                         onerun = True
 
                         c4gli_morning.dump(file_ini)
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index cdc8923..08d28b8 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -8,12 +8,43 @@
 import sys
 import pytz
 import math
-sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
 from class4gl import class4gl_input, data_global,class4gl
 from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 from class4gl import blh,class4gl_input
 
-
 # this is a variant of global run in which the output of runs are still written
 # out even when the run crashes.
 
@@ -26,100 +57,152 @@
 #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
 
 
-
 EXP_DEFS  =\
 {
-  'GLOBAL_ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'ITER_NOAC':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'ITER_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'ITER_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
-import argparse
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    #parser.add_argument('--timestamp')
-    parser.add_argument('--global-chunk')
-    parser.add_argument('--first-station')
-    parser.add_argument('--last-station')
-    parser.add_argument('--dataset')
-    parser.add_argument('--path-soundings')
-    parser.add_argument('--experiments')
-    parser.add_argument('--split-by',default=-1)# station soundings are split
-                                                # up in chunks
-    parser.add_argument('--station-chunk',default=0)
-    args = parser.parse_args()
-
-
-#SET = 'GLOBAL'
-SET = args.dataset
-
-if 'path-soundings' in args.__dict__.keys():
-    path_soundingsSET = args.__dict__['path-soundings']+'/'+SET+'/'
-else:
-    path_soundingsSET = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/'+SET+'/'
+# #SET = 'GLOBAL'
+# SET = args.dataset
 
-all_stations = stations(path_soundingsSET,suffix='morning',refetch_stations=True).table
+# ========================
+print("getting a list of stations")
+# ========================
 
-all_records_morning = get_records(all_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
-                              refetch_records=False,
-                              )
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
-if 'global_chunk' in args.__dict__.keys():
-    totalchunks = 0
-    stations_iterator = all_stations.iterrows()
-    in_current_chunk = False
-    while not in_current_chunk:
-        istation,current_station = stations_iterator.__next__()
-        all_records_morning_station = all_records_morning.query('STNID == '+str(current_station.name))
-        chunks_current_station = math.ceil(float(len(all_records_morning_station))/float(args.split_by))
-        in_current_chunk = (int(args.global_chunk) < (totalchunks+chunks_current_station))
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
 
-        if in_current_chunk:
-            run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-            run_station_chunk = int(args.global_chunk) - totalchunks 
 
-        totalchunks +=chunks_current_station
 
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
 else:
-    if 'last_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[:(int(args.__dict__['last_station'])+1)]
-    
-    if 'first_station' in args.__dict__.keys():
-        run_stations = run_stations.iloc[int(args.__dict__['first_station']):]
-    if 'station_chunk' in args.__dict__.keys():
-        run_station_chunk = args.station_chunk
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print(all_stations_select)
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
 #print(all_stations)
-print(run_stations)
-print(args.__dict__.keys())
+print('Fetching initial/forcing records')
 records_morning = get_records(run_stations,\
-                              path_soundingsSET,\
-                              subset='morning',
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
                               refetch_records=False,
                               )
-records_afternoon = get_records(run_stations,\
-                                path_soundingsSET,\
-                                subset='afternoon',
-                                refetch_records=False,
-                                )
-
-# align afternoon records with the noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-
-experiments = args.experiments.split(';')
 
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
 for expname in experiments:
     exp = EXP_DEFS[expname]
-    path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/'+SET+'_'+expname+'/'
+    path_exp = args.path_experiments+'/'+expname+'/'
 
     os.system('mkdir -p '+path_exp)
     records_morning_station = records_morning.query('STNID == '+str(current_station.name))
@@ -128,8 +211,18 @@
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(path_soundingsSET+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
+            else:
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
+
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -139,8 +232,12 @@
 
             #iexp = 0
             onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
 
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            isim = 0
+            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
                 #if iexp == 11:
                 
@@ -149,6 +246,15 @@
                                                     record_morning.index_start, 
                                                     record_morning.index_end,
                                                     mode='ini')
+                    if args.diag_tropo is not None:
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in args.diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
                     
                     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
                     
diff --git a/class4gl/simulations/simulations_iter_bowen.py b/class4gl/simulations/simulations_iter_bowen.py
new file mode 100644
index 0000000..9417534
--- /dev/null
+++ b/class4gl/simulations/simulations_iter_bowen.py
@@ -0,0 +1,475 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
+parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ITER_BOWEN':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+# ========================
+print("getting a list of stations")
+# ========================
+
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+
+
+
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print(all_stations_select)
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_morning = get_records(run_stations,\
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
+                              refetch_records=False,
+                              )
+
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
+for expname in experiments:
+    exp = EXP_DEFS[expname]
+    path_exp = args.path_experiments+'/'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+    for istation,current_station in run_stations.iterrows():
+        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
+            else:
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
+
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
+            file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
+
+            #iexp = 0
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+
+            isim = 0
+            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                #if iexp == 11:
+                
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='ini')
+                    if args.diag_tropo is not None:
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in args.diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
+                    
+                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    
+                    
+                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                      record_afternoon.index_start, 
+                                                      record_afternoon.index_end,
+                                                    mode='ini')
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+                    
+                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                    EFobs = (1.-c4gli_morning.pars.EF)/c4gli_morning.pars.EF
+                    
+                    b = c4gli_morning.pars.wwilt
+                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
+                    
+                    
+                    try:
+                        #fb = f(b)
+                        c4gli_morning.pars.wg = b
+                        c4gli_morning.pars.w2 = b
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.H.sum()/(c4gl.out.LE.sum())
+                        fb = EFmod - EFobs
+                        EFmodb = EFmod
+                        c4glb = c4gl
+                        c4gli_morningb = c4gli_morning
+                        
+                        #fc = f(c)
+                        c4gli_morning.pars.wg = c
+                        c4gli_morning.pars.w2 = c
+                        c4gl = class4gl(c4gli_morning)
+                        c4gl.run()
+                        EFmod = c4gl.out.H.sum()/(c4gl.out.LE.sum())
+                        fc = EFmod - EFobs
+                        print (EFmodb,EFobs,fb)
+                        print (EFmod,EFobs,fc)
+                        c4glc = c4gl
+                        c4gli_morningc = c4gli_morning
+                        i=0
+                        
+
+                        if fc*fb > 0.:
+                            if abs(fb) < abs(fc):
+                                c4gl = c4glb
+                                c4gli_morning = c4gli_morningb
+                            else:
+                                c4gl = c4glc
+                                c4gli_morning = c4gli_morningc
+                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        
+                        else:
+                            print('starting ITERATION!!!')
+                            cn  = c - fc/(fc-fb)*(c-b)
+                            
+                            
+                            #fcn = f(cn)
+                            c4gli_morning.pars.wg = np.asscalar(cn)
+                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            fcn = c4gl.out.H.sum()/c4gl.out.LE.sum() - EFobs
+                            
+                            tol = 0.02
+                            ftol = 10.
+                            maxiter = 10
+                            
+                            is1=0
+                            is1max=1
+                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                if fc * fcn > 0:
+                                    temp = c
+                                    c = b
+                                    b = temp
+                                
+                                a = b
+                                fa = fb
+                                b = c
+                                fb = fc
+                                c = cn
+                                fc = fcn
+                                              
+                                print(i,a,b,c,fcn)
+                                
+                                s1 = c - fc/(fc-fb)*(c-b) 
+                                s2 = c - fc/(fc-fa)*(c-a)
+                                
+                                
+                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                
+                                
+                                if (abs(s1-b) < abs(s2-b)):
+                                    is1 = 0
+                                else:
+                                    is1 +=1
+                                    
+                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                if is1 < is1max:           
+                                    s = s1
+                                    print('s1')
+                                else:
+                                    is1 = 0
+                                    s = s2
+                                    print('s2')
+                                
+                                if c > b:
+                                    l = b
+                                    r = c
+                                else:
+                                    l = c
+                                    r = b
+                                
+                                m = (b+c)/2.
+                                     
+                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                    cn = s
+                                    print('midpoint')
+                                else:
+                                    cn = m
+                                    print('bissection')
+                                    
+                                
+                                #fcn = f(cn)
+                                c4gli_morning.pars.wg = np.asscalar(cn)
+                                c4gli_morning.pars.w2 = np.asscalar(cn)
+                                c4gl = class4gl(c4gli_morning)
+                                c4gl.run()
+                                fcn = c4gl.out.H.sum()/c4gl.out.LE.sum() - EFobs
+                                
+                            
+                                i+=1
+                                
+                            if i == maxiter:
+                                raise StopIteration('did not converge')
+
+
+
+
+                        #c4gl = class4gl(c4gli_morning)
+                        #c4gl.run()
+
+                        c4gli_morning.pars.itersteps = i
+                        c4gli_morning.dump(file_ini)
+                        
+                        
+                        c4gl.dump(file_mod,\
+                                      include_input=False,\
+                                   #   timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    except:
+                        print('run not succesfull')
+
+                #iexp = iexp +1
+            file_ini.close()
+            file_mod.close()
+            file_morning.close()
+            file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='mod',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_mod = records_mod.loc[(STNID,index)]
+    #             c4gl_mod = get_record_yaml(file_station_mod, 
+    #                                         record_mod.index_start, 
+    #                                         record_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
diff --git a/class4gl/simulations/update_yaml.py b/class4gl/simulations/simulations_veg.py
similarity index 64%
rename from class4gl/simulations/update_yaml.py
rename to class4gl/simulations/simulations_veg.py
index 1681e88..b2f7ad8 100644
--- a/class4gl/simulations/update_yaml.py
+++ b/class4gl/simulations/simulations_veg.py
@@ -13,12 +13,14 @@
 
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
-parser.add_argument('--global_keys') 
+#parser.add_argument('--timestamp')
 parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 parser.add_argument('--first_station_row')
 parser.add_argument('--last_station_row')
 parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
 parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
                                                       # to initialize with.
                                                       # Most common options are
@@ -26,6 +28,8 @@
 
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
+parser.add_argument('--runtime',default='from_afternoon_profile')
+
 parser.add_argument('--experiments')
 parser.add_argument('--split_by',default=-1)# station soundings are split
                                             # up in chunks
@@ -55,24 +59,36 @@
 
 EXP_DEFS  =\
 {
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_WILT':   {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC_FC':     {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':         {'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_VMIN':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_VMAX':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_V0':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_L025':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_L100':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_L600':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
+# ========================
+print("getting a list of stations")
+# ========================
 
-# #SET = 'GLOBAL'
-# SET = args.dataset
-
-
-print("getting stations")
 # these are all the stations that are found in the input dataset
 all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
+# ====================================
 print('defining all_stations_select')
+# ====================================
+
 # these are all the stations that are supposed to run by the whole batch (all
 # chunks). We narrow it down according to the station(s) specified.
+
+
+
 if args.station_id is not None:
     print("Selecting station by ID")
     stations_iter = stations_iterator(all_stations)
@@ -88,6 +104,7 @@
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
+print(all_stations_select)
 print("getting all records of the whole batch")
 all_records_morning_select = get_records(all_stations_select,\
                                          args.path_forcing,\
@@ -152,30 +169,30 @@
                               refetch_records=False,
                               )
 
-# # note that if runtime is an integer number, we don't need to get the afternoon
-# # profiles. 
-# if args.runtime == 'from_afternoon_profile':
-#     print('Fetching afternoon records for determining the simulation runtimes')
-#     records_afternoon = get_records(run_stations,\
-#                                     args.path_forcing,\
-#                                     subset='afternoon',
-#                                     refetch_records=False,
-#                                     )
-#     
-#     # print(records_morning.index)
-#     # print(records_afternoon.index)
-#     # align afternoon records with the noon records, and set same index
-#     print('hello')
-#     print(len(records_afternoon))
-#     print(len(records_morning))
-# 
-#     print("aligning morning and afternoon records")
-#     records_morning['dates'] = records_morning.ldatetime.dt.date
-#     records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
-#     records_afternoon.set_index(['STNID','dates'],inplace=True)
-#     ini_index_dates = records_morning.set_index(['STNID','dates']).index
-#     records_afternoon = records_afternoon.loc[ini_index_dates]
-#     records_afternoon.index = records_morning.index
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_afternoon_profile':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='afternoon',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
 
 experiments = args.experiments.strip(' ').split(' ')
 for expname in experiments:
@@ -184,26 +201,36 @@
 
     os.system('mkdir -p '+path_exp)
     for istation,current_station in run_stations.iterrows():
+        print(istation,current_station)
         records_morning_station = records_morning.query('STNID == '+str(current_station.name))
         if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            fn_forcing = \
-                    args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                    str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
+            else:
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
 
-            #file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
+            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_mod.yaml'
             file_ini = open(fn_ini,'w')
+            file_mod = open(fn_mod,'w')
 
             #iexp = 0
             onerun = False
             print('starting station chunk number: '\
                   +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
 
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
@@ -216,8 +243,16 @@
                                                     record_morning.index_start, 
                                                     record_morning.index_end,
                                                     mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    if args.diag_tropo is not None:
+                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in args.diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
                     
                     
                     if args.runtime == 'from_afternoon_profile':
@@ -228,6 +263,8 @@
                                                         mode='ini')
                         runtime = int((c4gli_afternoon.pars.datetime_daylight - 
                                              c4gli_morning.pars.datetime_daylight).total_seconds())
+                    elif args.runtime == 'from_input':
+                        runtime = c4gli_morning.pars.runtime
                     else:
                         runtime = int(args.runtime)
 
@@ -235,15 +272,57 @@
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
+                    if expname[-5:] == '_VMAX':
+                        # ini_sel = ini[ini.cveg > ini.cveg.quantile(0.99)]
+                        # ini = c4gldata['GLOBAL_ADV'].frames['profiles']['records_all_stations_ini']
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'alpha' : 0.19835934815210562,
+                                              'cveg' : 0.9324493037539419,
+                                              'z0m' : 1.287697822716918,
+                                              'z0h' : 0.1287697822716918,
+                                              'LAI' : 2.4429540235782645,
+                                              }\
+                                            )
+                    if expname[-3:] == '_V0':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'cveg': 0.0,
+                                             'z0m': 0.01,
+                                             'z0h': 0.001,
+                                             }\
+                                            )
+                    if expname[-3:] == '_VMIN':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'alpha': 0.2868081648656875,
+                                             'cveg': 0.08275966502449594,
+                                             'z0m': 0.05760000169277192,
+                                             'z0h': 0.005760000169277192,
+                                             'LAI': 2.0,
+                                             }\
+                                            )
+                    if expname[-3:] == '_L025':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'lai':.25}\
+                                            )
+                    if expname[-3:] == '_L100':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'lai':1.0}\
+                                            )
+                    if expname[-3:] == '_L600':
+                        c4gli_morning.update(source=expname, pars=\
+                                             {'lai':6.0}\
+                                            )
 
                     c4gl = class4gl(c4gli_morning)
 
                     if args.error_handling == 'dump_always':
                         try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
                             c4gl.run()
-                            print('run succesfull')
+                            print('run succesful')
                         except:
-                            print('run not succesfull')
+                            print('run not succesful')
                         onerun = True
 
                         c4gli_morning.dump(file_ini)
@@ -257,7 +336,10 @@
                     # in this case, only the file will dumped if the runs were
                     # successful
                     elif args.error_handling == 'dump_on_success':
-                        try:
+                       try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
                             c4gl.run()
                             print('run succesfull')
                             c4gli_morning.dump(file_ini)
@@ -268,15 +350,16 @@
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                        except:
-                            print('run not succesfull')
+                       except:
+                           print('run not succesfull')
                     isim += 1
 
 
             file_ini.close()
             file_mod.close()
             file_morning.close()
-            file_afternoon.close()
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon.close()
     
             if onerun:
                 records_ini = get_records(pd.DataFrame([current_station]),\
diff --git a/class4gl/simulations/simulations_wwilt_wfc.py b/class4gl/simulations/simulations_wwilt_wfc.py
index 1e5450c..27a6d6a 100644
--- a/class4gl/simulations/simulations_wwilt_wfc.py
+++ b/class4gl/simulations/simulations_wwilt_wfc.py
@@ -20,6 +20,7 @@
 parser.add_argument('--last_station_row')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
 parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
                                                       # to initialize with.
                                                       # Most common options are
@@ -62,22 +63,28 @@
   'GLOBAL_NOAC_WILT':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC_FC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_WILT':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_FC':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
 
+# ========================
+print("getting a list of stations")
+# ========================
 
-# #SET = 'GLOBAL'
-# SET = args.dataset
-
-
-print("getting stations")
 # these are all the stations that are found in the input dataset
 all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
+# ====================================
 print('defining all_stations_select')
+# ====================================
+
 # these are all the stations that are supposed to run by the whole batch (all
 # chunks). We narrow it down according to the station(s) specified.
+
+
+
 if args.station_id is not None:
     print("Selecting station by ID")
     stations_iter = stations_iterator(all_stations)
@@ -93,6 +100,7 @@
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
+print(all_stations_select)
 print("getting all records of the whole batch")
 all_records_morning_select = get_records(all_stations_select,\
                                          args.path_forcing,\
@@ -175,8 +183,8 @@
     print(len(records_morning))
 
     print("aligning morning and afternoon records")
-    records_morning['dates'] = records_morning.ldatetime.dt.date
-    records_afternoon['dates'] = records_afternoon.ldatetime.dt.date
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
     records_afternoon.set_index(['STNID','dates'],inplace=True)
     ini_index_dates = records_morning.set_index(['STNID','dates']).index
     records_afternoon = records_afternoon.loc[ini_index_dates]
@@ -189,13 +197,23 @@
 
     os.system('mkdir -p '+path_exp)
     for istation,current_station in run_stations.iterrows():
+        print(istation,current_station)
         records_morning_station = records_morning.query('STNID == '+str(current_station.name))
         if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
             print("warning: outside of profile number range for station "+\
                   str(current_station)+". Skipping chunk number for this station.")
         else:
-            file_morning = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_morning.yaml')
-            file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
+            else:
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
+
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
             fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
                      str(int(run_station_chunk))+'_ini.yaml'
             fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
@@ -208,7 +226,7 @@
             print('starting station chunk number: '\
                   +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
 
-            records_morning_station_chunk = records_morning_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
 
             isim = 0
             for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
@@ -221,8 +239,16 @@
                                                     record_morning.index_start, 
                                                     record_morning.index_end,
                                                     mode='ini')
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                    if args.diag_tropo is not None:
+                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in args.diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
                     
                     
                     if args.runtime == 'from_afternoon_profile':
@@ -233,6 +259,8 @@
                                                         mode='ini')
                         runtime = int((c4gli_afternoon.pars.datetime_daylight - 
                                              c4gli_morning.pars.datetime_daylight).total_seconds())
+                    elif args.runtime == 'from_input':
+                        runtime = c4gli_morning.pars.runtime
                     else:
                         runtime = int(args.runtime)
 
@@ -240,12 +268,12 @@
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
-                    if expname == 'GLOBAL_NOAC_WILT':
+                    if expname[-5:] == '_WILT':
                         c4gli_morning.update(source=expname, pars=\
                                              {'wg':c4gli_morning.pars.wwilt,\
                                               'w2':c4gli_morning.pars.wwilt}\
                                             )
-                    if expname == 'GLOBAL_NOAC_FC':
+                    if expname[-3:] == '_FC':
                         c4gli_morning.update(source=expname, pars=\
                                              {'wg':c4gli_morning.pars.wfc,\
                                               'w2':c4gli_morning.pars.wfc}\
@@ -255,10 +283,13 @@
 
                     if args.error_handling == 'dump_always':
                         try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
                             c4gl.run()
-                            print('run successful')
+                            print('run succesful')
                         except:
-                            print('run not successful')
+                            print('run not succesful')
                         onerun = True
 
                         c4gli_morning.dump(file_ini)
@@ -272,7 +303,10 @@
                     # in this case, only the file will dumped if the runs were
                     # successful
                     elif args.error_handling == 'dump_on_success':
-                        try:
+                       try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
                             c4gl.run()
                             print('run succesfull')
                             c4gli_morning.dump(file_ini)
@@ -283,15 +317,16 @@
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                        except:
-                            print('run not succesfull')
+                       except:
+                           print('run not succesfull')
                     isim += 1
 
 
             file_ini.close()
             file_mod.close()
             file_morning.close()
-            file_afternoon.close()
+            if args.runtime == 'from_afternoon_profile':
+                file_afternoon.close()
     
             if onerun:
                 records_ini = get_records(pd.DataFrame([current_station]),\
diff --git a/class4gl/simulations/update_yaml_old.py b/class4gl/simulations/update_yaml_old.py
deleted file mode 100644
index aafed46..0000000
--- a/class4gl/simulations/update_yaml_old.py
+++ /dev/null
@@ -1,276 +0,0 @@
-# -*- coding: utf-8 -*-
-
-""" 
-Purpose:
-    update variables in class4gl yaml files, eg., when you need new categorical
-    values in the table.
-
-
-"""
-
-
-
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-import dateutil.parser
-
-import argparse
-
-
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--first_station_row')
-parser.add_argument('--last_station_row')
-parser.add_argument('--path_experiments')
-parser.add_argument('--experiments')
-parser.add_argument('--station_id') # run a specific station id
-parser.add_argument('--mode',default='ini') # this tells which yaml subset
-parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
-                                                      # to update in the yaml
-                                                      # dataset.
-                                                      # Most common options are
-                                                      # 'morning' and 'ini'.
-
-parser.add_argument('--split_by',default=-1)# station soundings are split
-
-#parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--global_keys') 
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# iniitialize global data
-globaldata = data_global()
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-
-print("getting stations")
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
-
-print('defining all_stations_select')
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
-else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print("getting all records of the whole batch")
-all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,
-                                         refetch_records=False,
-                                         )
-
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
-
-
-    # if not (int(args.split_by) > 0) :
-    #         raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
-
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-            #chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-
-            chunks_current_station = len(all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
-            print('chunks_current_station',chunks_current_station)
-
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk =all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique()[int(args.global_chunk_number) - totalchunks ]
-        
-            totalchunks +=chunks_current_station
-        
-
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
-    else:
-        if args.split_by != -1:
-            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
-        
-
-#print(all_stations)
-print('Fetching current records')
-records_forcing = get_records(run_stations,\
-                              args.path_forcing,\
-                              subset=args.subset_forcing,
-                              refetch_records=False,
-                              )
-
-# if args.timestamp is None:
-#     backupdir = args.path_forcing+'/'+dt.datetime.now().isoformat()+'/'
-# else: 
-#     backupdir = args.path_forcing+'/'+args.timestamp+'/'
-# print('creating backup dir: '+backupdir)
-# os.system('mkdir -p "'+backupdir+'"')
-
-
-for EXP in args.experiments.strip().split(" "):
-    os.system('mkdir -p '+args.path_experiments+'/'+EXP+'/')
-    for istation,current_station in run_stations.iterrows():
-        records_forcing_station = records_forcing.query('STNID == ' +\
-                                                        str(current_station.name))
-    
-        records_forcing_station_chunk = records_forcing.query('STNID == ' +\
-                                                        str(current_station.name)+\
-                                                       '& chunk == '+str(run_station_chunk))
-        print('lenrecords_forcing_station: ',len(records_forcing_station))
-        print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
-        print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
-        
-        # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
-        #     print("warning: outside of profile number range for station "+\
-        #           str(current_station)+". Skipping chunk number for this station.")
-        if len(records_forcing_station_chunk) == 0:
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-            # normal case
-            if ((int(args.split_by) > 0) or \
-                (os.path.isfile(args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'))):
-                fn_forcing = \
-                        args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                        str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_forcing = \
-                    open(fn_forcing,'r')
-                fn_experiment = args.path_experiments+'/'+EXP+'/'+format(current_station.name,'05d')+'_'+\
-                         str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_experiment = \
-                    open(fn_experiment,'w')
-                fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                         str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-    
-                # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-                #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-                #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-            else:
-                print("\
-    Warning. We are choosing chunk 0 without specifying it in filename.    \
-     No-chunk naming will be removed in the future."\
-                     )
-    
-                fn_forcing = \
-                        args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-                        args.subset_forcing+'.yaml'
-                file_forcing = \
-                    open(fn_forcing,'r')
-                fn_experiment = args.path_experiments+'/'+EXP+'/'+format(current_station.name,'05d')+'_'+\
-                         str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_experiment = \
-                    open(fn_experiment,'w')
-                fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
-                         str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-    
-                # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-                #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-                #          args.subset_forcing+'.pkl'
-    
-            onerun = False
-            print('starting station chunk number: '\
-                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-    
-            #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-    
-            # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
-            #                                                 str(current_station.name)+\
-            #                                                '& chunk == '+str(run_station_chunk))
-            isim = 0
-            for (STNID,chunk,index),record_forcing in records_forcing_station_chunk.iterrows():
-                    print('starting '+str(isim+1)+' out of '+\
-                      str(len(records_forcing_station_chunk) )+\
-                      ' (station total: ',str(len(records_forcing_station)),')')  
-                
-                    c4gli_forcing = get_record_yaml(file_forcing, 
-                                                    record_forcing.index_start, 
-                                                    record_forcing.index_end,
-                                                    mode=args.mode)
-                    seltropo = (c4gli_forcing.air_ac.p > c4gli_forcing.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                    profile_tropo = c4gli_forcing.air_ac[seltropo]
-                    mean_advt_tropo = np.mean(profile_tropo.advt_x +profile_tropo.advt_y )
-                    c4gli_forcing.update(source='era-interim',pars={'advt_tropo':mean_advt_tropo})
-                    
-                    #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
-                    
-                    if args.global_keys is not None:
-                        print(args.global_keys.strip(' ').split(' '))
-                        c4gli_forcing.get_global_input(
-                            globaldata, 
-                            only_keys=args.global_keys.strip(' ').split(' ')
-                        )
-    
-                    c4gli_forcing.dump(file_experiment)
-                        
-                        
-                    onerun = True
-                    isim += 1
-    
-    
-            file_forcing.close()
-            file_experiment.close()
-    
-            if onerun:
-                # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
-                # if os.path.isfile(fn_forcing_pkl):
-                #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
-                # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-                # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-                records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
-                                                           args.path_experiments+'/'+EXP+'/',\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset=args.subset_forcing,
-                                                           refetch_records=True,
-                                                           )
-    

From 4e2728fad73f40792f1eab8e786b24cd36d2c297 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Fri, 26 Oct 2018 15:19:13 +0200
Subject: [PATCH 095/129] new procedure update_input.py and update_output.py

---
 class4gl/setup/batch_update_input.py | 149 ++++++++++++
 class4gl/setup/update_input.py       | 331 +++++++++++++++++++++++++++
 2 files changed, 480 insertions(+)
 create mode 100644 class4gl/setup/batch_update_input.py
 create mode 100644 class4gl/setup/update_input.py

diff --git a/class4gl/setup/batch_update_input.py b/class4gl/setup/batch_update_input.py
new file mode 100644
index 0000000..d2b06b0
--- /dev/null
+++ b/class4gl/setup/batch_update_input.py
@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+parser = argparse.ArgumentParser()
+#if __name__ == '__main__':
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--pbs_string',default=' -l walltime=2:0:0')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling')
+parser.add_argument('--updates')
+parser.add_argument('--subset_input',default='morning') 
+                                        # this tells which yaml subset
+                                        # to initialize with.
+                                        # Most common options are
+                                        # 'morning' and 'ini'.
+parser.add_argument('--subset_output',default='morning') 
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime')
+# delete folders of experiments before running them
+parser.add_argument('--cleanup_output',default=False)
+parser.add_argument('--split_by',default=50)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--path_input') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+
+
+#arguments only used for update_yaml.py
+parser.add_argument('--path_dataset') 
+parser.add_argument('--global_keys') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+
+# #SET = 'GLOBAL'
+# SET = args.dataset
+
+# path_inputSET = args.path_input+'/'+SET+'/'
+
+print("getting all stations from --path_input")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting stations by --station_id")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table [--first_station_row,--last_station_row]")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,\
+                                         refetch_records=False,\
+                                        )
+
+print('splitting batch in --split_by='+args.split_by+' jobs.')
+totalchunks = 0
+for istation,current_station in all_stations_select.iterrows():
+    records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+    chunks_current_station = math.ceil(float(len(records_morning_station_select))/float(args.split_by))
+    totalchunks +=chunks_current_station
+
+print('total chunks (= size of array-job): ' + str(totalchunks))
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+
+
+if args.cleanup_output:
+    os.system("rm -R "+args.path_output+'/')
+
+# C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+            str(totalchunks-1)+" -v '"
+# propagate arguments towards the job script
+first = True
+for argkey in args.__dict__.keys():
+    if ((argkey not in ['pbs_string','cleanup_output']) and \
+        # default values are specified in the simulation script, so
+        # excluded here
+        (args.__dict__[argkey] is not None)
+       ):
+        if first:
+            command +='C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        else:
+            command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+    first = False
+
+command = command+"'"
+print('Submitting array job: '+command)
+os.system(command)
+
+
+    #os.system(command)
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/setup/update_input.py b/class4gl/setup/update_input.py
new file mode 100644
index 0000000..07000f2
--- /dev/null
+++ b/class4gl/setup/update_input.py
@@ -0,0 +1,331 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--updates')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
+parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+parser.add_argument('--subset_output',default='morning')
+
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+
+# iniitialize global data
+globaldata = data_global()
+if 'era_profiles' in args.updates.strip().split(","):
+    globaldata.sources = {**globaldata.sources,**{
+            "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
+            "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
+            "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
+            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+# ========================
+print("getting a list of stations")
+# ========================
+
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+
+
+
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print(all_stations_select)
+print("getting all records of the whole batch")
+all_records_input_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_input_station_select = all_records_input_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_input_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_input = get_records(run_stations,\
+                              args.path_input,\
+                              subset=args.subset_input,
+                              refetch_records=False,
+                              )
+
+
+
+os.system('mkdir -p '+args.path_output)
+for istation,current_station in run_stations.iterrows():
+    print(istation,current_station)
+    records_input_station = records_input.query('STNID == '+str(current_station.name))
+    if (int(args.split_by) * int(run_station_chunk)) >= (len(records_input_station)):
+        print("warning: outside of profile number range for station "+\
+              str(current_station)+". Skipping chunk number for this station.")
+    else:
+        fn_input = args.path_input+'/'+format(current_station.name,'05d')+'_'+args.subset_input+'.yaml'
+        if os.path.isfile(fn_input):
+            file_input = open(fn_input)
+        else:
+            fn_input = \
+                 args.path_input+'/'+format(current_station.name,'05d')+\
+                 '_'+str(run_station_chunk)+'_'+args.subset_input+'.yaml'
+            file_input = open(fn_input)
+
+        fn_output = args.path_output+'/'+format(current_station.name,'05d')+'_'+\
+                 str(int(run_station_chunk))+'_'+args.subset_output+'.yaml'
+        file_output = open(fn_output,'w')
+
+        #iexp = 0
+        onerun = False
+        print('starting station chunk number: '\
+              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+        records_input_station_chunk = records_input_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        isim = 0
+        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
+                print('starting '+str(isim+1)+' out of '+\
+                  str(len(records_input_station_chunk) )+\
+                  ' (station total: ',str(len(records_input_station)),')')  
+            
+        
+                c4gli_output = get_record_yaml(file_input, 
+                                                record_input.index_start, 
+                                                record_input.index_end,
+                                                mode='ini')
+                if args.diag_tropo is not None:
+                    print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                    seltropo = (c4gli_output.air_ac.p > c4gli_output.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_output.air_ac[seltropo]
+                    for var in args.diag_tropo:#['t','q','u','v',]:
+                        if var[:3] == 'adv':
+                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                        else:
+                            print("warning: tropospheric variable "+var+" not recognized")
+
+
+                if 'era_profiles' in args.updates.strip().split(","):
+                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
+
+                    c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
+
+                    cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+                    Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+                    Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+                    R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
+                    rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
+                    dz = c4gli_output.air_ac.delpdgrav/rho
+                    z = [dz.iloc[-1]/2.]
+                    for idz in list(reversed(range(0,len(dz)-1,1))):
+                        z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
+                    z = list(reversed(z))
+
+                    theta = c4gli_output.air_ac.t * \
+                               (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
+                    thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
+
+                    
+                    c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
+                                                                           'theta':list(theta),
+                                                                           'thetav':list(thetav),
+                                                                          }))
+                    air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
+                    air_ap_mode = 'b'
+                    air_ap_input_source = c4gli_output.query_source('air_ac:theta')
+
+
+                    c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
+                                         source=air_ap_input_source,
+                                         mode=air_ap_mode)
+
+
+                onerun = True
+                
+                c4gli_output.dump(file_output)
+                    
+                    
+        file_output.close()
+        file_input.close()
+
+        if onerun:
+            records_output = get_records(pd.DataFrame([current_station]),\
+                                                       args.path_output,\
+                                                       getchunk = int(run_station_chunk),\
+                                                       subset=args.subset_output,
+                                                       refetch_records=True,
+                                                       )
+        else:
+            # remove empty files
+            os.system('rm '+fn_output)
+
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_output.ldatetime.dt.date]
+# records_afternoon.index = records_output.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_output.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_output.yaml','r') as file_station_output, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_input+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_output in records_iterator(records_output):
+#             c4gli_output = get_record_yaml(file_station_output, 
+#                                         record_output.index_start, 
+#                                         record_output.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_output.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+

From facdd8186aaf95c55192f7206ef0af3bd2d62dbf Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 29 Oct 2018 13:01:35 +0100
Subject: [PATCH 096/129] new procedure update_input.py and update_output.py

---
 class4gl/model.py                          |  10 +-
 class4gl/processing/batch_update_output.py | 149 ++++++++++
 class4gl/processing/update_output.py       | 327 +++++++++++++++++++++
 class4gl/simulations/copy_update.py        | 316 ++++++++++++++++++++
 class4gl/simulations/simulations.py        |   1 +
 5 files changed, 799 insertions(+), 4 deletions(-)
 create mode 100644 class4gl/processing/batch_update_output.py
 create mode 100644 class4gl/processing/update_output.py
 create mode 100644 class4gl/simulations/copy_update.py

diff --git a/class4gl/model.py b/class4gl/model.py
index 2ae873f..28fe5d0 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -151,6 +151,7 @@ def __init__(self, model_input = None,debug_level=None):
                 self.input = cp.deepcopy(model_input)
 
     def load_yaml_dict(self,yaml_dict):
+        dictouttemp = pd.DataFrame()
         for key,data in yaml_dict.items():
             if key == 'pars':
                 for keydata,value in data.items():
@@ -167,10 +168,11 @@ def load_yaml_dict(self,yaml_dict):
             #     self.__dict__[key] = data
 
 
-        self.tsteps = len(dictouttemp['h'])
-        self.out = model_output(self.tsteps)
-        for keydictouttemp in dictouttemp.keys():
-            self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
+        if len(dictouttemp) > 0:
+            self.tsteps = len(dictouttemp['h'])
+            self.out = model_output(self.tsteps)
+            for keydictouttemp in dictouttemp.keys():
+                self.out.__dict__[keydictouttemp] = np.array(dictouttemp[keydictouttemp])
 
 
   
diff --git a/class4gl/processing/batch_update_output.py b/class4gl/processing/batch_update_output.py
new file mode 100644
index 0000000..ab51951
--- /dev/null
+++ b/class4gl/processing/batch_update_output.py
@@ -0,0 +1,149 @@
+# -*- coding: utf-8 -*-
+
+"""
+Usage:
+python batch_update.py --exec $CLASS4GL/simulations/update_yaml_old.py
+--path_experiments $VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC/ --path_input
+$VSC_DATA_VO/D2D/data/C4GL/GLOBAL_NOAC_BACKUP_20180904/ --c4gl_path_lib
+$CLASS4GL --split_by 50 --global_keys "KGC" --subset_input morning --experiments
+"GLOBAL_NOAC"
+"""
+
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+parser = argparse.ArgumentParser()
+#if __name__ == '__main__':
+parser.add_argument('--exec') # chunk simulation script
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--pbs_string',default=' -l walltime=2:0:0')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling')
+parser.add_argument('--subset_input',default='morning') 
+                                        # this tells which yaml subset
+                                        # to initialize with.
+                                        # Most common options are
+                                        # 'morning' and 'ini'.
+parser.add_argument('--subset_output',default='morning') 
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+parser.add_argument('--runtime')
+# delete folders of experiments before running them
+parser.add_argument('--split_by',default=50)# station soundings are split
+                                            # up in chunks
+
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--path_input') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+
+
+#arguments only used for update_yaml.py
+parser.add_argument('--path_dataset') 
+parser.add_argument('--global_keys') 
+parser.add_argument('--updates') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+print("getting all stations from --path_input")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting stations by --station_id")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table [--first_station_row,--last_station_row]")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,\
+                                         refetch_records=False,\
+                                        )
+
+print('splitting batch in --split_by='+args.split_by+' jobs.')
+totalchunks = 0
+for istation,current_station in all_stations_select.iterrows():
+    records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+    chunks_current_station = len(records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
+    totalchunks +=chunks_current_station
+
+print('total chunks (= size of array-job) per experiment: ' + str(totalchunks))
+
+#if sys.argv[1] == 'qsub':
+# with qsub
+
+
+
+#C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+            str(totalchunks-1)+" -v '"
+# propagate arguments towards the job script
+first = True
+for argkey in args.__dict__.keys():
+    if ((argkey not in ['pbs_string']) and \
+        # default values are specified in the simulation script, so
+        # excluded here
+        (args.__dict__[argkey] is not None)
+       ):
+        if first:
+            command +='C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+        else:
+            command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+    first = False
+
+command = command+"'"
+print('Submitting array job: '+command)
+os.system(command)
+
+
+    #os.system(command)
+# elif sys.argv[1] == 'wsub':
+#     
+#     # with wsub
+#     STNlist = list(df_stations.iterrows())
+#     NUMSTNS = len(STNlist)
+#     PROCS = NUMSTNS 
+#     BATCHSIZE = 1 #math.ceil(np.float(NUMSTNS)/np.float(PROCS))
+# 
+#     os.system('wsub -batch /user/data/gent/gvo000/gvo00090/D2D/scripts/C4GL/global_run.pbs -t 0-'+str(PROCS-1))
+
diff --git a/class4gl/processing/update_output.py b/class4gl/processing/update_output.py
new file mode 100644
index 0000000..8fab56b
--- /dev/null
+++ b/class4gl/processing/update_output.py
@@ -0,0 +1,327 @@
+# -*- coding: utf-8 -*-
+
+""" 
+Purpose:
+    update variables in class4gl yaml files, eg., when you need new categorical
+    values in the table.
+
+
+"""
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+import dateutil.parser
+
+import argparse
+
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--path_output')
+parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--mode',default='ini') # run a specific station id
+# this is the type of the yaml that needs to be updated. Can be 'ini' or 'mod'
+parser.add_argument('--updates')
+parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
+parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+                                                      # to update in the yaml
+                                                      # dataset.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+
+parser.add_argument('--split_by',default=-1)# station soundings are split
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--global_keys') 
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# iniitialize global data
+globaldata = data_global()
+if 'era_profiles' in args.updates.strip().split(","):
+    globaldata.sources = {**globaldata.sources,**{
+            "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
+            "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
+            "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
+            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            }}
+
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+
+print("getting stations")
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+print('defining all_stations_select')
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    # if not (int(args.split_by) > 0) :
+    #         raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            #chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+
+            chunks_current_station = len(all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
+            print('chunks_current_station',chunks_current_station)
+
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk =all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique()[int(args.global_chunk_number) - totalchunks ]
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching current records')
+records_input = get_records(run_stations,\
+                              args.path_input,\
+                              subset=args.subset_input,
+                              refetch_records=False,
+                              )
+
+# if args.timestamp is None:
+#     backupdir = args.path_input+'/'+dt.datetime.now().isoformat()+'/'
+# else: 
+#     backupdir = args.path_input+'/'+args.timestamp+'/'
+# print('creating backup dir: '+backupdir)
+# os.system('mkdir -p "'+backupdir+'"')
+
+
+os.system('mkdir -p '+args.path_output)
+
+for istation,current_station in run_stations.iterrows():
+    records_input_station = records_input.query('STNID == ' +\
+                                                    str(current_station.name))
+
+    records_input_station_chunk = records_input_station.query('STNID == ' +\
+                                                    str(current_station.name)+\
+                                                   '& chunk == '+str(run_station_chunk))
+    print('lenrecords_input_station_chunk: ',len(records_input_station_chunk))
+    print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
+    print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
+    
+    # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
+    #     print("warning: outside of profile number range for station "+\
+    #           str(current_station)+". Skipping chunk number for this station.")
+    if len(records_input_station_chunk) == 0:
+        print("warning: outside of profile number range for station "+\
+              str(current_station)+". Skipping chunk number for this station.")
+    else:
+        # normal case
+        if ((int(args.split_by) > 0) or \
+            (os.path.isfile(args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                 str(run_station_chunk)+'_'+args.subset_input+'.yaml'))):
+            fn_input = \
+                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                    str(run_station_chunk)+'_'+args.subset_input+'.yaml'
+            file_input = \
+                open(fn_input,'r')
+            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
+            file_output = \
+                open(fn_output,'w')
+            # fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+        else:
+            print("\
+Warning. We are choosing chunk 0 without specifying it in filename.    \
+ No-chunk naming will be removed in the future."\
+                 )
+
+            fn_input = \
+                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
+                    args.subset_input+'.yaml'
+            file_input = \
+                open(fn_input,'r')
+            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
+                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
+            file_output = \
+                open(fn_output,'w')
+            # fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
+
+            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
+            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
+            #          args.subset_forcing+'.pkl'
+
+        onerun = False
+        print('starting station chunk number: '\
+              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+        #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
+        #                                                 str(current_station.name)+\
+        #                                                '& chunk == '+str(run_station_chunk))
+        isim = 0
+        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
+                print('starting '+str(isim+1)+' out of '+\
+                  str(len(records_input_station_chunk) )+\
+                  ' (station total: ',str(len(records_input_station)),')')  
+            
+                c4gli_output = get_record_yaml(file_input, 
+                                                record_input.index_start, 
+                                                record_input.index_end,
+                                                mode=args.mode)
+                if args.diag_tropo is not None:
+                    seltropo = (c4gli_input.air_ac.p > c4gli_input.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_input.air_ac[seltropo]
+                    for var in args.diag_tropo:
+                        if var[:3] == 'adv':
+                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                        else:
+                            print("warning: tropospheric variable "+var+" not recognized")
+                if 'era_profiles' in args.updates.strip().split(" "):
+                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
+
+                    c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
+
+                    cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+                    Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+                    Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+                    R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
+                    rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
+                    dz = c4gli_output.air_ac.delpdgrav/rho
+                    z = [dz.iloc[-1]/2.]
+                    for idz in list(reversed(range(0,len(dz)-1,1))):
+                        z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
+                    z = list(reversed(z))
+
+                    theta = c4gli_output.air_ac.t * \
+                               (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
+                    thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
+
+                    
+                    c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
+                                                                           'theta':list(theta),
+                                                                           'thetav':list(thetav),
+                                                                          }))
+                    air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
+                    air_ap_mode = 'b'
+                    air_ap_input_source = c4gli_output.query_source('air_ac:theta')
+
+
+                    c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
+                                         source=air_ap_input_source,
+                                         mode=air_ap_mode)
+
+                if not c4gli_output.check_source_globaldata():
+                    print('Warning: some input sources appear invalid')
+
+
+                
+                #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
+                
+                # if args.global_keys is not None:
+                #     print(args.global_keys.strip(' ').split(' '))
+                #     c4gli_forcing.get_global_input(
+                #         globaldata, 
+                #         only_keys=args.global_keys.strip(' ').split(' ')
+                #     )
+
+                c4gli_output.dump(file_output)
+                    
+                    
+                onerun = True
+                isim += 1
+
+
+        file_input.close()
+        file_output.close()
+
+        if onerun:
+            # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
+            # if os.path.isfile(fn_forcing_pkl):
+            #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
+            # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
+            records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
+                                                       args.path_output+'/'+'/',\
+                                                       getchunk = int(run_station_chunk),\
+                                                       subset=args.subset_output,
+                                                       refetch_records=True,
+                                                       )
+
diff --git a/class4gl/simulations/copy_update.py b/class4gl/simulations/copy_update.py
new file mode 100644
index 0000000..2410d2c
--- /dev/null
+++ b/class4gl/simulations/copy_update.py
@@ -0,0 +1,316 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import sys
+import pytz
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--updates')
+parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--error_handling',default='dump_on_success')
+parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
+parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
+                                                      # to initialize with.
+                                                      # Most common options are
+                                                      # 'morning' and 'ini'.
+parser.add_argument('--subset_output',default='morning')
+
+
+# Tuntime is usually specified from the afternoon profile. You can also just
+# specify the simulation length in seconds
+
+parser.add_argument('--experiments')
+parser.add_argument('--split_by',default=-1)# station soundings are split
+                                            # up in chunks
+
+#parser.add_argument('--station-chunk',default=0)
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+# ========================
+print("getting a list of stations")
+# ========================
+
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+
+
+
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print(all_stations_select)
+print("getting all records of the whole batch")
+all_records_input_select = get_records(all_stations_select,\
+                                         args.path_input,\
+                                         subset=args.subset_input,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+
+    if not (int(args.split_by) > 0) :
+            raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_input_station_select = all_records_input_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_input_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+        
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
+        
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
+    else:
+        if args.split_by != -1:
+            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_input = get_records(run_stations,\
+                              args.path_input,\
+                              subset=args.subset_input,
+                              refetch_records=False,
+                              )
+
+
+
+os.system('mkdir -p '+args.path_output)
+for istation,current_station in run_stations.iterrows():
+    print(istation,current_station)
+    records_input_station = records_input.query('STNID == '+str(current_station.name))
+    if (int(args.split_by) * int(run_station_chunk)) >= (len(records_input_station)):
+        print("warning: outside of profile number range for station "+\
+              str(current_station)+". Skipping chunk number for this station.")
+    else:
+        fn_input = args.path_input+'/'+format(current_station.name,'05d')+'_'+args.subset_input+'.yaml'
+        if os.path.isfile(fn_input):
+            file_input = open(fn_input)
+        else:
+            fn_input = \
+                 args.path_input+'/'+format(current_station.name,'05d')+\
+                 '_'+str(run_station_chunk)+'_'+args.subset_input+'.yaml'
+            file_input = open(fn_input)
+
+        fn_output = args.path_output+'/'+format(current_station.name,'05d')+'_'+\
+                 str(int(run_station_chunk))+'_'+args.subset_output+'.yaml'
+        file_output = open(fn_output,'w')
+
+        #iexp = 0
+        onerun = False
+        print('starting station chunk number: '\
+              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+
+        records_input_station_chunk = records_input_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        isim = 0
+        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
+                print('starting '+str(isim+1)+' out of '+\
+                  str(len(records_input_station_chunk) )+\
+                  ' (station total: ',str(len(records_input_station)),')')  
+            
+        
+                c4gli_output = get_record_yaml(file_input, 
+                                                record_input.index_start, 
+                                                record_input.index_end,
+                                                mode='ini')
+                if args.diag_tropo is not None:
+                    print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                    seltropo = (c4gli_input.air_ac.p > c4gli_input.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                    profile_tropo = c4gli_input.air_ac[seltropo]
+                    for var in args.diag_tropo:#['t','q','u','v',]:
+                        if var[:3] == 'adv':
+                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                        else:
+                            print("warning: tropospheric variable "+var+" not recognized")
+
+
+                if 'era_profiles' in args.updates.strip().split(" "):
+                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
+
+                    c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
+
+                    cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+                    Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+                    Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+                    R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
+                    rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
+                    dz = c4gli_output.air_ac.delpdgrav/rho
+                    z = [dz.iloc[-1]/2.]
+                    for idz in list(reversed(range(0,len(dz)-1,1))):
+                        z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
+                    z = list(reversed(z))
+
+                    theta = c4gli_output.air_ac.t * \
+                               (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
+                    thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
+
+                    
+                    c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
+                                                                           'theta':list(theta),
+                                                                           'thetav':list(thetav),
+                                                                          }))
+                    air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
+                    air_ap_mode = 'b'
+                    air_ap_input_source = c4gli_output.query_source('air_ac:theta')
+
+
+                    c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
+                                         source=air_ap_input_source,
+                                         mode=air_ap_mode)
+
+
+
+                
+                c4gli_output.dump(file_output)
+                    
+                    
+        file_output.close()
+        file_input.close()
+
+        if onerun:
+            records_output = get_records(pd.DataFrame([current_station]),\
+                                                       args.path_output,\
+                                                       getchunk = int(run_station_chunk),\
+                                                       subset=args.subset_output,
+                                                       refetch_records=True,
+                                                       )
+        else:
+            # remove empty files
+            os.system('rm '+fn_output)
+
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_output.ldatetime.dt.date]
+# records_afternoon.index = records_output.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_output.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_output.yaml','r') as file_station_output, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_input+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_output in records_iterator(records_output):
+#             c4gli_output = get_record_yaml(file_station_output, 
+#                                         record_output.index_start, 
+#                                         record_output.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_output.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index eb2c3b4..0303d5a 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -66,6 +66,7 @@
   'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
     'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},

From eb902e6e0ac926ebe5190c57ab599c2ef50f25b0 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 29 Oct 2018 13:30:05 +0100
Subject: [PATCH 097/129] new procedure update_input.py and update_output.py

---
 class4gl/interface/interface_new_koeppen.py | 4 +++-
 class4gl/interface/interface_stations.py    | 6 +++---
 class4gl/interface_multi.py                 | 7 +++++--
 3 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index a7bd245..3936756 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -116,7 +116,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_experiments+'/'+key+'/',\
                       args.path_forcing+'/',\
                       globaldata,\
-                      refetch_records=False
+                      refetch_records=False,
+                      obs_filter = True
+                                            
                     )
 
 sns.reset_orig()
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index 4fe9218..b290c35 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -1,4 +1,3 @@
-'''
 import numpy as np
 
 import pandas as pd
@@ -124,9 +123,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_experiments+'/'+key+'/',\
                       args.path_forcing+'/',\
                       globaldata,\
-                      refetch_records=False
+                      refetch_records=False,
+                      obs_filter = True
+
                     )
-'''
 
 if bool(args.make_figures):
     fig = plt.figure(figsize=(10,7))   #width,height
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 0409509..ff8c32e 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -51,13 +51,15 @@
 os.system('module load Ruby')
 
 class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',]):
+    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False):
         """ creates an interactive interface for analysing class4gl experiments
 
         INPUT:
             path_exp : path of the experiment output
             path_obs : path of the observations 
             globaldata: global data that is being shown on the map
+            obs_filtering: extra data filter considering observation tendencies
+                           beyond what the model can capture
             refetch_stations: do we need to build the list of the stations again?
         OUTPUT:
             the procedure returns an interface object with interactive plots
@@ -68,6 +70,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
         self.globaldata = globaldata
 
  
+        self.obs_filter= obs_filter
         self.path_exp = path_exp
         self.path_obs = path_obs
         self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
@@ -219,7 +222,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
             # some observational sounding still seem problematic, which needs to be
             # investigated. In the meantime, we filter them
 
-            if self.path_obs is not None:
+            if ((self.path_obs is not None) and (self.obs_filter)):
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\

From 18c2a794819906842549bb0b81f93b581a21b439 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 29 Oct 2018 13:39:42 +0100
Subject: [PATCH 098/129] new procedure update_input.py and update_output.py

---
 class4gl/interface_functions.py | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index fbc4db0..0d9af4e 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -596,4 +596,11 @@ def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
                               (obs_afternoon.ldatetime - \
                                obs_morning.ldatetime).dt.seconds*3600.
     return stats
+def tendencies_new(mod_afternoon,mod_ini,keys):
+    stats = pd.DataFrame()
+    for key in keys: 
+        stats['d'+key+'dt'] = ""
+        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+                              (mod_ini.runtime)
+    return stats
 

From eb6769cfcc8714f1cf502a2562327538b946cf46 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 29 Oct 2018 14:02:25 +0100
Subject: [PATCH 099/129] new procedure update_input.py and update_output.py

---
 class4gl/interface/interface.py          |  4 ++-
 class4gl/interface/interface_koeppen.py  |  4 ++-
 class4gl/interface/interface_stations.py |  5 +--
 class4gl/interface_functions.py          |  4 +--
 class4gl/interface_multi.py              | 41 ++++++++++++++++--------
 5 files changed, 39 insertions(+), 19 deletions(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index b895ea4..4531b5b 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -19,6 +19,7 @@
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--figure_filename_2',default=None)
 parser.add_argument('--experiments_labels',default=None)
+parser.add_argument('--tendencies_revised',default=False)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -125,7 +126,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_experiments+'/'+key+'/',\
                       args.path_forcing,\
                       globaldata,\
-                      refetch_records=False
+                      refetch_records=False,
+                      tendencies_revised = args.tendencies_revised
                     )
 
 if args.make_figures:
diff --git a/class4gl/interface/interface_koeppen.py b/class4gl/interface/interface_koeppen.py
index ec77bb8..ada9a44 100644
--- a/class4gl/interface/interface_koeppen.py
+++ b/class4gl/interface/interface_koeppen.py
@@ -18,6 +18,7 @@
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--figure_filename_2',default=None)
 parser.add_argument('--experiments_labels',default=None)
+parser.add_argument('--tendencies_revised',default=False)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -124,7 +125,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_experiments+'/'+key+'/',\
                       args.path_forcing+'/',\
                       globaldata,\
-                      refetch_records=False
+                      refetch_records=False,
+                      tendencies_revised = args.tendencies_revised
                     )
 
 key = args.experiments.strip(' ').split(' ')[0]
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index b290c35..f452269 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -15,6 +15,7 @@
 parser.add_argument('--load_globaldata',default=False) # load the data needed for the interface
 parser.add_argument('--make_figures',default=None)
 parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--tendencies_revised',default=False)
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -124,8 +125,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_forcing+'/',\
                       globaldata,\
                       refetch_records=False,
-                      obs_filter = True
-
+                      obs_filter = True,
+                      tendencies_revised = args.tendencies_revised
                     )
 
 if bool(args.make_figures):
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 0d9af4e..b880ae9 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -596,11 +596,11 @@ def tendencies(mod_afternoon,obs_afternoon,obs_morning,keys):
                               (obs_afternoon.ldatetime - \
                                obs_morning.ldatetime).dt.seconds*3600.
     return stats
-def tendencies_new(mod_afternoon,mod_ini,keys):
+def tendencies_rev(mod_afternoon,mod_ini,keys):
     stats = pd.DataFrame()
     for key in keys: 
         stats['d'+key+'dt'] = ""
-        stats['d'+key+'dt'] = (mod_afternoon[key] - obs_morning[key])/ \
+        stats['d'+key+'dt'] = (mod_afternoon[key] - mod_ini[key])/ \
                               (mod_ini.runtime)
     return stats
 
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index ff8c32e..2e83af9 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -51,7 +51,7 @@
 os.system('module load Ruby')
 
 class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False):
+    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False,tendencies_revised=False):
         """ creates an interactive interface for analysing class4gl experiments
 
         INPUT:
@@ -71,6 +71,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
 
  
         self.obs_filter= obs_filter
+        self.tendencies_revised = tendencies_revised
         self.path_exp = path_exp
         self.path_obs = path_obs
         self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
@@ -156,18 +157,32 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
 
             self.frames['stats']['viewkeys'] = ['h','theta','q']
             print('Calculating table statistics')
-            self.frames['stats']['records_all_stations_mod_stats'] = \
-                    tendencies(self.frames['stats']['records_all_stations_mod'],\
-                               self.frames['stats']['records_all_stations_obs_afternoon'],\
-                               self.frames['stats']['records_all_stations_ini'],\
-                               self.frames['stats']['viewkeys']\
-                              )
-            self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                    tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                               self.frames['stats']['records_all_stations_obs_afternoon'],\
-                               self.frames['stats']['records_all_stations_ini'],\
-                               self.frames['stats']['viewkeys']\
-                              )
+
+            if self.tendencies_revised:
+                self.frames['stats']['records_all_stations_mod_stats'] = \
+                        tendencies_rev(self.frames['stats']['records_all_stations_mod'],\
+                                           self.frames['stats']['records_all_stations_ini'],\
+                                           self.frames['stats']['viewkeys']\
+                                  )
+                self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                        tendencies_rev(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                                           self.frames['stats']['records_all_stations_ini'],\
+                                           self.frames['stats']['viewkeys']\
+                                  )
+
+            else:
+                self.frames['stats']['records_all_stations_mod_stats'] = \
+                        tendencies(self.frames['stats']['records_all_stations_mod'],\
+                                   self.frames['stats']['records_all_stations_obs_afternoon'],\
+                                   self.frames['stats']['records_all_stations_ini'],\
+                                   self.frames['stats']['viewkeys']\
+                                  )
+                self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
+                        tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                                   self.frames['stats']['records_all_stations_obs_afternoon'],\
+                                   self.frames['stats']['records_all_stations_ini'],\
+                                   self.frames['stats']['viewkeys']\
+                                  )
 
         self.frames['stats']['inputkeys'] = inputkeys
         

From 9ebd8c6b7a6140b30af8055f9d822632c4ae0af5 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 6 Nov 2018 15:34:10 +0100
Subject: [PATCH 100/129] full update

---
 class4gl/interface/interface_new_koeppen.py | 107 ++++++-------
 class4gl/interface/interface_stations.py    |  65 ++++++--
 class4gl/interface/taylorDiagram.py         |   7 +
 class4gl/interface/test.png                 | Bin 0 -> 17020 bytes
 class4gl/interface/test_histogram.py        |  25 +++
 class4gl/interface/world_histogram.py       | 164 ++++++++++++++++++++
 class4gl/interface_multi.py                 |   9 +-
 class4gl/setup/setup_goamazon.py            |  63 ++++----
 class4gl/setup/setup_igra.py                |   4 +-
 class4gl/simulations/simulations.py         |   5 +
 10 files changed, 351 insertions(+), 98 deletions(-)
 create mode 100644 class4gl/interface/test.png
 create mode 100644 class4gl/interface/test_histogram.py
 create mode 100644 class4gl/interface/world_histogram.py

diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index 3936756..27bc478 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -16,6 +16,7 @@
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--figure_filename_2',default=None)
 parser.add_argument('--experiments_labels',default=None)
+parser.add_argument('--obs_filter',default='True')
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -117,10 +118,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_forcing+'/',\
                       globaldata,\
                       refetch_records=False,
-                      obs_filter = True
+                      obs_filter = (args.obs_filter == 'True')
                                             
                     )
-
 sns.reset_orig()
 key = args.experiments.strip(' ').split(' ')[0]
 xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
@@ -217,7 +217,7 @@ def brightness(rrggbb):
     print(np.sum(kgc_select))
     koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
 
-koeppenlookuptable = koeppenlookuptable[koeppenlookuptable.amount >= 200]
+#koeppenlookuptable = koeppenlookuptable[koeppenlookuptable.amount >= 200]
 koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
 # koeppenlookuptable = koeppenlookuptable[:9]
 include_koeppen = list(koeppenlookuptable.KGCID)
@@ -241,9 +241,8 @@ def brightness(rrggbb):
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
         #print(obs.std())
-        #if i == 2:
         dias[varkey]._ax.axis["left"].label.set_text(\
-            "Normalized standard deviation")
+             "Normalized root mean square error")
         if i == 1:
             axes[varkey].annotate('Normalized standard deviation',\
                         xy= (0.05,0.27),
@@ -261,9 +260,11 @@ def brightness(rrggbb):
                        )
 
 
+            # ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x-1.))
+            # #ax.axis["left"].axis.set_major_formatter(ticks)
 
-            # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
-            # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+            # # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+            # dias[varkey]._ax.axis["left"].axis.set_major_formatter(ticks)
         #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
         # Q95 = obs.quantile(0.95)
         # Q95 = obs.quantile(0.90)
@@ -317,7 +318,7 @@ def brightness(rrggbb):
             dias[varkey].add_sample(STD/STD_OBS, PR,\
                            marker='o',ls='', mfc='white',mec='black',
                            zorder=-100,
-                           ms=10.*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                           ms=3.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
                                 np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                            # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                            # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
@@ -325,7 +326,7 @@ def brightness(rrggbb):
             dias[varkey].add_sample(STD/STD_OBS, PR,\
                            marker='o',ls='', mfc='none',mec='black',
                            zorder=700,
-                           ms=10.*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                           ms=3.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
                                 np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                            # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                            # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
@@ -357,53 +358,54 @@ def brightness(rrggbb):
         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
             icolor = 0
             for ikoeppen,koeppen in koeppenlookuptable.iterrows():
-                print(ikoeppen,':',koeppen)
-                kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
-                koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
-                koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
+                if koeppen.amount >= 200:
+                    print(ikoeppen,':',koeppen)
+                    kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
+                    koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
+                    koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
     
-                #axes[varkey].scatter(koeppen_obs,koeppen_mod,marker=symbols[ikoeppen],color=colors[ikey])
-                         #  label=key+", "+\
-                         #                    'R = '+str(round(PR[0],3))+', '+\
-                         #                    'RMSE = '+str(round(RMSE,5))+', '+\
-                         #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+                    #axes[varkey].scatter(koeppen_obs,koeppen_mod,marker=symbols[ikoeppen],color=colors[ikey])
+                             #  label=key+", "+\
+                             #                    'R = '+str(round(PR[0],3))+', '+\
+                             #                    'RMSE = '+str(round(RMSE,5))+', '+\
+                             #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
     
     
     
-            # # pl.scatter(obs,mod,label=key+", "+\
-            # #                              'R = '+str(round(PR[0],3))+', '+\
-            # #                              'RMSE = '+str(round(RMSE,5))+', '+\
-            # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
-                
-                dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                               pearsonr(koeppen_mod,koeppen_obs)[0],
-                               marker='o',linewidth=0.5,
-                                        mfc=koeppen.color,mec='black',#koeppen.color,
-                                        zorder=300+icolor,
-                               ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
-                               # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
-                               # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
-                               )
-                dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                               pearsonr(koeppen_mod,koeppen_obs)[0],
-                               marker='o',linewidth=0.5,
-                                        mfc=koeppen.color,mec='black',#koeppen.color,
-                                        zorder=301+icolor, ms=1
-                               # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
-                               # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
-                               )
-
-
-                # dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                #                pearsonr(koeppen_mod,koeppen_obs)[0],
-                #                         marker='o',linewidth=0.5, mfc='none',mec=str(koeppen.color),
-                #                         zorder=600+icolor,
-                #                ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
-                #                # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
-                #                # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
-                #                )
-
-                icolor += 1
+                # # pl.scatter(obs,mod,label=key+", "+\
+                # #                              'R = '+str(round(PR[0],3))+', '+\
+                # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+                # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+                    
+                    dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                                   pearsonr(koeppen_mod,koeppen_obs)[0],
+                                   marker='o',linewidth=0.5,
+                                            mfc=koeppen.color,mec='black',#koeppen.color,
+                                            zorder=300+icolor,
+                                   ms=3.5*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                                   # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                                   # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                                   )
+                    dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                                   pearsonr(koeppen_mod,koeppen_obs)[0],
+                                   marker='o',linewidth=0.5,
+                                            mfc=koeppen.color,mec='black',#koeppen.color,
+                                            zorder=301+icolor, ms=1
+                                   # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                                   # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                                   )
+
+
+                    # dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
+                    #                pearsonr(koeppen_mod,koeppen_obs)[0],
+                    #                         marker='o',linewidth=0.5, mfc='none',mec=str(koeppen.color),
+                    #                         zorder=600+icolor,
+                    #                ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
+                    #                # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                    #                # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                    #                )
+
+                    icolor += 1
     
             latex = {}
             latex['dthetadt'] =  r'$d \theta / dt $'
@@ -608,6 +610,7 @@ def brightness(rrggbb):
     
     if args.figure_filename is not None:
         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+        fig.savefig(args.figure_filename.replace('png','pdf')); print("Image file written to:", args.figure_filename)
     fig.show()  
 
     koeppenlookuptable = koeppenlookuptable.sort_index()
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index f452269..a7dbd21 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -16,6 +16,7 @@
 parser.add_argument('--make_figures',default=None)
 parser.add_argument('--figure_filename',default=None)
 parser.add_argument('--tendencies_revised',default=False)
+parser.add_argument('--obs_filter',default="True")
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -125,7 +126,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_forcing+'/',\
                       globaldata,\
                       refetch_records=False,
-                      obs_filter = True,
+                      obs_filter = (args.obs_filter == 'True'),
                       tendencies_revised = args.tendencies_revised
                     )
 
@@ -137,7 +138,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     #colors = ['r','g','b','m']
     colors = ['k']
-    symbols = ['*','x','+']
+    symbols = ['^','x','+']
     dias = {}
     
     for varkey in ['h','theta','q']:                                                    
@@ -150,6 +151,21 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
         dias[varkey]._ax.axis["left"].label.set_text(\
             "Normalized standard deviation")
+        if i == 1:
+            axes[varkey].annotate('Normalized standard deviation',\
+                        xy= (0.05,0.36),
+                        color='black',
+                        rotation=90.,
+                        xycoords='figure fraction',
+                        weight='normal',
+                        fontsize=10.,
+                        horizontalalignment='center',
+                        verticalalignment='center' ,
+                        #bbox={'edgecolor':'black',
+                        #      'boxstyle':'circle',
+                        #      'fc':koeppen.color,
+                        #      'alpha':1.0}
+                       )
         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
         #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
@@ -221,8 +237,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                 'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
                                 'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
                                 r'$R$ = '+format(PR,'0.2f')
-                ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
-       horizontalalignment='right', verticalalignment='bottom' ,
+                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='left', verticalalignment='top' ,
         bbox={'edgecolor':'black',
                           'fc':'white',  
                               'boxstyle':'square',
@@ -234,7 +250,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                 'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
                                 r'$R$ = '+format(PR,'0.2f')
 
-                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .98 ), xycoords='axes fraction',fontsize=9,
+                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
        horizontalalignment='left', verticalalignment='top' ,
         bbox={'edgecolor':'black',
                           'fc':'white',  
@@ -290,6 +306,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                mfc='k', mec='k', # B&W
                                #mfc=colors[ikey], mec=colors[ikey], # Colors
                                label=key)
+
                 istation += 1
     
             if varkey == 'q':
@@ -299,13 +316,38 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             elif varkey == 'h':
                 units_final = r'[$m\, h^{-1}$]'
     
-            axes[varkey].set_xlabel('observations')     
+            axes[varkey].set_xlabel('Observed')     
             axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12)                                     
+
+
+        # if varkey == 'q':
+        #     print('get_xlim not working well...STRANGE')
+        #     limits =  [np.percentile(nani,1),np.percentile(nani,99)]
+        # else:
+        #     limits =  [np.percentile(nani,1.0),np.percentile(nani,99.0)]
+
+
         if i==0:                                    
-            axes[varkey].set_ylabel('model')                                            
-        abline(1,0,axis=axes[varkey])
+            axes[varkey].set_ylabel('Modelled')                                            
         i +=1
-    
+          
+        axes[varkey].set_aspect('equal')
+        low  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].min()
+        high  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].max()
+
+        low  = np.min([low,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].min()])
+        high  = np.max([high,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].max()])
+
+        low = low - (high - low)*0.1
+        high = high + (high - low)*0.1
+        axes[varkey].set_xlim([low,high])
+        axes[varkey].set_ylim([low,high])
+        abline(1,0,axis=axes[varkey])
+        if varkey == 'q':
+            ticks = ticker.FuncFormatter(lambda x, pos:
+                                         '{0:g}'.format(x*1000.))
+            axes[varkey].xaxis.set_major_formatter(ticks)
+            axes[varkey].yaxis.set_major_formatter(ticks)
     
     
     # # legend for different forcing simulations (colors)
@@ -320,7 +362,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     
     # legend for different stations (symbols)
-    ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+    ax = fig.add_axes([0.08,-0.02,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
     leg = []
     isymbol = 0
     for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
@@ -334,7 +376,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     
     ax.axis('off')
-    ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
+    ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10,ncol=4)
     
     
     fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
@@ -345,6 +387,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
     if args.figure_filename is not None:
         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+        fig.savefig(args.figure_filename.replace('png','pdf')); print("Image file written to:", args.figure_filename)
     fig.show()  
 
 
diff --git a/class4gl/interface/taylorDiagram.py b/class4gl/interface/taylorDiagram.py
index d9e9e26..5ece652 100644
--- a/class4gl/interface/taylorDiagram.py
+++ b/class4gl/interface/taylorDiagram.py
@@ -10,6 +10,7 @@
 
 import numpy as NP
 import matplotlib.pyplot as PLT
+from matplotlib import ticker
 
 
 class TaylorDiagram(object):
@@ -83,6 +84,12 @@ def __init__(self, refstd,
         ax.axis["left"].set_axis_direction("bottom")  # "X axis"
         #ax.axis["left"].label.set_text("Standard deviation (model)/ Observed (observations)")
 
+
+        # #ax.axis["left"].axis.set_ticklabels(["1.0","0.8","0.6","0.4","0.2","0.0","0.2","0.4","0.6","0.8","1.0"])
+        # ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x-1.))
+        # ax.axis["left"].axis.set_major_formatter(ticks)
+
+        ax.axis["left"].toggle(ticklabels=False, label=False)
         ax.axis["right"].set_axis_direction("top")    # "Y-axis"
         ax.axis["right"].toggle(ticklabels=True)
         ax.axis["right"].major_ticklabels.set_axis_direction(
diff --git a/class4gl/interface/test.png b/class4gl/interface/test.png
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USnE should be investigated!')
                 valid = (\
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
index 3cc3c74..2315353 100644
--- a/class4gl/setup/setup_goamazon.py
+++ b/class4gl/setup/setup_goamazon.py
@@ -179,14 +179,15 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         dpars['STNID'] = current_station.name
         
 
-        # there are issues with the lower measurements in the HUMPPA campaign,
-        # for which a steady decrease of potential temperature is found, which
-        # is unrealistic.  Here I filter them away
-        ifirst = 0
-        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
-            ifirst = ifirst+1
-        print ('ifirst:',ifirst)
-        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        # # there are issues with the lower measurements in the HUMPPA campaign,
+        # # for which a steady decrease of potential temperature is found, which
+        # # is unrealistic.  Here I filter them away
+        # ifirst = 0
+        # while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+        #     ifirst = ifirst+1
+        # print ('ifirst:',ifirst)
+        # air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        air_balloon = air_balloon.iloc[:].reset_index().drop(['index'],axis=1)
         
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
         valid_indices = air_balloon.index[is_valid].values
@@ -299,29 +300,29 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # make theta increase strong enough to avoid numerical
         # instability
         air_ap_tail_orig = pd.DataFrame(air_ap_tail)
-        air_ap_tail = pd.DataFrame()
-        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
-        theta_low = air_ap_head['theta'].iloc[2]
-        z_low = air_ap_head['z'].iloc[2]
-        ibottom = 0
-        for itop in range(0,len(air_ap_tail_orig)):
-            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
-            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
-            if (
-                #(z_mean > z_low) and \
-                (z_mean > (z_low+10.)) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                #(theta_mean > (theta_low+0.2) ) and \
-                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
-
-                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
-                ibottom = itop+1
-                theta_low = air_ap_tail.theta.iloc[-1]
-                z_low =     air_ap_tail.z.iloc[-1]
-            # elif  (itop > len(air_ap_tail_orig)-10):
-            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
-        
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if (
+        #         #(z_mean > z_low) and \
+        #         (z_mean > (z_low+10.)) and \
+        #         #(theta_mean > (theta_low+0.2) ) and \
+        #         #(theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.00001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
         air_ap = \
             pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
         
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index dd71324..3c71599 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -260,8 +260,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
                         logic_afternoon['afternoon'] = \
                             (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                        # the sounding should have taken place before 2 hours
-                        # before sunset. This is to minimize the change that a
+                        # the sounding should have taken place before 1 hours
+                        # before sunset. This is to minimize the chance that a
                         # stable boundary layer (yielding very low mixed layer
                         # heights) is formed which can not be represented by
                         # class.
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 0303d5a..3008f65 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -66,6 +66,8 @@
   'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
     'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
@@ -275,6 +277,9 @@
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
+                    if exp[-3:] == 'SM2':
+                        c4gli_morning.update(source=expname, pars={'wg': c4gli_morning.pars.wg - (c4gli_morning.pars.wg - c4gli_morning.pars.wwilt)/2.)
+                        c4gli_morning.update(source=expname, pars={'w2': c4gli_morning.pars.w2 - (c4gli_morning.pars.w2 - c4gli_morning.pars.wwilt)/2.)
 
                     c4gl = class4gl(c4gli_morning)
 

From bce24936acaa0a72b255f50697026f5afbe6a0f7 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 6 Nov 2018 15:34:29 +0100
Subject: [PATCH 101/129] full update

---
 class4gl/interface/test.png | Bin 17020 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 class4gl/interface/test.png

diff --git a/class4gl/interface/test.png b/class4gl/interface/test.png
deleted file mode 100644
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USnE
Date: Tue, 6 Nov 2018 15:40:04 +0100
Subject: [PATCH 102/129] full update

---
 class4gl/interface/interface.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index 4531b5b..eb9311d 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -20,6 +20,7 @@
 parser.add_argument('--figure_filename_2',default=None)
 parser.add_argument('--experiments_labels',default=None)
 parser.add_argument('--tendencies_revised',default=False)
+parser.add_argument('--obs_filter',default='True')
 args = parser.parse_args()
 
 print('Adding python library:',args.c4gl_path_lib)
@@ -128,6 +129,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       globaldata,\
                       refetch_records=False,
                       tendencies_revised = args.tendencies_revised
+                      obs_filter = (args.obs_filter == 'True')
                     )
 
 if args.make_figures:

From 25ae1c6bd6ead82d85a4dad5268054030112d5a6 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 6 Nov 2018 15:44:35 +0100
Subject: [PATCH 103/129] full update

---
 class4gl/interface/interface.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index eb9311d..bb234fc 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -128,7 +128,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       args.path_forcing,\
                       globaldata,\
                       refetch_records=False,
-                      tendencies_revised = args.tendencies_revised
+                      tendencies_revised = args.tendencies_revised,
                       obs_filter = (args.obs_filter == 'True')
                     )
 

From df6907926582f248c27f45c177f5b41857ec7bb5 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 13 Nov 2018 18:47:26 +0100
Subject: [PATCH 104/129] make batch pbs work with arbitrary modules

---
 class4gl/simulations/batch_simulations.pbs | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index 92e496c..830aef7 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -7,7 +7,8 @@
 #PBS -m a
 #PBS -N c4gl_sim
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+#module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+$LOADDEPSCLASS4GL 
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 

From de6af0f9a7910461e310e9c86410322354c58450 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 20 Nov 2018 14:41:42 +0100
Subject: [PATCH 105/129] update

---
 class4gl/simulations/batch_simulations.pbs | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index 830aef7..a8da5e2 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -8,6 +8,10 @@
 #PBS -N c4gl_sim
 
 #module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+module purge
+source ~/.bashrc
+
+echo $LOADDEPSCLASS4GL 
 $LOADDEPSCLASS4GL 
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"

From 1839a2a145e5ec547be9c597631d5605dfd64cd4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 21 Nov 2018 09:52:27 +0100
Subject: [PATCH 106/129] fix load modules batch_setup_era.py

---
 class4gl/class4gl.py                        |  23 +-
 class4gl/interface/interface_new_koeppen.py | 334 +++++++++++++-------
 class4gl/interface/world_histogram.py       | 237 +++++++++++++-
 class4gl/setup/batch_setup_era.pbs          |   6 +-
 class4gl/setup/batch_update_input.py        |   1 +
 class4gl/setup/update_input.py              |   8 +-
 class4gl/simulations/batch_simulations.pbs  |   3 +-
 class4gl/simulations/simulations.py         |   8 +-
 class4gl/simulations/simulations_iter.py    |   1 +
 class4gl/simulations/simulations_veg.py     |  44 ++-
 10 files changed, 494 insertions(+), 171 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index e0a307a..2bdf6b4 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -924,6 +924,7 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
             #    self.update(source='globaldata',air_ac=pd.DataFrame({key:list([np.nan])}))
 
         #print('keys 2', keys)
+        print(keys)
 
         for key in keys:
             # If we find it, then we obtain the variables
@@ -1058,15 +1059,21 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                         if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
                             ilonmin = ilon        
                         
-                        if ilatmin < ilatmax:
-                            ilatrange = range(ilatmin,ilatmax+1)
+                        # for the koeppen climate classification we just take nearest
+                        print(key)
+                        if key == 'KGC':
+                            ilatrange = range(ilat,ilat+1)
+                            ilonrange = range(ilon,ilon+1)
                         else:
-                            ilatrange = range(ilatmax,ilatmin+1)
-                            
-                        if ilonmin < ilonmax:
-                            ilonrange = range(ilonmin,ilonmax+1)
-                        else:
-                            ilonrange = range(ilonmax,ilonmin+1)     
+                            if ilatmin < ilatmax:
+                                ilatrange = range(ilatmin,ilatmax+1)
+                            else:
+                                ilatrange = range(ilatmax,ilatmin+1)
+                                
+                            if ilonmin < ilonmax:
+                                ilonrange = range(ilonmin,ilonmax+1)
+                            else:
+                                ilonrange = range(ilonmax,ilonmin+1)     
                             
                         if 'time' in list(globaldata.datasets[key].page[key].dims):
                             DIST = np.abs((globaldata.datasets[key].page['time'].values - classdatetime))
diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index 27bc478..e5d7161 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -1,3 +1,4 @@
+'''
 import numpy as np
 import pandas as pd
 import sys
@@ -121,12 +122,40 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       obs_filter = (args.obs_filter == 'True')
                                             
                     )
+                    '''
 sns.reset_orig()
+
+
+
+lookup_symbols= {
+ 'A':'equatorial',
+ 'B':'arid',
+ 'C':'warm temperate',
+ 'D':'snow',
+ 'E':'polar',
+ 'W':'desert',
+ 'S':'steppe',
+ 'f':'fully humid',
+ 's':'summer dry',
+ 'w':'winter dry',
+ 'm':'monsoonal',
+ 'h':'hot arid',
+ 'k':'cold arid',
+ 'a':'hot summer',
+ 'b':'warm summer',
+ 'c':'cool summer',
+ 'd':'extremely continental',
+ 'F':'polar frost',
+ 'T':'polar tundra',
+ } 
+
 key = args.experiments.strip(' ').split(' ')[0]
 xrkoeppen = xr.open_dataset('/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc')
 koeppenlookuptable = pd.DataFrame()
 koeppenlookuptable['KGCID'] = pd.Series(xrkoeppen['KGCID'])
 
+from matplotlib.patches import Rectangle,FancyBboxPatch
+
 def abline(slope, intercept,axis):
     """Plot a line from slope and intercept"""
     #axis = plt.gca()
@@ -217,14 +246,12 @@ def brightness(rrggbb):
     print(np.sum(kgc_select))
     koeppenlookuptable.iloc[ikoeppen]['amount'] = np.sum(kgc_select)
 
-#koeppenlookuptable = koeppenlookuptable[koeppenlookuptable.amount >= 200]
 koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
-# koeppenlookuptable = koeppenlookuptable[:9]
 include_koeppen = list(koeppenlookuptable.KGCID)
 
 
 if args.make_figures:
-    fig = plt.figure(figsize=(11,7))   #width,height
+    fig = plt.figure(figsize=(11,8.0))   #width,height
     i = 1                                                                           
     axes = {}         
     axes_taylor = {}         
@@ -245,7 +272,7 @@ def brightness(rrggbb):
              "Normalized root mean square error")
         if i == 1:
             axes[varkey].annotate('Normalized standard deviation',\
-                        xy= (0.05,0.27),
+                        xy= (0.05,0.37),
                         color='black',
                         rotation=90.,
                         xycoords='figure fraction',
@@ -406,6 +433,14 @@ def brightness(rrggbb):
                     #                )
 
                     icolor += 1
+
+
+
+
+
+
+
+
     
             latex = {}
             latex['dthetadt'] =  r'$d \theta / dt $'
@@ -428,6 +463,60 @@ def brightness(rrggbb):
         abline(1,0,axis=axes[varkey])
         i +=1
 
+
+
+    axph = fig.add_axes([0,0,1,1])
+    axph.xaxis.set_visible(False)
+    axph.yaxis.set_visible(False)
+    axph.set_zorder(1000)
+    axph.patch.set_alpha(0.0)
+    
+    axph.add_patch(Rectangle((0.006,0.01), 0.99, 0.13, alpha=0.71,
+                           facecolor='white',edgecolor='grey'))
+
+
+    idx = 0
+    koeppenlookuptable_sel = koeppenlookuptable[koeppenlookuptable.amount >= 200].sort_values('KGCID',ascending=True)
+
+    for ikoepp,koepp in koeppenlookuptable_sel.iterrows():
+        xy_box = ((0.059+np.floor(idx/4)*0.335),0.127- 0.09*((np.mod(idx,4)/(len(koeppenlookuptable_sel)/4))))
+        xy_text = list(xy_box)
+        xy_text[0] += 0.009
+        xy_text[1] -= 0.00
+        xy_color = list(xy_box)
+        xy_color[0] += -0.045
+        xy_color[1] -= 0.019
+        axph.add_patch(Rectangle(xy_color,0.049,0.023,
+                                      edgecolor='black', 
+                               #       boxstyle='round', 
+                                      #xycoords='figure fraction',
+                                      fc=koepp.color,
+                                      alpha=1.0))
+    
+        axph.annotate(koepp.KGCID,
+                    xy= xy_box,
+                    color='white' if (brightness(koepp.color)<0.5) else 'black', 
+                    family='monospace',
+                    xycoords='figure fraction',
+                    weight='bold',
+                    fontsize=10.,
+                    horizontalalignment='right',
+                    verticalalignment='top' ,
+                    # bbox={'edgecolor':'black',
+                    #       'boxstyle':'round',
+                    #       'fc':koepp.color,
+                    #       'alpha':1.0}
+                   )
+        full_name = ""
+        if koepp.KGCID is not "Ocean":
+            for char in koepp.KGCID:
+                full_name += lookup_symbols[char] + ' - '
+            full_name = full_name[:-3]
+        axph.annotate(full_name,xy=xy_text,color='k'
+                    ,fontsize=8,xycoords='figure fraction',weight='bold',horizontalalignment='left',verticalalignment='top')
+
+        idx +=1
+
     
 
     i = 1
@@ -601,7 +690,7 @@ def brightness(rrggbb):
     # ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10)
     
     
-    fig.subplots_adjust(top=0.95,bottom=0.09,left=0.08,right=0.94,hspace=0.35,wspace=0.29)
+    fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.35,wspace=0.29)
     
     
     #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
@@ -779,9 +868,9 @@ def brightness(rrggbb):
         #sns.set(style="ticks", palette="deep")
 
         
-        exppairs = {'obs|ref'     :['soundings','GLOBAL_ADV'],
-                    'fcap|wilt'   :['GLOBAL_ADV_FC'  ,'GLOBAL_ADV_WILT'],
-                    'allveg|noveg':['GLOBAL_ADV_VMAX','GLOBAL_ADV_VMIN']
+        exppairs = {'obs|ref'     :['soundings',keylabels[0]],
+                    'fcap|wilt'   :[keylabels[1] ,keylabels[2]],
+                    'allveg|noveg':[keylabels[3],keylabels[4]]
                    }
         current_palette = sns.color_palette('deep')
         exppalettes = {'obs|ref'     :['white','grey'],
@@ -792,11 +881,11 @@ def brightness(rrggbb):
         data_all['expname'] = ""
         print('making alternative names for legends')
         expnames = {'soundings':'obs',\
-                    'GLOBAL_ADV':'ref',\
-                    'GLOBAL_ADV_WILT':'dry',\
-                    'GLOBAL_ADV_FC':'wet',\
-                    'GLOBAL_ADV_VMIN':'noveg',\
-                    'GLOBAL_ADV_VMAX':'fullveg',\
+                    keylabels[0]:'ref',\
+                    keylabels[1]:'dry',\
+                    keylabels[2]:'wet',\
+                    keylabels[3]:'noveg',\
+                    keylabels[4]:'fullveg',\
                    }
         for expname_orig,expname in expnames.items():
             data_all['expname'][data_all['source'] == expname_orig] = expname
@@ -807,6 +896,9 @@ def brightness(rrggbb):
 
         icolor = 0
         
+        koeppenlookuptable = koeppenlookuptable[koeppenlookuptable.amount >= 200]
+        koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
+        koeppenlookuptable = koeppenlookuptable[:9]
         fig, axes = plt.subplots(nrows=len(varkeys)*len(koeppenlookuptable), \
                                  ncols=len(exppairs), \
                                  figsize=(8, 13), #width, height
@@ -820,114 +912,114 @@ def brightness(rrggbb):
         irow = 0
         sns.set_style('whitegrid')
         for ikoeppen,koeppen in koeppenlookuptable.iterrows():
-            for exppairname,exppair in exppairs.items():
-                for varkey in varkeys:
-                    varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
-                    ax = axes[irow,icol]
-
-                 #   axes[i] = fig.add_subplot(len(varkeys)*len(koeppenlookuptable),len(exppairs),icolor)
-            #sns.violinplot(x='KGC',y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
-            
-            #ax.set_title(input_key_full)
-                    current_data = data_all[(data_all['exppair'] == exppairname) & (data_all['KGCname']  == koeppen.KGCID)]
-                    sns.violinplot(y='exppair', x=varkey_full,
-                                        hue="expname",split=True,
-                             palette=exppalettes[exppairname],
-                            # palette=["m", "g",'r','b'],
-                             linewidth=1.0,inner='quart',
-                                        data=current_data,sym='',legend=False,ax=ax)
-                    ax.legend("")
-                    ax.legend_.draw_frame(False)
-                    ax.set_yticks([])
-                    ax.set_ylabel("")
-
-                    # if varkey == 'q':
-                    #     ticks = ticker.FuncFormatter(lambda x, pos:
-                    #                                  '{0:g}'.format(x*1000.))
-                    #     ax.xaxis.set_major_formatter(ticks)
-
-                    if varkey == 'q':
-                        title_final = r'$dq/dt$'
-                        xlabel_final = r'[$\mathrm{g\, kg^{-1}\, h^{-1}}$]'
-                    elif varkey == 'theta':
-                        title_final = r'$d\theta/dt$'
-                        xlabel_final = r'[$\mathrm{K\, h^{-1}}$]'
-                    elif varkey == 'h':
-                        title_final = r'$dh/dt$'
-                        xlabel_final = r'[$\mathrm{m\, h^{-1}}$]'
-
-
-                    ax.set_xlabel("")
-                    #sns.despine(left=True, right=True, bottom=False, top=False)
-                    if irow == (len(varkeys)*len(koeppenlookuptable)-1):
-                        #ax.set_frame_on(False)
-
-                        ax.set_xlabel(xlabel_final)
-                        ax.tick_params(top='off', bottom='on', left='off',
-                                        right='off', labelleft='off',
-                                        labeltop='off',
-                                        labelbottom='on'
-                                      )
-                        ax.spines['top'].set_visible(False)
-                        ax.spines['bottom'].set_visible(True)
-                        ax.spines['left'].set_visible(True)
-                        ax.spines['right'].set_visible(True)
-                        #sns.despine(left=True, right=True, bottom=True, top=False)
-                    elif irow == 0:
-                        ax.set_title(title_final,fontsize=17.)
-                        ax.tick_params(top='off', bottom='off', left='off',
-                                        right='off', labelleft='off',
-                                        labelbottom='off')
-                        #ax.set_frame_on(False)
-                        # ax.spines['left'].set_visible(True)
-                        # ax.spines['right'].set_visible(True)
-                        ax.spines['top'].set_visible(True)
-                        ax.spines['bottom'].set_visible(False)
-                        ax.spines['left'].set_visible(True)
-                        ax.spines['right'].set_visible(True)
-                        #sns.despine(left=True, right=True, bottom=False, top=True)
-                        #ax.axis("off")
-                    elif np.mod(irow,len(exppairs)) == 0:
-                        ax.tick_params(top='off', bottom='off', left='off',
-                                        right='off', labelleft='off',
-                                        labelbottom='off')
-                        #ax.set_frame_on(False)
-                        # ax.spines['left'].set_visible(True)
-                        # ax.spines['right'].set_visible(True)
-                        ax.spines['top'].set_visible(True)
-                        ax.spines['bottom'].set_visible(False)
-                        ax.spines['left'].set_visible(True)
-                        ax.spines['right'].set_visible(True)
-                        #sns.despine(left=True, right=True, bottom=False, top=True)
-                        #ax.axis("off")
-                    elif np.mod(irow,len(exppairs)) == 2:
-                        ax.tick_params(top='off', bottom='on', left='off',
-                                        right='off', labelleft='off',
-                                        labelbottom='off')
-                        #ax.set_frame_on(False)
-                        # ax.spines['left'].set_visible(True)
-                        # ax.spines['right'].set_visible(True)
-                        ax.spines['top'].set_visible(False)
-                        ax.spines['bottom'].set_visible(True)
-                        ax.spines['left'].set_visible(True)
-                        ax.spines['right'].set_visible(True)
-                        #sns.despine(left=True, right=True, bottom=False, top=True)
-                        #ax.axis("off")
-                    else:
-                        ax.tick_params(top='off', bottom='off', left='off',
-                                        right='off', labelleft='off',
-                                        labelbottom='off')
-                        #ax.set_frame_on(False)
-                        #ax.spines['left'].set_visible(True)
-                        #ax.spines['right'].set_visible(True)
-                        ax.spines['top'].set_visible(False)
-                        ax.spines['bottom'].set_visible(False)
-                        ax.spines['left'].set_visible(True)
-                        ax.spines['right'].set_visible(True)
-                        #ax.axis("off")
-                    icol +=1
-                irow +=1
-                icol=0
+                for exppairname,exppair in exppairs.items():
+                    for varkey in varkeys:
+                        varkey_full = 'd'+varkey+'dt ['+units[varkey]+'/h]'
+                        ax = axes[irow,icol]
+
+                     #   axes[i] = fig.add_subplot(len(varkeys)*len(koeppenlookuptable),len(exppairs),icolor)
+                #sns.violinplot(x='KGC',y=varkey_full,data=data_all,hue='source',linewidth=2.,palette="muted",split=True,inner='quart') #,label=key+", R = "+str(round(PR[0],3)),data=data)       
+                
+                #ax.set_title(input_key_full)
+                        current_data = data_all[(data_all['exppair'] == exppairname) & (data_all['KGCname']  == koeppen.KGCID)]
+                        sns.violinplot(y='exppair', x=varkey_full,
+                                            hue="expname",split=True,
+                                 palette=exppalettes[exppairname],
+                                # palette=["m", "g",'r','b'],
+                                 linewidth=1.0,inner='quart',
+                                            data=current_data,sym='',legend=False,ax=ax)
+                        ax.legend("")
+                        ax.legend_.draw_frame(False)
+                        ax.set_yticks([])
+                        ax.set_ylabel("")
+
+                        # if varkey == 'q':
+                        #     ticks = ticker.FuncFormatter(lambda x, pos:
+                        #                                  '{0:g}'.format(x*1000.))
+                        #     ax.xaxis.set_major_formatter(ticks)
+
+                        if varkey == 'q':
+                            title_final = r'$dq/dt$'
+                            xlabel_final = r'[$\mathrm{g\, kg^{-1}\, h^{-1}}$]'
+                        elif varkey == 'theta':
+                            title_final = r'$d\theta/dt$'
+                            xlabel_final = r'[$\mathrm{K\, h^{-1}}$]'
+                        elif varkey == 'h':
+                            title_final = r'$dh/dt$'
+                            xlabel_final = r'[$\mathrm{m\, h^{-1}}$]'
+
+
+                        ax.set_xlabel("")
+                        #sns.despine(left=True, right=True, bottom=False, top=False)
+                        if irow == (len(varkeys)*len(koeppenlookuptable)-1):
+                            #ax.set_frame_on(False)
+
+                            ax.set_xlabel(xlabel_final)
+                            ax.tick_params(top='off', bottom='on', left='off',
+                                            right='off', labelleft='off',
+                                            labeltop='off',
+                                            labelbottom='on'
+                                          )
+                            ax.spines['top'].set_visible(False)
+                            ax.spines['bottom'].set_visible(True)
+                            ax.spines['left'].set_visible(True)
+                            ax.spines['right'].set_visible(True)
+                            #sns.despine(left=True, right=True, bottom=True, top=False)
+                        elif irow == 0:
+                            ax.set_title(title_final,fontsize=17.)
+                            ax.tick_params(top='off', bottom='off', left='off',
+                                            right='off', labelleft='off',
+                                            labelbottom='off')
+                            #ax.set_frame_on(False)
+                            # ax.spines['left'].set_visible(True)
+                            # ax.spines['right'].set_visible(True)
+                            ax.spines['top'].set_visible(True)
+                            ax.spines['bottom'].set_visible(False)
+                            ax.spines['left'].set_visible(True)
+                            ax.spines['right'].set_visible(True)
+                            #sns.despine(left=True, right=True, bottom=False, top=True)
+                            #ax.axis("off")
+                        elif np.mod(irow,len(exppairs)) == 0:
+                            ax.tick_params(top='off', bottom='off', left='off',
+                                            right='off', labelleft='off',
+                                            labelbottom='off')
+                            #ax.set_frame_on(False)
+                            # ax.spines['left'].set_visible(True)
+                            # ax.spines['right'].set_visible(True)
+                            ax.spines['top'].set_visible(True)
+                            ax.spines['bottom'].set_visible(False)
+                            ax.spines['left'].set_visible(True)
+                            ax.spines['right'].set_visible(True)
+                            #sns.despine(left=True, right=True, bottom=False, top=True)
+                            #ax.axis("off")
+                        elif np.mod(irow,len(exppairs)) == 2:
+                            ax.tick_params(top='off', bottom='on', left='off',
+                                            right='off', labelleft='off',
+                                            labelbottom='off')
+                            #ax.set_frame_on(False)
+                            # ax.spines['left'].set_visible(True)
+                            # ax.spines['right'].set_visible(True)
+                            ax.spines['top'].set_visible(False)
+                            ax.spines['bottom'].set_visible(True)
+                            ax.spines['left'].set_visible(True)
+                            ax.spines['right'].set_visible(True)
+                            #sns.despine(left=True, right=True, bottom=False, top=True)
+                            #ax.axis("off")
+                        else:
+                            ax.tick_params(top='off', bottom='off', left='off',
+                                            right='off', labelleft='off',
+                                            labelbottom='off')
+                            #ax.set_frame_on(False)
+                            #ax.spines['left'].set_visible(True)
+                            #ax.spines['right'].set_visible(True)
+                            ax.spines['top'].set_visible(False)
+                            ax.spines['bottom'].set_visible(False)
+                            ax.spines['left'].set_visible(True)
+                            ax.spines['right'].set_visible(True)
+                            #ax.axis("off")
+                        icol +=1
+                    irow +=1
+                    icol=0
 
         idx = 0
         for ikoeppen,koeppen in koeppenlookuptable.iterrows():
diff --git a/class4gl/interface/world_histogram.py b/class4gl/interface/world_histogram.py
index 2b53ab2..b1fcec6 100644
--- a/class4gl/interface/world_histogram.py
+++ b/class4gl/interface/world_histogram.py
@@ -139,26 +139,229 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 
 
+
 import matplotlib
 import matplotlib.pyplot as plt
+from matplotlib import rc
 import numpy as np
+import seaborn as sns
+# activate latex text rendering
+sns.reset_orig()
+
+data = [220,14.2,150,400,420,100,150,30,60,20,500,]
+error = [10, 1, 20, 60, 10,10, 1, 20, 60, 10, 5, ]
+
+ini = c4gldata['GLOBAL_ADV'].frames['stats']['records_all_stations_ini'].set_index(['STNID','dates'])
+vals = c4gldata['GLOBAL_ADV'].frames['stats']['records_all_stations_mod']
+stats = c4gldata['GLOBAL_ADV'].frames['stats']['records_all_stations_mod_stats']
+
+ini_fc = c4gldata['GLOBAL_ADV_FC'].frames['stats']['records_all_stations_ini'].set_index(['STNID','dates'])
+vals_fc = c4gldata['GLOBAL_ADV_FC'].frames['stats']['records_all_stations_mod']
+stats_fc = c4gldata['GLOBAL_ADV_FC'].frames['stats']['records_all_stations_mod']
+
+vals_obs = c4gldata['GLOBAL_ADV'].frames['stats']['records_all_stations_obs_afternoon']
+stats_obs = c4gldata['GLOBAL_ADV'].frames['stats']['records_all_stations_obs_afternoon']
+
+ini_common_index = ini.index.intersection(ini_fc.index)
+
+vals.index = ini.index
+vals = vals.loc[ini_common_index]
+
+stats.index = ini.index
+stats = stats.loc[ini_common_index]
+
+
+vals_obs.index = ini.index
+vals_obs = vals_obs.loc[ini_common_index]
+
+stats_obs.index = ini.index
+stats_obs = stats_obs.loc[ini_common_index]
+
+
+ini = ini.loc[ini_common_index]
+
 
-data = [220,14.2,150,400,420]
-error = [10, 1, 20, 60, 10]
-x = [i + .5 for i in range(5)]
-
-fig, ax = plt.subplots()
-bar = ax.barh(x, data,0.1 +np.arange(len(data))*0.9/len(data), align="center", yerr=error)
-plot = ax.plot(x, data)
-ax.set_xticks(x)
-ax.set_xticklabels(('wt', 'N23PP', 'N23PP/PEKN', 'PEKN', 'N23PP/PEKN/L28F'))
-ax.set_title(r"Everything in the document can use m$\alpha$th language",
-             y=1.05)
-ax.set_ylabel(r"Rate (s$^{-1}$)", labelpad=10)
-ax.set_xlabel("Mutant",labelpad=10)
-ax.yaxis.set_ticks_position('left')
-ax.xaxis.set_ticks_position('bottom')
-plt.savefig('test.png')
-plt.show()
+vals_fc.index = ini_fc.index
+vals_fc = vals_fc.loc[ini_common_index]
+
+stats_fc.index = ini_fc.index
+stats_fc = stats_fc.loc[ini_common_index]
+
+ini_fc = ini_fc.loc[ini_common_index]
+
+dlat = 10
+blat = np.arange(-55.,60.,dlat)[[2,3,4,7,8,9,10,11]]
+lats = (blat[1:] + blat[:-1])/2.
+
+fig = plt.figure(figsize=(10,6)) 
+variables = ['h','theta',r'q']
+labels = [r'$h$',r'$\theta$',r'$q$']
+units = ['m','K','g/kg']
+xlims = [(-10,3000),(280,310.),(2.5,17.5)]
+#var = "theta"
+for ivar,var in enumerate(variables):
+    ax = fig.add_subplot(1,len(variables),ivar+1,)
+    data = []
+    data_025 = []
+    data_075 = []
+    
+    data_fc = []
+    data_fc_025 = []
+    data_fc_075 = []
+    
+    data_obs = []
+    data_obs_025 = []
+    data_obs_075 = []
+    
+    data_ini = []
+    data_ini_025 = []
+    data_ini_075 = []
+    
+    lnts = []
+    
+    for ilat,lat in enumerate(lats):
+        print(ilat,lat)
+    # 
+        query = 'latitude >= '+str(blat[ilat])+' and '+ 'latitude < '+str(blat[ilat+1])
+        print(query)
+        select = ini.query(query)
+        if len(select) >= 7:
+            lnts.append(len(select))
+            #print(stats.iloc[select.index])
+            print(stats.loc[select.index])
+            data.append(vals.loc[select.index][var].mean()) 
+            data_025.append(vals.loc[select.index][var].quantile(0.25)) 
+            data_075.append(vals.loc[select.index][var].quantile(0.75)) 
+            data_fc.append(vals_fc.loc[select.index][var].mean()) 
+            data_fc_025.append(vals_fc.loc[select.index][var].quantile(0.25)) 
+            data_fc_075.append(vals_fc.loc[select.index][var].quantile(0.75)) 
+    
+            data_obs.append(vals_obs.loc[select.index][var].mean()) 
+            data_obs_025.append(vals_obs.loc[select.index][var].quantile(0.25)) 
+            data_obs_075.append(vals_obs.loc[select.index][var].quantile(0.75)) 
+    
+            data_ini.append(ini.loc[select.index][var].mean()) 
+            data_ini_025.append(ini.loc[select.index][var].quantile(0.25)) 
+            data_ini_075.append(ini.loc[select.index][var].quantile(0.75)) 
+        else:
+            lnts.append(0)
+            data.append(np.nan)
+            data_025.append(np.nan)
+            data_075.append(np.nan)
+            data_fc.append(np.nan)
+            data_fc_025.append(np.nan)
+            data_fc_075.append(np.nan)
+    
+            data_obs.append(np.nan)
+            data_obs_025.append(np.nan)
+            data_obs_075.append(np.nan)
+    
+    
+            data_ini.append(np.nan)
+            data_ini_025.append(np.nan)
+            data_ini_075.append(np.nan)
+    
+    data = np.array(data)
+    data_025 = np.array(data_025)
+    data_075 = np.array(data_075)
+    
+    data_fc = np.array(data_fc)
+    data_fc_025 = np.array(data_fc_025)
+    data_fc_075 = np.array(data_fc_075)
+    
+    
+    data_obs = np.array(data_obs)
+    data_obs_025 = np.array(data_obs_025)
+    data_obs_075 = np.array(data_obs_075)
+    
+    data_ini = np.array(data_ini)
+    data_ini_025 = np.array(data_ini_025)
+    data_ini_075 = np.array(data_ini_075)
+
+    if var == 'q':
+        data = data*1000.
+        data_025 = data_025*1000.
+        data_075 = data_075*1000.
+
+        data_fc = data_fc*1000.
+        data_fc_025 = data_fc_025*1000.
+        data_fc_075 = data_fc_075*1000.
+    
+        data_obs = data_obs*1000.
+        data_obs_025 = data_obs_025*1000.
+        data_obs_075 = data_obs_075*1000.
+
+        data_ini = data_ini*1000.
+        data_ini_025 = data_ini_025*1000.
+        data_ini_075 = data_ini_075*1000.
+    
+
+    
+
+    data_left = np.zeros_like(data)
+    data_right = np.zeros_like(data)
+    select = (data >= data_ini)
+    data_left[select] = data[select]
+    data_right[select] = data_ini[select]
+    data_left[~select] = data_ini[~select]
+    data_right[~select] = data[~select]
+
+    data_diff_left = np.zeros_like(data)
+    data_diff_right = np.zeros_like(data)
+    select = (data_fc >= data)
+    data_diff_left[select] = data[select]
+    data_diff_right[select] = data_fc[select]
+    data_diff_left[~select] = data_fc[~select]
+    data_diff_right[~select] = data[~select]
+
+
+    
+    
+    #bar = ax.barh(lats, data,blat[1:] - blat[:-1] , align="center", xerr=error)
+    
+    
+    erb = ax.errorbar( data_ini, lats+2.*dlat/8.,xerr=[data_ini-data_ini_025,data_ini_075-data_ini], fmt='s', color='darkgrey',mfc='white', ms=3, mew=1)
+    erb = ax.errorbar( data_obs, lats+2.*dlat/8.,xerr=[data_obs-data_obs_025,data_obs_075-data_obs], fmt='s', color='darkgrey',mfc='black', ms=3, mew=1)
+    erb = ax.errorbar( data,    lats-1.*dlat/8,xerr=[data-data_025,data_075-data], fmt='s', color='black',mfc='black', ms=3, mew=1)
+    er2 = ax.errorbar( data_fc, lats-2*dlat/8.,xerr=[data_fc-data_fc_025,data_fc_075-data_fc], fmt='s', color='blue',mfc='blue', ms=3, mew=1)
+    
+    ba2 = ax.barh(lats, data_diff_right - data_diff_left  ,(blat[1:] -
+                                                            blat[:-1])*0.85 ,
+                  align="center", left=data_diff_left,color='red',
+                  edgecolor='lightgrey',linewidth=2.)
+    bar = ax.barh(lats, data_right - data_left ,(blat[1:] - blat[:-1])*0.85 ,
+                  align="center", left=data_left, color='none',edgecolor='black',linewidth=2.)
+    # ax.set_yticks(blat)
+    # labels = [ w.get_text() for w in ax.get_yticklabels()]
+    # ax.set_yticklabels(labels)
+    # ax.set_yticks(blat)
+    ax.set_yticks([["test",-30.]])
+    if ivar == 0:
+        plt.legend([r"morning observations",\
+                    r"afternoon observations",\
+                    r"afternoon control",\
+                    r"afternoon $\it{wet}$ "])
+    
+    #er2 = ax.errorbar( data_fc, lats,xerr=[data_fc-data_fc_025,data_fc_075-data_fc], fmt='s', mfc='blue', ms=3, mew=1)
+    
+    
+    
+    # plot = ax.plot(x, data)
+    ax.set_yticks(lats)
+    ax.set_xlim(xlims[ivar])
+    ax.set_ylim((-65.,65.))
+    #ax.set_yticklabels(('wt', 'N23PP', 'N23PP/PEKN', 'PEKN', 'N23PP/PEKN/L28F'))
+    #ax.set_title(r"Everything in the document can use m$\alpha$th language", y=1.05)
+    ax.set_title(labels[ivar],fontsize=17.)
+    ax.set_xlabel("["+units[ivar]+"]", labelpad=10,fontsize=15.)
+    if ivar == 0:
+        ax.set_ylabel("Latitude [°]",labelpad=10,fontsize=15.)
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+    ax.tick_params(labelsize=15.)
+fig.tight_layout()
+fig.subplots_adjust(left=0.10,bottom=0.13,right=0.98,top=0.94,wspace=0.24,hspace=0.20)
+fig.savefig('test.png',dpi=200)
+fig.show()
 
 
diff --git a/class4gl/setup/batch_setup_era.pbs b/class4gl/setup/batch_setup_era.pbs
index 1ee9b9b..18a6018 100644
--- a/class4gl/setup/batch_setup_era.pbs
+++ b/class4gl/setup/batch_setup_era.pbs
@@ -7,7 +7,11 @@
 #PBS -m a
 #PBS -N c4gl_setup
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+module purge
+source ~/.bashrc
+
+echo loading modules: $LOADDEPSCLASS4GL 
+$LOADDEPSCLASS4GL 
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 
diff --git a/class4gl/setup/batch_update_input.py b/class4gl/setup/batch_update_input.py
index d2b06b0..355f532 100644
--- a/class4gl/setup/batch_update_input.py
+++ b/class4gl/setup/batch_update_input.py
@@ -20,6 +20,7 @@
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling')
 parser.add_argument('--updates')
+parser.add_argument('--global_vars')
 parser.add_argument('--subset_input',default='morning') 
                                         # this tells which yaml subset
                                         # to initialize with.
diff --git a/class4gl/setup/update_input.py b/class4gl/setup/update_input.py
index 07000f2..635e1c0 100644
--- a/class4gl/setup/update_input.py
+++ b/class4gl/setup/update_input.py
@@ -19,6 +19,7 @@
 parser.add_argument('--first_station_row')
 parser.add_argument('--last_station_row')
 parser.add_argument('--updates')
+parser.add_argument('--global_vars')
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling',default='dump_on_success')
 parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
@@ -51,7 +52,7 @@
 
 # iniitialize global data
 globaldata = data_global()
-if 'era_profiles' in args.updates.strip().split(","):
+if (args.updates is not None) and ('era_profiles' in args.updates.strip().split(",")):
     globaldata.sources = {**globaldata.sources,**{
             "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
             "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
@@ -230,6 +231,9 @@
                                                 record_input.index_start, 
                                                 record_input.index_end,
                                                 mode='ini')
+                if args.global_vars is not None:
+                    c4gli_output.get_global_input(globaldata,only_keys=args.global_vars.strip().split(','))
+
                 if args.diag_tropo is not None:
                     print('add tropospheric parameters on advection and subsidence (for diagnosis)')
                     seltropo = (c4gli_output.air_ac.p > c4gli_output.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
@@ -242,7 +246,7 @@
                             print("warning: tropospheric variable "+var+" not recognized")
 
 
-                if 'era_profiles' in args.updates.strip().split(","):
+                if (args.updates is not None) and ('era_profiles' in args.updates.strip().split(",")):
                     c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
 
                     c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index a8da5e2..4f12ccc 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -7,11 +7,10 @@
 #PBS -m a
 #PBS -N c4gl_sim
 
-#module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
 module purge
 source ~/.bashrc
 
-echo $LOADDEPSCLASS4GL 
+echo loading modules: $LOADDEPSCLASS4GL 
 $LOADDEPSCLASS4GL 
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 3008f65..20fc34f 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -69,7 +69,7 @@
   'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
+    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
@@ -277,9 +277,9 @@
                     c4gli_morning.update(source='pairs',pars={'runtime' : \
                                         runtime})
                     c4gli_morning.update(source=expname, pars=exp)
-                    if exp[-3:] == 'SM2':
-                        c4gli_morning.update(source=expname, pars={'wg': c4gli_morning.pars.wg - (c4gli_morning.pars.wg - c4gli_morning.pars.wwilt)/2.)
-                        c4gli_morning.update(source=expname, pars={'w2': c4gli_morning.pars.w2 - (c4gli_morning.pars.w2 - c4gli_morning.pars.wwilt)/2.)
+                    if expname[-3:] == 'SM2':
+                        c4gli_morning.update(source=expname, pars={'wg': c4gli_morning.pars.wg - (c4gli_morning.pars.wg - c4gli_morning.pars.wwilt)/2.})
+                        c4gli_morning.update(source=expname, pars={'w2': c4gli_morning.pars.w2 - (c4gli_morning.pars.w2 - c4gli_morning.pars.wwilt)/2.})
 
                     c4gl = class4gl(c4gli_morning)
 
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 08d28b8..5c1bd2b 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -66,6 +66,7 @@
   'AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    'GLOBAL_ADV_SHR_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
   'GLOBAL_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
   'IOPS_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
diff --git a/class4gl/simulations/simulations_veg.py b/class4gl/simulations/simulations_veg.py
index b2f7ad8..6dbcdac 100644
--- a/class4gl/simulations/simulations_veg.py
+++ b/class4gl/simulations/simulations_veg.py
@@ -66,9 +66,9 @@
   'GLOBAL_ADV_VMIN':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_VMAX':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_V0':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_L025':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_L001':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_ADV_L100':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_L600':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_L099':    {'sw_ac' : ['adv'],'sw_ap': True,'sw_lit': False},
   'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
   'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
 }
@@ -290,26 +290,38 @@
                                              'z0h': 0.001,
                                              }\
                                             )
-                    if expname[-3:] == '_VMIN':
+                    if expname[-5:] == '_VMIN':
                         c4gli_morning.update(source=expname, pars=\
-                                             {'alpha': 0.2868081648656875,
-                                             'cveg': 0.08275966502449594,
-                                             'z0m': 0.05760000169277192,
-                                             'z0h': 0.005760000169277192,
-                                             'LAI': 2.0,
+                                             {'alpha': 0.2868081648656875,\
+                                             'cveg': 0.08275966502449594,\
+                                             'z0m': 0.05760000169277192,\
+                                             'z0h': 0.005760000169277192,\
+                                             'LAI': 2.0,\
                                              }\
                                             )
-                    if expname[-3:] == '_L025':
-                        c4gli_morning.update(source=expname, pars=\
-                                             {'lai':.25}\
-                                            )
-                    if expname[-3:] == '_L100':
+                    if expname[-5:] == '_L001':
+                        #c4gldata['GLOBAL_ADV_SHR'].frames['stats']['records_all_stations_ini'].LAIpixel.quantile(0.01)
+
                         c4gli_morning.update(source=expname, pars=\
-                                             {'lai':1.0}\
+                                             {'LAI':0.111/0.325,\
+                                              'cveg':0.325,\
+                                              'z0m':0.17425,\
+                                              'z0h':0.017425,\
+                                              'alpha':0.270,\
+                                             }\
                                             )
-                    if expname[-3:] == '_L600':
+                    if expname[-5:] == '_L099':
+                        # select = c4gldata['GLOBAL_ADV_SHR'].frames['stats']['records_all_stations_ini'].LAIpixel >= 4.38
+                        # np.mean(c4gldata['GLOBAL_ADV_SHR'].frames['stats']['records_all_stations_ini'][select].LAIpixel)
+                        # np.mean(c4gldata['GLOBAL_ADV_SHR'].frames['stats']['records_all_stations_ini'][select].LAIpixel)
+                        # np.mean(c4gldata['GLOBAL_ADV_SHR'].frames['stats']['records_all_stations_ini'][select].LAIpixel)
                         c4gli_morning.update(source=expname, pars=\
-                                             {'lai':6.0}\
+                                             {'LAI':4.605/0.807,\
+                                              'cveg':0.807,\
+                                              'z0m':1.78,\
+                                              'z0h':0.178,\
+                                              'alpha':0.195,\
+                                             }\
                                             )
 
                     c4gl = class4gl(c4gli_morning)

From e0b41c3557240adff0b246876d6d7c804f7de52c Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 22 Nov 2018 09:32:58 +0100
Subject: [PATCH 107/129] make batch pbs work with arbitrary modules

---
 class4gl/model.py |  12 +++++++++++-
 test.png          | Bin 0 -> 92416 bytes
 2 files changed, 11 insertions(+), 1 deletion(-)
 create mode 100644 test.png

diff --git a/class4gl/model.py b/class4gl/model.py
index 28fe5d0..d3b1fdb 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -1775,6 +1775,11 @@ def store(self):
         self.out.wthetav[t]    = self.wthetav
         self.out.wthetae[t]    = self.wthetae
         self.out.wthetave[t]   = self.wthetave
+
+        self.out.advtheta[t]   = self.advtheta
+        self.out.advu[t]       = self.advu
+        self.out.advv[t]       = self.advv
+        self.out.advq[t]       = self.advq
         
         self.out.q[t]          = self.q
         self.out.dq[t]         = self.dq
@@ -1875,7 +1880,7 @@ def exitmodel(self):
         del(self.theta)
         del(self.dtheta)
         del(self.gammatheta)
-        del(self.advtheta)
+        #del(self.advtheta)
         del(self.beta)
         del(self.wtheta)
     
@@ -2019,6 +2024,11 @@ def __init__(self, tsteps):
         self.wthetav    = np.zeros(tsteps)    # surface kinematic virtual heat flux [K m s-1]
         self.wthetae    = np.zeros(tsteps)    # entrainment kinematic heat flux [K m s-1]
         self.wthetave   = np.zeros(tsteps)    # entrainment kinematic virtual heat flux [K m s-1]
+
+        self.advtheta   = np.zeros(tsteps)
+        self.advu       = np.zeros(tsteps)
+        self.advv       = np.zeros(tsteps)
+        self.advq       = np.zeros(tsteps)
         
         self.q          = np.zeros(tsteps)    # mixed-layer specific humidity [kg kg-1]
         self.dq         = np.zeros(tsteps)    # initial specific humidity jump at h [kg kg-1]
diff --git a/test.png b/test.png
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From b9f2e3c46e4bbf7f3d5d43cc3ea5544fe2ad7eda Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 12 Jan 2019 14:09:42 +0100
Subject: [PATCH 108/129] add MANIFEST.in to include everything in pypi
 distribution

---
 MANIFEST                                      |  49 +-
 MANIFEST.in                                   |   3 +
 class4gl/interface/interface_new_koeppen.py   |   3 +-
 class4gl/interface/interface_show_profiles.py | 569 +++++++++++++
 class4gl/interface_multi.py                   |   4 +-
 class4gl/model.py                             |   2 +-
 class4gl/setup/batch_setup_igra.pbs           |   6 +-
 class4gl/setup/setup_bllast_noon.py           | 719 +++++++++++++++++
 class4gl/setup/setup_goamazon_noon.py         | 755 ++++++++++++++++++
 class4gl/setup/setup_humppa_noon.py           | 733 +++++++++++++++++
 class4gl/setup/setup_igra_20181217.py         | 361 +++++++++
 setup.cfg                                     |   4 +
 setup.py                                      |  32 +-
 13 files changed, 3228 insertions(+), 12 deletions(-)
 create mode 100644 MANIFEST.in
 create mode 100644 class4gl/interface/interface_show_profiles.py
 create mode 100644 class4gl/setup/setup_bllast_noon.py
 create mode 100644 class4gl/setup/setup_goamazon_noon.py
 create mode 100644 class4gl/setup/setup_humppa_noon.py
 create mode 100644 class4gl/setup/setup_igra_20181217.py
 create mode 100644 setup.cfg

diff --git a/MANIFEST b/MANIFEST
index 1dde1bb..c4510cb 100644
--- a/MANIFEST
+++ b/MANIFEST
@@ -1,10 +1,55 @@
 # file GENERATED by distutils, do NOT edit
+setup.cfg
 setup.py
-bin/__init__.py
 class4gl/__init__.py
 class4gl/class4gl.py
-class4gl/data_air.py
 class4gl/data_global.py
+class4gl/data_soundings.py
+class4gl/era_advection.py
 class4gl/interface_functions.py
 class4gl/interface_multi.py
 class4gl/model.py
+class4gl/interface/interface.py
+class4gl/interface/interface_cloudiness.py
+class4gl/interface/interface_koeppen.py
+class4gl/interface/interface_new_koeppen.py
+class4gl/interface/interface_show_profiles.py
+class4gl/interface/interface_stations.py
+class4gl/interface/taylorDiagram.py
+class4gl/interface/test_histogram.py
+class4gl/interface/world_histogram.py
+class4gl/processing/batch_update_output.py
+class4gl/processing/update_output.py
+class4gl/ribtol/__init__.py
+class4gl/ribtol/ribtol_hw.py
+class4gl/ribtol/setup.py
+class4gl/setup/batch_setup_era.py
+class4gl/setup/batch_setup_global_old.py
+class4gl/setup/batch_setup_igra.py
+class4gl/setup/batch_update.py
+class4gl/setup/batch_update_input.py
+class4gl/setup/setup_bllast.py
+class4gl/setup/setup_bllast_noon.py
+class4gl/setup/setup_era.py
+class4gl/setup/setup_global_afternoon.py
+class4gl/setup/setup_goamazon.py
+class4gl/setup/setup_goamazon_noon.py
+class4gl/setup/setup_humppa.py
+class4gl/setup/setup_humppa_noon.py
+class4gl/setup/setup_igra.py
+class4gl/setup/setup_igra_20181217.py
+class4gl/setup/setup_igra_pkl.py
+class4gl/setup/update_input.py
+class4gl/setup/update_setup.py
+class4gl/setup/trash/setup_global_old.py
+class4gl/simulations/batch_simulations.py
+class4gl/simulations/copy_update.py
+class4gl/simulations/runmodel.py
+class4gl/simulations/simulations.py
+class4gl/simulations/simulations_iter.py
+class4gl/simulations/simulations_iter_bowen.py
+class4gl/simulations/simulations_iter_test.py
+class4gl/simulations/simulations_smchange2.py
+class4gl/simulations/simulations_veg.py
+class4gl/simulations/simulations_wwilt_wfc.py
+class4gl/simulations/trash/run_test.py
diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..174aa24
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,3 @@
+recursive-include class4gl *.py
+
+
diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index e5d7161..f9a2a3e 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -1,4 +1,3 @@
-'''
 import numpy as np
 import pandas as pd
 import sys
@@ -122,7 +121,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                       obs_filter = (args.obs_filter == 'True')
                                             
                     )
-                    '''
+
 sns.reset_orig()
 
 
diff --git a/class4gl/interface/interface_show_profiles.py b/class4gl/interface/interface_show_profiles.py
new file mode 100644
index 0000000..734e8b9
--- /dev/null
+++ b/class4gl/interface/interface_show_profiles.py
@@ -0,0 +1,569 @@
+'''
+import numpy as np
+
+import pandas as pd
+import sys
+
+import matplotlib
+matplotlib.use('TkAgg')
+
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--experiments')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--load_globaldata',default=False) # load the data needed for the interface
+parser.add_argument('--make_figures',default=None)
+parser.add_argument('--figure_filename',default=None)
+parser.add_argument('--tendencies_revised',default=False)
+parser.add_argument('--obs_filter',default="True")
+args = parser.parse_args()
+
+print('Adding python library:',args.c4gl_path_lib)
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import c4gl_interface_soundings,get_record_yaml
+from class4gl import class4gl_input, data_global,class4gl,units
+#from sklearn.metrics import mean_squared_error
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+#import seaborn.apionly as sns
+import pylab as pl
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import kde
+from scipy.stats import pearsonr                                                
+from taylorDiagram import TaylorDiagram
+from matplotlib import ticker
+# import importlib
+# importlib.reload(mpl); importlib.reload(plt); importlib.reload(sns)
+
+
+
+
+
+latex = {}
+latex['dthetadt'] =  r'$d \theta / dt $'
+latex['dqdt'] =      r'$d q / dt $'
+latex['dhdt'] =      r'$d h / dt $'
+
+def abline(slope, intercept,axis):
+    """Plot a line from slope and intercept"""
+    #axis = plt.gca()
+    x_vals = np.array(axis.get_xlim())
+    y_vals = intercept + slope * x_vals
+    axis.plot(x_vals, y_vals, 'k--')
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+
+
+
+# EXPS  =\
+# {
+# 'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'GLOBAL_ITER_ADV':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# #'IOPS_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+# # 'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+# }
+
+if args.load_globaldata:
+    # iniitialize global data
+    globaldata = data_global()
+    # ...  and load initial data pages
+    globaldata.load_datasets(recalc=0)
+else:
+    globaldata = None
+
+c4gldata = []
+    
+c4gldata.append(c4gl_interface_soundings( \
+                  '/data/gent/vo/000/gvo00090/D2D/data/C4GL/20181030/IOPS_ADV_ITER/',\
+                  '/data/gent/vo/000/gvo00090/D2D/data/SOUNDINGS/IOPS/',\
+                  globaldata,\
+                  refetch_records=False,\
+                  obs_filter = True,\
+                  tendencies_revised = args.tendencies_revised\
+                ))
+c4gldata.append(c4gl_interface_soundings( \
+                  '/data/gent/vo/000/gvo00090/D2D/data/C4GL/20181017_NOON/IOPS_ADV_ITER/',\
+                  '/data/gent/vo/000/gvo00090/D2D/data/SOUNDINGS/IOPS_NOON/',\
+                  globaldata,\
+                  refetch_records=False,
+                  obs_filter = False,
+                  tendencies_revised = args.tendencies_revised
+                ))
+
+
+profiles_morning            = []
+profiles_morning_raw        = []
+profiles_noon_obs           = []
+profiles_noon_obs_raw       = []
+profiles_noon_mod           = []
+profiles_afternoon_obs      = []
+profiles_afternoon_obs_raw  = []
+profiles_afternoon_mod      = []
+profiles_location      = []
+profiles_morning_datetime      = []
+profiles_noon_datetime      = []
+profiles_afternoon_datetime      = []
+
+for i in range(15):
+
+    if i == 0:
+        location = 'HUMPPA'
+        c4gldata[0].sel_station(90000)
+        c4gldata[1].sel_station(90000)
+    elif i == 7:
+        location = 'BLLAST'
+        c4gldata[0].sel_station(90001)
+        c4gldata[1].sel_station(90001)
+    elif i == 11:
+        location = 'GOAMAZON'
+        c4gldata[0].sel_station(90002)
+        c4gldata[1].sel_station(90002)
+
+    # if i == 12:
+    #     c4gldata[1].next_record()
+    #     c4gldata[1].next_record()
+
+        
+    profiles_location.append(str(location))
+    profiles_morning_datetime.append(c4gldata[0].frames['profiles']['record_yaml_ini'].pars.ldatetime)
+    profiles_morning.append( c4gldata[0].frames['profiles']['record_yaml_ini'].air_ap)
+    profiles_morning_raw.append( c4gldata[0].frames['profiles']['record_yaml_ini'].air_balloon)
+
+    profiles_noon_datetime.append(c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime)
+
+    if i in [12,13]:
+        profiles_noon_datetime.append(c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime)
+        profiles_noon_obs.append(c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].air_ap)
+        profiles_noon_obs_raw.append(c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].air_balloon)
+        profiles_noon_mod.append(c4gldata[1].frames['profiles']['record_yaml_mod'].air_ap)
+        profiles_noon_obs[-1].theta = np.nan
+        profiles_noon_obs_raw[-1].theta = np.nan
+        profiles_noon_mod[-1].theta = np.nan
+        profiles_noon_obs[-1].q = np.nan
+        profiles_noon_obs_raw[-1].q = np.nan
+        profiles_noon_mod[-1].q = np.nan
+
+    else:
+        profiles_noon_obs.append( c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].air_ap)
+        profiles_noon_obs_raw.append( c4gldata[1].frames['profiles']['record_yaml_obs_afternoon'].air_balloon)
+        profiles_noon_mod.append( c4gldata[1].frames['profiles']['record_yaml_mod'].air_ap)
+
+    profiles_afternoon_datetime.append(c4gldata[0].frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime)
+    profiles_afternoon_obs.append( c4gldata[0].frames['profiles']['record_yaml_obs_afternoon'].air_ap)
+    profiles_afternoon_obs_raw.append(c4gldata[0].frames['profiles']['record_yaml_obs_afternoon'].air_balloon)
+    profiles_afternoon_mod.append( c4gldata[0].frames['profiles']['record_yaml_mod'].air_ap)
+
+    if i < 14:
+        c4gldata[0].next_record()
+        if i not in [9,10]:
+            c4gldata[1].next_record()
+
+
+
+
+            #axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12.)                                     
+
+
+'''
+
+
+
+numprofiles = len(profiles_afternoon_mod)
+
+
+per_row = 4.
+VARS = ['theta','q']
+
+for var in VARS:
+    fig = plt.figure(figsize=(9,8.5))
+    for i in range(numprofiles):
+        ax = fig.add_subplot(np.ceil(numprofiles/per_row),per_row,i+1)
+    
+        if var == 'q':
+            fac = 1000.
+        else:
+            fac = 1.
+    
+        label = profiles_morning_datetime[i].strftime("%H:%m")+'ST'+' ini'
+        plt.plot(profiles_morning[i][var].values*fac,profiles_morning[i].z.values,'b--',label=label)
+        #plt.plot(profiles_morning_raw[i][var].values,profiles_morning_raw[i].z.values,'b*')
+        
+        label = profiles_noon_datetime[i].strftime("%H:%m")+'ST'+' obs'
+        #label = profiles_morning_datetime[i].hour()+':'+profiles_morning_datetime[i].minute()+'ST')+' ini')
+        plt.plot(profiles_noon_obs[i][var].values*fac,profiles_noon_obs[i].z.values,'g--',label=label)
+        #plt.plot(profiles_noon_obs_raw[i][var].values,profiles_noon_obs_raw[i].z.values,'g*')
+        label = profiles_noon_datetime[i].strftime("%H:%m")+'ST'+' mod'
+        plt.plot(profiles_noon_mod[i][var].values*fac,profiles_noon_mod[i].z.values,'g-',label=label)
+        
+        
+        label = profiles_afternoon_datetime[i].strftime("%H:%m")+'ST'+' obs'
+        plt.plot(profiles_afternoon_obs[i][var].values*fac, profiles_afternoon_obs[i].z.values,'r--',label=label)
+        #plt.plot(profiles_afternoon_obs_raw[i][var].values,profiles_afternoon_obs_raw[i].z.values,'r*')
+        label = profiles_afternoon_datetime[i].strftime("%H:%m")+'ST'+' mod'
+        plt.plot(profiles_afternoon_mod[i][var].values*fac, profiles_afternoon_mod[i].z.values,'r-',label=label)
+        if var == 'q':
+            if i >=(numprofiles - per_row):
+                ax.legend(loc=2,fontsize=6)
+            else:
+                ax.legend(loc=1,fontsize=6)
+        else:
+            ax.legend(loc=2,fontsize=6)
+    
+    
+        ax.set_ylim(0,3500)
+        if var == 'theta':
+             ax.set_xlim((283.,315.))
+        elif var == 'q':
+             ax.set_xlim((0.,21.))
+        ax.set_title(profiles_location[i]+' '+ str(profiles_morning_datetime[i].date()))
+    
+        if (np.mod(i,per_row) !=0):
+            ax.set_yticklabels([])
+        else:
+            ax.set_ylabel('$h$ [$\mathrm{m}$]')
+    
+        if i < (numprofiles - per_row):
+            ax.set_xticklabels([])
+        else:
+           if var == 'q':
+               units_final = r'$q$ [$\mathrm{g\, kg^{-1}}$]'
+           elif var == 'theta':
+               units_final = r'$\theta$ [$\mathrm{K}$]'
+           ax.set_xlabel(units_final)
+
+
+
+
+    #ax.tick_params(
+    #axis='x',          # changes apply to the x-axis
+    #which='both',      # both major and minor ticks are affected
+    #left=(np.mod(i,per_row) ==0),      # ticks along the bottom edge are off
+    #top=False,         # ticks along the top edge are off
+    #labelbottom= (i==np.ceil(numprofiles/per_row)) # labels along the bottom edge are off
+    #)
+
+    fig.tight_layout()
+    fig.subplots_adjust(wspace=0.05,right=0.99,hspace=0.25)
+    fig.show()
+
+
+
+# if bool(args.make_figures):
+#     fig = plt.figure(figsize=(10,7))   #width,height
+#     i = 1                                                                           
+#     axes = {}         
+#     axes_taylor = {}         
+#     
+#     #colors = ['r','g','b','m']
+#     colors = ['k']
+#     symbols = ['^','x','+']
+#     dias = {}
+#     
+#     for varkey in ['h','theta','q']:                                                    
+#         axes[varkey] = fig.add_subplot(2,3,i)                                       
+#         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
+#     
+#         #print(obs.std())
+#         obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+#         STD_OBS = obs.std()
+#         dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+#         dias[varkey]._ax.axis["left"].label.set_text(\
+#             "Normalized standard deviation")
+#         if i == 1:
+#             axes[varkey].annotate('Normalized standard deviation',\
+#                         xy= (0.05,0.36),
+#                         color='black',
+#                         rotation=90.,
+#                         xycoords='figure fraction',
+#                         weight='normal',
+#                         fontsize=10.,
+#                         horizontalalignment='center',
+#                         verticalalignment='center' ,
+#                         #bbox={'edgecolor':'black',
+#                         #      'boxstyle':'circle',
+#                         #      'fc':koeppen.color,
+#                         #      'alpha':1.0}
+#                        )
+#         # dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#         # dias[varkey]._ax.axis["left"].axis.set_major_locator(np.arange(0.,2.,0.25))
+#         #dias[varkey]._ax.axis["left"].axis.set_ticks(np.arange(0.,2.,0.25))
+#         # Q95 = obs.quantile(0.95)
+#         # Q95 = obs.quantile(0.90)
+#         # Add RMS contours, and label them
+#         contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
+#         dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+#         #dia._ax.set_title(season.capitalize())
+#     
+#         dias[varkey].add_grid()
+#     
+#     
+#         #dia.ax.plot(x99,y99,color='k')
+#     
+#         
+#         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+#             mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
+#             obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+#             x, y = obs.values,mod.values
+#             print(key,len(obs.values))
+#     
+#             #scores
+#             PR = pearsonr(mod,obs)[0]
+#             RMSE = rmse(obs,mod)                                               
+#             BIAS = np.mean(mod) - np.mean(obs)
+#             STD = mod.std()
+#             
+#             fit = np.polyfit(x,y,deg=1)
+# 
+#             if varkey == 'q':
+#                 axes[varkey].plot(x, fit[0] * x + fit[1],\
+#                                   color=colors[ikey],alpha=0.8,lw=2,\
+#                                   label=key+", "+\
+#                            'RMSE = '+format((RMSE*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+ '\n'+\
+#                            'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
+#                            r'$R$ = '+format(PR,'0.2f') )
+# 
+# 
+#             elif varkey == 'h':
+#                 axes[varkey].plot(x, fit[0] * x + fit[1],\
+#                                   color=colors[ikey],alpha=0.8,lw=2,\
+#                                   label=key+", "+\
+#                             'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+#                             'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+#                             r'$R$ = '+format(PR,'0.2f'))
+#             else: #theta
+#                 axes[varkey].plot(x, fit[0] * x + fit[1],\
+#                                   color=colors[ikey],alpha=0.8,lw=2,\
+#                                   label=key+", "+\
+#                             'RMSE = '+format(RMSE,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+#                             'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+#                             r'$R$ = '+format(PR,'0.2f'))
+# 
+#             if varkey == 'q':
+#                 annotate_text = \
+#                                'RMSE = '+format((RMSE*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+ '\n'+\
+#                                'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
+#                                r'$R$ = '+format(PR,'0.2f')
+#                 ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+#        horizontalalignment='right', verticalalignment='bottom' ,
+#         bbox={'edgecolor':'black',
+#                           'fc':'white',  
+#                               'boxstyle':'square',
+#                               'alpha':0.8}
+#                                        )
+#             elif varkey == 'h':
+#                 annotate_text = \
+#                                 'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+#                                 'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
+#                                 r'$R$ = '+format(PR,'0.2f')
+#                 ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
+#        horizontalalignment='left', verticalalignment='top' ,
+#         bbox={'edgecolor':'black',
+#                           'fc':'white',  
+#                               'boxstyle':'square',
+#                               'alpha':0.8}
+#                                        )
+#             else:
+#                 annotate_text = \
+#                                 'RMSE = '+format(RMSE,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+#                                 'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
+#                                 r'$R$ = '+format(PR,'0.2f')
+# 
+#                 ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
+#        horizontalalignment='left', verticalalignment='top' ,
+#         bbox={'edgecolor':'black',
+#                           'fc':'white',  
+#                               'boxstyle':'square',
+#                               'alpha':0.8}
+#                                        )
+# 
+# 
+# 
+# 
+#             
+#             # print(STD)
+#             # print(PR)
+#             dias[varkey].add_sample(STD/STD_OBS, PR,
+#                            marker='o', ms=5, ls='',
+#                            #mfc='k', mec='k', # B&W
+#                            mfc=colors[ikey], mec=colors[ikey], # Colors
+#                            label=key)
+#     
+#         # put ticker position, see
+#         # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
+#         # dia.ax.axis['bottom'].
+#         # dia.ax.axis['left'].
+#         # dia.ax.axis['left'].
+#     
+#         i += 1
+#     
+#     i = 0
+#     for varkey in ['h','theta','q']:                                                    
+#         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+#             istation = 0
+#             for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+#                 indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
+#                 station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
+#                 station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
+#     
+#                 axes[varkey].scatter(station_obs,station_mod,marker=symbols[istation],color=colors[ikey])
+#                          #  label=key+", "+\
+#                          #                    'R = '+str(round(PR[0],3))+', '+\
+#                          #                    'RMSE = '+str(round(RMSE,5))+', '+\
+#                          #                    'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+#     
+#     
+#     
+#             # # pl.scatter(obs,mod,label=key+", "+\
+#             # #                              'R = '+str(round(PR[0],3))+', '+\
+#             # #                              'RMSE = '+str(round(RMSE,5))+', '+\
+#             # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
+#                 
+#                 dias[varkey].add_sample(station_mod.std()/station_obs.std(),
+#                                pearsonr(station_mod,station_obs)[0],#annotate=symbols[istation],
+#                                marker=symbols[istation], ms=5, ls='',
+#                                mfc='k', mec='k', # B&W
+#                                #mfc=colors[ikey], mec=colors[ikey], # Colors
+#                                label=key)
+# 
+#                 istation += 1
+#     
+#             if varkey == 'q':
+#                 units_final = r'[$g\, kg^{-1}\, h^{-1}$]'
+#             elif varkey == 'theta':
+#                 units_final = r'[$K\, h^{-1}$]'
+#             elif varkey == 'h':
+#                 units_final = r'[$m\, h^{-1}$]'
+#     
+#             axes[varkey].set_xlabel('Observed')     
+#             axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12)                                     
+# 
+# 
+#         # if varkey == 'q':
+#         #     print('get_xlim not working well...STRANGE')
+#         #     limits =  [np.percentile(nani,1),np.percentile(nani,99)]
+#         # else:
+#         #     limits =  [np.percentile(nani,1.0),np.percentile(nani,99.0)]
+# 
+# 
+#         if i==0:                                    
+#             axes[varkey].set_ylabel('Modelled')                                            
+#         i +=1
+#           
+#         axes[varkey].set_aspect('equal')
+#         low  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].min()
+#         high  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].max()
+# 
+#         low  = np.min([low,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].min()])
+#         high  = np.max([high,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].max()])
+# 
+#         low = low - (high - low)*0.1
+#         high = high + (high - low)*0.1
+#         axes[varkey].set_xlim([low,high])
+#         axes[varkey].set_ylim([low,high])
+#         abline(1,0,axis=axes[varkey])
+#         if varkey == 'q':
+#             ticks = ticker.FuncFormatter(lambda x, pos:
+#                                          '{0:g}'.format(x*1000.))
+#             axes[varkey].xaxis.set_major_formatter(ticks)
+#             axes[varkey].yaxis.set_major_formatter(ticks)
+#     
+#     
+#     # # legend for different forcing simulations (colors)
+#     # ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+#     # leg = []
+#     # for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
+#     #     leg1, = ax.plot([],colors[ikey]+'s' ,markersize=10)
+#     #     leg.append(leg1)
+#     # ax.axis('off')
+#     # #leg1 =
+#     # ax.legend(leg,list(args.experiments.strip(' ').split(' ')),loc=2,fontsize=10)
+#     
+#     
+#     # legend for different stations (symbols)
+#     ax = fig.add_axes([0.08,-0.02,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
+#     leg = []
+#     isymbol = 0
+#     for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
+#         leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+#         leg.append(leg1)
+#         isymbol += 1
+#     
+#     # symbol for all stations
+#     leg1, = ax.plot([],'ko',markersize=10)
+#     leg.append(leg1)
+#     
+#     
+#     ax.axis('off')
+#     ax.legend(leg,['HUMPPA','BLLAST','GOAMAZON','All'],loc=2,fontsize=10,ncol=4)
+#     
+#     
+#     fig.subplots_adjust(top=0.95,bottom=0.20,left=0.08,right=0.94,hspace=0.28,wspace=0.29)
+#     
+#     
+#     #pl.legend(leglist,('EMI:WOC','EMI:MED','EMI:BEC'),loc=2,fontsize=16,prop={'family':
+#     #figfn = '/user/data/gent/gvo000/gvo00090/D2D/archive/report/iops_eval_report.png'
+#     
+#     if args.figure_filename is not None:
+#         fig.savefig(args.figure_filename,dpi=200); print("Image file written to:",args.figure_filename)
+#         fig.savefig(args.figure_filename.replace('png','pdf')); print("Image file written to:", args.figure_filename)
+#     fig.show()  
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 72e1ba5..b5e5c6d 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -253,7 +253,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
                          #(self.frames['stats']['records_all_stations_mod_stats'].dhdt >  50.0000) & 
                          (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt >  40.0000) & 
                          #(self.frames['stats']['records_all_stations_mod_stats'].dhdt <  350.) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt <  350.) & 
+                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt <  400.) & 
                          (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt >  -.00055) & 
                          #(self.frames['stats']['records_all_stations_mod_stats'].dqdt >  -.00055) & 
                          (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt <  .0003) & 
@@ -453,7 +453,7 @@ def next_record(self,event=None,jump=1):
                 if 'current_station_file_afternoon' in self.frames['profiles'].keys():
                     self.frames['profiles']['current_station_file_afternoon'].close()
                 self.frames['profiles']['current_station_file_afternoon'] = \
-                    open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_afternoon.yaml','r')
+                    open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
 
         self.update_record()
 
diff --git a/class4gl/model.py b/class4gl/model.py
index d3b1fdb..92792f5 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -1880,7 +1880,7 @@ def exitmodel(self):
         del(self.theta)
         del(self.dtheta)
         del(self.gammatheta)
-        #del(self.advtheta)
+        del(self.advtheta)
         del(self.beta)
         del(self.wtheta)
     
diff --git a/class4gl/setup/batch_setup_igra.pbs b/class4gl/setup/batch_setup_igra.pbs
index ecf97f8..9777de5 100644
--- a/class4gl/setup/batch_setup_igra.pbs
+++ b/class4gl/setup/batch_setup_igra.pbs
@@ -7,7 +7,11 @@
 #PBS -m a
 #PBS -N c4gl_setup
 
-module load Python/3.6.4-intel-2018a IPython basemap BeautifulSoup xarray Pysolar PyYAML netcdf4-python Ruby
+module purge
+source ~/.bashrc
+
+echo loading modules: $LOADDEPSCLASS4GL 
+$LOADDEPSCLASS4GL 
 
 EXEC_ALL="python $C4GLJOB_exec --first_station_row $PBS_ARRAYID --last_station_row $PBS_ARRAYID"
 
diff --git a/class4gl/setup/setup_bllast_noon.py b/class4gl/setup/setup_bllast_noon.py
new file mode 100644
index 0000000..e846d25
--- /dev/null
+++ b/class4gl/setup/setup_bllast_noon.py
@@ -0,0 +1,719 @@
+# -*- coding: utf-8 -*-
+# Read data from BLLAST campaing and convert it to class4gl input
+
+# WARNING!! stupid tab versus space formatting, grrrmmmlmlmlll!  the following command needs to be executed first: 
+#    for file in RS_2011????_????_site1_MODEM_CRA.cor ;  do expand -i -t 4 $file > $file.fmt ; done
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 42.971834,
+                  "longitude" : 0.3671169,
+                  "name" : "the BLLAST experiment"
+                })
+current_station.name = 90001
+
+
+
+
+
+# RS_20110624_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110630_1700_site1_MODEM_CRA.cor.fmt
+# RS_20110702_1655_site1_MODEM_CRA.cor.fmt
+# RS_20110621_0509_site1_MODEM_CRA.cor.fmt
+
+HOUR_FILES = \
+{ dt.datetime(2011,6,19,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110619_0521_site1_MODEM_CRA.cor.fmt'],'afternoon':[11.25,'RS_20110619_1115_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,20,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110620_0515_site1_MODEM_CRA.cor.fmt'],'afternoon':[11.25,'RS_20110620_1115_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,6,25,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110625_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[11,'RS_20110625_1100_site1_MODEM_CRA.cor.fmt']},
+# dt.datetime(2011,6,26,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110626_0500_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110626_1700_site1_MODEM_CRA.cor.fmt']},
+# dt.datetime(2011,6,27,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110627_0503_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110627_1700_site1_MODEM_CRA.cor.fmt']},
+ dt.datetime(2011,7, 2,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110702_0501_site1_MODEM_CRA.cor.fmt'],'afternoon':[11,'RS_20110702_1057_site1_MODEM_CRA.cor.fmt']},
+# dt.datetime(2011,7, 5,0,0,0,0,pytz.UTC):{'morning':[5,'RS_20110705_0448_site1_MODEM_CRA.cor.fmt'],'afternoon':[17,'RS_20110705_1701_site1_MODEM_CRA.cor.fmt']},
+}
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_csv(balloon_file,delimiter='\t',)
+                                     #widths=[14]*19,
+                                     #skiprows=9,
+                                     #skipfooter=15,
+                                     #decimal='.',
+                                     #header=None,
+                                     #names = columns,
+                                     #na_values='-----')
+        air_balloon_in = air_balloon_in.rename(columns=lambda x: x.strip())
+        print(air_balloon_in.columns)
+        rowmatches = {
+            't':      lambda x: x['TaRad']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['Press']*100.,
+            'u':      lambda x: x['VHor'] * np.sin((90.-x['VDir'])/180.*np.pi),
+            'v':      lambda x: x['VHor'] * np.cos((90.-x['VDir'])/180.*np.pi),
+            'z':      lambda x: x['Altitude'] -582.,
+            # from virtual temperature to absolute humidity
+            'q':      lambda x: qfrom_e_p(efrom_rh100_T(x['UCal'],x['TaRad']+273.15),x['Press']*100.),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['VHor'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+        # filter data so that potential temperature always increases with
+        # height 
+        cols = []
+        for column in air_ap_tail.columns:
+            #if column != 'z':
+                cols.append(column)
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        # 
+        # # we copy the pressure at ground level from balloon sounding. The
+        # # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually
+        # write local solar time, we need to assign the timezone to UTC (which
+        # is WRONG!!!). Otherwise ruby cannot understand it (it always converts
+        # tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise']+dt.timedelta(hours=2))\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        print('ldatetime_daylight',dpars['ldatetime_daylight'])
+        print('ldatetime',dpars['ldatetime'])
+        print('lSunrise',dpars['lSunrise'])
+        dpars['day'] = dpars['ldatetime'].day
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        print('tstart',dpars['tstart'])
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='bllast',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS_NOON/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
+    
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = bllast_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = bllast_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS_NOON/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+
+    
+    c4gli_morning.pars.sw_ac = []
+    c4gli_morning.pars.sw_ap = True
+    c4gli_morning.pars.sw_lit = False
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/setup_goamazon_noon.py b/class4gl/setup/setup_goamazon_noon.py
new file mode 100644
index 0000000..c99e913
--- /dev/null
+++ b/class4gl/setup/setup_goamazon_noon.py
@@ -0,0 +1,755 @@
+# -*- coding: utf-8 -*-
+
+import xarray as xr
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+import glob
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : -3.21,
+                  "longitude" : -60.6,
+                  "name" : "the GOAMAZON experiment"
+                })
+current_station.name = 90002
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+#DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
+#DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
+#DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
+
+DTS = [dt.datetime(2014,11,15,0,0,0,0,pytz.UTC), 
+       dt.datetime(2014,12,29,0,0,0,0,pytz.UTC),
+       dt.datetime(2015,1,5,0,0,0,0,pytz.UTC),
+       dt.datetime(2015,5,7,0,0,0,0,pytz.UTC)
+      ]
+
+
+
+
+
+HOUR_FILES = {}
+for iDT, DT in enumerate(DTS):
+    morning_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
+    if len(possible_files)>0:
+        morning_file= possible_files[0]
+    afternoon_file = None
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.12??00.*.cdf')+\
+                     glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.11??00.*.cdf')+\
+                     glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.10??00.*.cdf')
+    if len(possible_files)>0:
+        afternoon_file= possible_files[-1]
+        hour_afternoon = int(afternoon_file[-17:-15])+float(afternoon_file[-15:-13])/60.
+    if (morning_file is not None) and (afternoon_file is not None):
+        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
+                          'afternoon':[hour_afternoon,afternoon_file]}
+
+print(HOUR_FILES)
+
+# HOUR_FILES = \
+# {
+#     dt.datetime(2015,5,7,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150507.052900.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150507.172700.custom.cdf']},
+#     dt.datetime(2015,3,13,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150313.052700.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150313.173000.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+#     dt.datetime(2015,3,12,0,0,0,0,pytz.UTC):{'morning':  [5.5,'maosondewnpnM1.b1.20150312.052800.custom.cdf'],
+#                                              'afternoon':[17.50,'maosondewnpnM1.b1.20150312.173400.custom.cdf']},
+# }
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+def esat(T):
+    return 0.611e3 * np.exp(17.2694 * (T - 273.16) / (T - 35.86))
+def efrom_rh100_T(rh100,T):
+    return esat(T)*rh100/100.
+def qfrom_e_p(e,p):
+    return epsilon * e/(p - (1.-epsilon)*e)
+
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
+        print(balloon_file)
+        
+        xrin = balloon_file
+        air_balloon = pd.DataFrame()
+
+        air_balloon['t'] = xrin.tdry.values+273.15
+        air_balloon['p'] = xrin.pres.values*100.
+        
+        air_balloon['u'] = xrin.u_wind.values
+        air_balloon['v'] = xrin.v_wind.values
+        air_balloon['WSPD'] = xrin['wspd'].values
+        
+        print(xrin.rh.values.shape)
+        air_balloon['q'] = qfrom_e_p(efrom_rh100_T(xrin.rh.values,air_balloon['t'].values),air_balloon.p.values)
+        
+
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        # air_balloon_in = pd.read_fwf(balloon_file,
+        #                              widths=[14]*19,
+        #                              skiprows=9,
+        #                              skipfooter=15,
+        #                              decimal=',',
+        #                              header=None,
+        #                              names = columns,
+        #                              na_values='-----')
+    
+
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
+            'rho': lambda x: x.p /x.t / x.R ,
+        }
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        print('alt in xrin?:','alt' in xrin)
+        if 'alt' in xrin:
+            air_balloon['z'] = xrin.alt.values
+        else:
+            air_balloon['z'] = 0.
+            for irow,row in air_balloon.iloc[1:].iterrows():
+                air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
+                        
+             
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # # there are issues with the lower measurements in the HUMPPA campaign,
+        # # for which a steady decrease of potential temperature is found, which
+        # # is unrealistic.  Here I filter them away
+        # ifirst = 0
+        # while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+        #     ifirst = ifirst+1
+        # print ('ifirst:',ifirst)
+        # air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        air_balloon = air_balloon.iloc[:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if (
+        #         #(z_mean > z_low) and \
+        #         (z_mean > (z_low+10.)) and \
+        #         #(theta_mean > (theta_low+0.2) ) and \
+        #         #(theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.00001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        print('ldate',ldate)
+        print('lhour',lhour)
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunately!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS_NOON/'
+
+
+os.system('mkdir -p '+path_soundings)
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn =pair['morning'][1]
+    print(humpafn)
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn = pair['afternoon'][1]
+    balloon_file = xr.open_dataset(humpafn)
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM//humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS_NOON/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+
+"""
+stations_for_iter = stations(path_exp)
+for STNID,station in stations_iterator(stations_for_iter):
+    records_current_station_index = \
+            (records_ini.index.get_level_values('STNID') == STNID)
+    file_current_station_mod = STNID
+
+    with \
+    open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+    open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        for (STNID,index),record_ini in records_iterator(records_ini):
+            c4gli_ini = get_record_yaml(file_station_ini, 
+                                        record_ini.index_start, 
+                                        record_ini.index_end,
+                                        mode='ini')
+            #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+
+            record_mod = records_mod.loc[(STNID,index)]
+            c4gl_mod = get_record_yaml(file_station_mod, 
+                                        record_mod.index_start, 
+                                        record_mod.index_end,
+                                        mode='mod')
+            record_afternoon = records_afternoon.loc[(STNID,index)]
+            c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+                                        record_afternoon.index_start, 
+                                        record_afternoon.index_end,
+                                        mode='ini')
+"""
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/setup_humppa_noon.py b/class4gl/setup/setup_humppa_noon.py
new file mode 100644
index 0000000..72bbf89
--- /dev/null
+++ b/class4gl/setup/setup_humppa_noon.py
@@ -0,0 +1,733 @@
+# -*- coding: utf-8 -*-
+
+import pandas as pd
+import io
+import os
+import numpy as np
+import datetime as dt
+import Pysolar
+import sys
+import pytz
+sys.path.insert(0,'/user/home/gent/vsc422/vsc42247/software/class4gl/class4gl/')
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+
+globaldata = data_global()
+globaldata.load_datasets(recalc=0)
+
+Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
+cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
+Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
+epsilon = Rd/Rv # or mv/md
+
+
+def replace_iter(iterable, search, replace):
+    for value in iterable:
+        value.replace(search, replace)
+        yield value
+
+from class4gl import blh,class4gl_input
+
+# definition of the humpa station
+current_station = pd.Series({ "latitude"  : 61.8448,
+                  "longitude" : 24.2882,
+                  "name" : "the HUMMPA experiment"
+                })
+current_station.name = 90000
+
+# we define the columns ourselves because it is a mess in the file itself.
+columns =\
+['Time[min:sec]',
+ 'P[hPa]',
+ 'T[C]',
+ 'U[%]',
+ 'Wsp[m/s]',
+ 'Wdir[Grd]',
+ 'Lon[°]',
+ 'Lat[°]',
+ 'Altitude[m]',
+ 'GeoPot[m]',
+ 'MRI',
+ 'RI',    
+ 'DewPoint[C]',
+ 'Virt. Temp[C]',
+ 'Rs[m/min]',
+ 'D[kg/m3]',
+ 'Azimut[°]',
+ 'Elevation[°]',
+ 'Range[m]',
+]
+
+
+HOUR_FILES = \
+{ 
+  # dt.datetime(2010,7,12,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071210_0300.txt'],'afternoon':[15,'humppa_071210_1500.txt']},
+  dt.datetime(2010,7,13,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071310_0300.txt'],'afternoon':[12,'humppa_071310_1200.txt']},
+#  dt.datetime(2010,7,14,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071410_0300.txt'],'afternoon':[16,'humppa_071410_1600.txt']},
+  #dt.datetime(2010,7,15,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071510_0300.txt'],'afternoon':[15,'humppa_071510_1500.txt']},
+  #dt.datetime(2010,7,16,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071610_0300.txt'],'afternoon':[21,'humppa_071610_2100.txt']},
+  #dt.datetime(2010,7,17,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071710_0300.txt'],'afternoon':[18,'humppa_071710_1800.txt']},
+  #dt.datetime(2010,7,18,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071810_0300.txt'],'afternoon':[21,'humppa_071810_2100.txt']},
+  dt.datetime(2010,7,19,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_071910_0300.txt'],'afternoon':[9,'humppa_071910_0900.txt']},
+#  dt.datetime(2010,7,20):{'morning':[4,'humppa_072010_0400.txt'],'afternoon':[15,'humppa_072010_1500.txt']},
+#  dt.datetime(2010,7,21,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072110_0300.txt'],'afternoon':[21,'humppa_072110_2100.txt']},
+  dt.datetime(2010,7,22,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072210_0400.txt'],'afternoon':[12,'humppa_072210_1200.txt']},
+ # something is wrong with ths profile
+ # dt.datetime(2010,7,23,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072310_0300.txt'],'afternoon':[15,'humppa_072310_1500.txt']},
+#  dt.datetime(2010,7,24,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072410_0300.txt'],'afternoon':[16,'humppa_072410_1600.txt']},
+#  dt.datetime(2010,7,25,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072510_0300.txt'],'afternoon':[21,'humppa_072510_2100.txt']},
+#  dt.datetime(2010,7,26,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072610_0300.txt'],'afternoon':[21,'humppa_072610_2100.txt']},
+#  dt.datetime(2010,7,27,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072710_0300.txt'],'afternoon':[15,'humppa_072710_1500.txt']},
+#  dt.datetime(2010,7,28,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_072810_0300.txt'],'afternoon':[15,'humppa_072810_1500.txt']},
+  dt.datetime(2010,7,29,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_072910_0400.txt'],'afternoon':[12,'humppa_072910_1200.txt']},
+#  dt.datetime(2010,7,30,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_073010_0900.txt'],'afternoon':[15,'humppa_073010_1500.txt']},
+  dt.datetime(2010,7,31,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_073110_0300_01.txt'],'afternoon':[12,'humppa_073110_1200.txt']},
+#  dt.datetime(2010,8, 1,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080110_0300.txt'],'afternoon':[18,'humppa_080110_1800.txt']},
+  dt.datetime(2010,8, 2,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080210_0300.txt'],'afternoon':[9,'humppa_080210_0900.txt']},
+#  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[9,'humppa_080310_0900.txt'],'afternoon':[18,'humppa_080310_1800.txt']},
+#  dt.datetime(2010,8, 3,0,0,0,0,pytz.UTC):{'morning':[8,'humppa_080410_0800.txt'],'afternoon':[18,'humppa_080410_1800.txt']},
+#  dt.datetime(2010,8, 5,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080510_0300.txt'],'afternoon':[18,'humppa_080510_1800.txt']},
+#  dt.datetime(2010,8, 6,0,0,0,0,pytz.UTC):{'morning':[4,'humppa_080610_0400.txt'],'afternoon':[18,'humppa_080610_1800.txt']},
+#  dt.datetime(2010,8, 7,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080710_0300.txt'],'afternoon':[18,'humppa_080710_1800.txt']},
+#  dt.datetime(2010,8, 8,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_080810_0300.txt'],'afternoon':[18,'humppa_080810_1800.txt']},
+  dt.datetime(2010,8,10,0,0,0,0,pytz.UTC):{'morning':[3,'humppa_081010_0300.txt'],'afternoon':[12,'humppa_081010_1200.txt']},
+}
+
+
+
+
+
+
+#only include the following timeseries in the model output
+timeseries_only = \
+['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+ 'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+ 'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+ 'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+ 'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
+        #balloon_conv = replace_iter(balloon_file,"°","deg")
+        #readlines = [ str(line).replace('°','deg') for line in balloon_file.readlines()]
+        #air_balloon = pd.read_fwf( io.StringIO(''.join(readlines)),skiprows=8,skipfooter=15)
+        air_balloon_in = pd.read_fwf(balloon_file,
+                                     widths=[14]*19,
+                                     skiprows=9,
+                                     skipfooter=15,
+                                     decimal=',',
+                                     header=None,
+                                     names = columns,
+                                     na_values='-----')
+    
+        rowmatches = {
+            't':      lambda x: x['T[C]']+273.15,
+            #'tv':     lambda x: x['Virt. Temp[C]']+273.15,
+            'p':      lambda x: x['P[hPa]']*100.,
+            'u':      lambda x: x['Wsp[m/s]'] * np.sin((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'v':      lambda x: x['Wsp[m/s]'] * np.cos((90.-x['Wdir[Grd]'])/180.*np.pi),
+            'z':      lambda x: x['Altitude[m]'],
+            'q':      lambda x: np.clip((1. - (273.15+x['Virt. Temp[C]'])/(273.15+x['T[C]']))/(1. - 1./epsilon),a_min=0.,a_max=None),
+        }
+        
+        air_balloon = pd.DataFrame()
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon_in)
+        
+        rowmatches = {
+            'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
+            'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q
+        }
+        
+        for varname,lfunction in rowmatches.items():
+            air_balloon[varname] = lfunction(air_balloon)
+        
+        dpars = {}
+        dpars['longitude']  = current_station['longitude']
+        dpars['latitude']  = current_station['latitude'] 
+        
+        dpars['STNID'] = current_station.name
+        
+
+        # there are issues with the lower measurements in the HUMPPA campaign,
+        # for which a steady decrease of potential temperature is found, which
+        # is unrealistic.  Here I filter them away
+        ifirst = 0
+        while  (air_balloon.theta.iloc[ifirst+1] < air_balloon.theta.iloc[ifirst]):
+            ifirst = ifirst+1
+        print ('ifirst:',ifirst)
+        air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
+        
+        is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
+        valid_indices = air_balloon.index[is_valid].values
+        
+        air_ap_mode='b'
+        
+        if len(valid_indices) > 0:
+            dpars['h'],dpars['h_u'],dpars['h_l'] =\
+                blh(air_balloon.z,air_balloon.thetav,air_balloon_in['Wsp[m/s]'])
+            dpars['h_b'] = np.max((dpars['h'],10.))
+            dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
+            dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
+            dpars['h_e'] = np.abs( dpars['h_u'] - dpars['h_l']) # error of mixed-layer height
+            dpars['h'] = np.round(dpars['h_'+air_ap_mode],1)
+        else:
+            dpars['h_u'] =np.nan
+            dpars['h_l'] =np.nan
+            dpars['h_e'] =np.nan
+            dpars['h'] =np.nan
+        
+        
+        
+        if ~np.isnan(dpars['h']):
+            dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        else:
+            dpars['Ps'] = np.nan
+        
+        if ~np.isnan(dpars['h']):
+        
+            # determine mixed-layer properties (moisture, potential temperature...) from profile
+            
+            # ... and those of the mixed layer
+            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices) > 1:
+                if len(valid_indices_below_h) >= 3.:
+                    ml_mean = air_balloon[is_valid_below_h].mean()
+                else:
+                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+            elif len(valid_indices) == 1:
+                ml_mean = (air_balloon.iloc[0:1]).mean()
+            else:
+                temp =  pd.DataFrame(air_balloon)
+                temp.iloc[0] = np.nan
+                ml_mean = temp
+                       
+            dpars['theta']= ml_mean.theta
+            dpars['q']    = ml_mean.q
+            dpars['u']    = ml_mean.u
+            dpars['v']    = ml_mean.v 
+        else:
+            dpars['theta'] = np.nan
+            dpars['q'] = np.nan
+            dpars['u'] = np.nan
+            dpars['v'] = np.nan
+        
+        air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
+        # All other  data points above the mixed-layer fit
+        air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
+
+
+
+        air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
+        jump = air_ap_head.iloc[0] * np.nan
+        
+        if air_ap_tail.shape[0] > 1:
+        
+            # we originally used THTA, but that has another definition than the
+            # variable theta that we need which should be the temperature that
+            # one would have if brought to surface (NOT reference) pressure.
+            for column in ['theta','q','u','v']:
+               
+               # initialize the profile head with the mixed-layer values
+               air_ap_head[column] = ml_mean[column]
+               # calculate jump values at mixed-layer height, which will be
+               # added to the third datapoint of the profile head
+               jump[column] = (air_ap_tail[column].iloc[1]\
+                               -\
+                               air_ap_tail[column].iloc[0])\
+                              /\
+                              (air_ap_tail.z.iloc[1]\
+                               - air_ap_tail.z.iloc[0])\
+                              *\
+                              (dpars['h']- air_ap_tail.z.iloc[0])\
+                              +\
+                              air_ap_tail[column].iloc[0]\
+                              -\
+                              ml_mean[column] 
+               if column == 'theta':
+                  # for potential temperature, we need to set a lower limit to
+                  # avoid the model to crash
+                  jump.theta = np.max((0.1,jump.theta))
+        
+               air_ap_head[column][2] += jump[column]
+        
+        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+
+
+
+        # only select samples monotonically increasing with height
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        for ibottom in range(1,len(air_ap_tail_orig)):
+            if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
+
+        # make theta increase strong enough to avoid numerical
+        # instability
+        air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        air_ap_tail = pd.DataFrame()
+        #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        theta_low = air_ap_head['theta'].iloc[2]
+        z_low = air_ap_head['z'].iloc[2]
+        ibottom = 0
+        for itop in range(0,len(air_ap_tail_orig)):
+            theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+            z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+            if (
+                #(z_mean > z_low) and \
+                (z_mean > (z_low+10.)) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                #(theta_mean > (theta_low+0.2) ) and \
+                 (((theta_mean - theta_low)/(z_mean - z_low)) > 0.0001)):
+
+                air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+                ibottom = itop+1
+                theta_low = air_ap_tail.theta.iloc[-1]
+                z_low =     air_ap_tail.z.iloc[-1]
+            # elif  (itop > len(air_ap_tail_orig)-10):
+            #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        
+        air_ap = \
+            pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+
+        # # make theta increase strong enough to avoid numerical
+        # # instability
+        # air_ap_tail_orig = pd.DataFrame(air_ap_tail)
+        # air_ap_tail = pd.DataFrame()
+        # #air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
+        # theta_low = air_ap_head['theta'].iloc[2]
+        # z_low = air_ap_head['z'].iloc[2]
+        # ibottom = 0
+        # for itop in range(0,len(air_ap_tail_orig)):
+        #     theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
+        #     z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
+        #     if ((theta_mean > (theta_low+0.2) ) and \
+        #          (((theta_mean - theta_low)/(z_mean - z_low)) > 0.001)):
+
+        #         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom:(itop+1)].mean(),ignore_index=True)
+        #         ibottom = itop+1
+        #         theta_low = air_ap_tail.theta.iloc[-1]
+        #         z_low =     air_ap_tail.z.iloc[-1]
+        #     # elif  (itop > len(air_ap_tail_orig)-10):
+        #     #     air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[itop],ignore_index=True)
+        # 
+        # air_ap = \
+        #     pd.concat((air_ap_head,air_ap_tail)).reset_index().drop(['index'],axis=1)
+        
+        # we copy the pressure at ground level from balloon sounding. The
+        # pressure at mixed-layer height will be determined internally by class
+        
+        rho        = 1.2                   # density of air [kg m-3]
+        g          = 9.81                  # gravity acceleration [m s-2]
+        
+        air_ap['p'].iloc[0] =dpars['Ps'] 
+        air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
+        air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
+        
+        
+        dpars['lat'] = dpars['latitude']
+        # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
+        dpars['lon'] = 0.
+        # this is the real longitude that will be used to extract ground data
+        
+        dpars['ldatetime'] = ldate+dt.timedelta(hours=hour)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-3)
+        dpars['doy'] = dpars['datetime'].timetuple().tm_yday
+        
+        dpars['SolarAltitude'] = \
+                                Pysolar.GetAltitude(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        dpars['SolarAzimuth'] =  Pysolar.GetAzimuth(\
+                                    dpars['latitude'],\
+                                    dpars['longitude'],\
+                                    dpars['datetime']\
+                                )
+        
+        
+        dpars['lSunrise'], dpars['lSunset'] \
+        =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
+                                         0.,
+                                         dpars['ldatetime'],0.)
+        
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
+        dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
+        
+        # This is the nearest datetime when the sun is up (for class)
+        dpars['ldatetime_daylight'] = \
+                                np.min(\
+                                    (np.max(\
+                                        (dpars['ldatetime'],\
+                                         dpars['lSunrise'])\
+                                     ),\
+                                     dpars['lSunset']\
+                                    )\
+                                )
+        # apply the same time shift for UTC datetime
+        dpars['datetime_daylight'] = dpars['datetime'] \
+                                    +\
+                                    (dpars['ldatetime_daylight']\
+                                     -\
+                                     dpars['ldatetime'])
+        
+        
+        # We set the starting time to the local sun time, since the model 
+        # thinks we are always at the meridian (lon=0). This way the solar
+        # radiation is calculated correctly.
+        dpars['tstart'] = dpars['ldatetime_daylight'].hour \
+                         + \
+                         dpars['ldatetime_daylight'].minute/60.\
+                         + \
+                         dpars['ldatetime_daylight'].second/3600.
+        
+        dpars['sw_lit'] = False
+        # convert numpy types to native python data types. This provides
+        # cleaner data IO with yaml:
+        for key,value in dpars.items():
+            if type(value).__module__ == 'numpy':
+                dpars[key] = dpars[key].item()
+        
+                decimals = {'p':0,'t':2,'theta':4, 'z':2, 'q':5, 'u':4, 'v':4}
+        # 
+                for column,decimal in decimals.items():
+                    air_balloon[column] = air_balloon[column].round(decimal)
+                    air_ap[column] = air_ap[column].round(decimal)
+        
+        updateglobal = False
+        if c4gli is None:
+            c4gli = class4gl_input()
+            updateglobal = True
+        
+        print('updating...')
+        print(column)
+        c4gli.update(source='humppa',\
+                    # pars=pars,
+                    pars=dpars,\
+                    air_balloon=air_balloon,\
+                    air_ap=air_ap)
+        if updateglobal:
+            c4gli.get_global_input(globaldata)
+
+        # if profile_ini:
+        #     c4gli.runtime = 10 * 3600
+
+        c4gli.dump(file_sounding)
+        
+        # if profile_ini:
+        #     c4gl = class4gl(c4gli)
+        #     c4gl.run()
+        #     c4gl.dump(file_model,\
+        #               include_input=True,\
+        #               timeseries_only=timeseries_only)
+        #     
+        #     # This will cash the observations and model tables per station for
+        #     # the interface
+        # 
+        # if profile_ini:
+        #     profile_ini=False
+        # else:
+        #     profile_ini=True
+        return c4gli
+
+
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS_NOON/'
+
+
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    print(pair['morning'])
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
+    print(humpafn)
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
+    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+file_morning.close()
+
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+for date,pair  in HOUR_FILES.items(): 
+    humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
+    balloon_file = open(humpafn,'r',encoding='latin-1')
+
+    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
+    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+file_afternoon.close()
+ 
+
+# file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+# for date,pair  in HOUR_FILES.items(): 
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1],
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_morning,hour,c4gli_morning)
+#     print('c4gli_morning_ldatetime 1',c4gli_morning.pars.ldatetime)
+# file_morning.close()
+# 
+# file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+# for hour in [18]:
+#     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/humppa_080610_'+format(hour,"02d")+'00.txt'
+#     balloon_file = open(humpafn,'r',encoding='latin-1')
+# 
+#     humppa_parser(balloon_file,file_afternoon,hour,c4gli_afternoon)
+# file_afternoon.close()
+
+
+
+# path_model = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/HUMPPA/'
+# 
+# file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
+
+
+records_morning = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='morning',
+                                           refetch_records=True,
+                                           )
+print('records_morning_ldatetime',records_morning.ldatetime)
+
+records_afternoon = get_records(pd.DataFrame([current_station]),\
+                                           path_soundings,\
+                                           subset='afternoon',
+                                           refetch_records=True,
+                                           )
+
+# align afternoon records with noon records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+records_afternoon.index = records_morning.index
+path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS_NOON/'
+
+os.system('mkdir -p '+path_exp)
+file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+
+for (STNID,chunk,index),record_morning in records_morning.iterrows():
+    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+
+    c4gli_morning = get_record_yaml(file_morning, 
+                                    record_morning.index_start, 
+                                    record_morning.index_end,
+                                    mode='ini')
+    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+    
+    
+    c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                      record_afternoon.index_start, 
+                                      record_afternoon.index_end,
+                                    mode='ini')
+
+    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon.pars.datetime_daylight - 
+                             c4gli_morning.pars.datetime_daylight).total_seconds())})
+    c4gli_morning.update(source='manual',
+                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+    c4gli_morning.dump(file_ini)
+    
+    c4gl = class4gl(c4gli_morning)
+    c4gl.run()
+    
+    c4gl.dump(file_mod,\
+              include_input=False,\
+              timeseries_only=timeseries_only)
+file_ini.close()
+file_mod.close()
+file_morning.close()
+file_afternoon.close()
+
+records_ini = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='ini',
+                                           refetch_records=True,
+                                           )
+records_mod = get_records(pd.DataFrame([current_station]),\
+                                           path_exp,\
+                                           subset='mod',
+                                           refetch_records=True,
+                                           )
+
+records_mod.index = records_ini.index
+
+# align afternoon records with initial records, and set same index
+records_afternoon.index = records_afternoon.ldatetime.dt.date
+records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+records_afternoon.index = records_ini.index
+
+# stations_for_iter = stations(path_exp)
+# for STNID,station in stations_iterator(stations_for_iter):
+#     records_current_station_index = \
+#             (records_ini.index.get_level_values('STNID') == STNID)
+#     file_current_station_mod = STNID
+# 
+#     with \
+#     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+#     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+#         for (STNID,index),record_ini in records_iterator(records_ini):
+#             c4gli_ini = get_record_yaml(file_station_ini, 
+#                                         record_ini.index_start, 
+#                                         record_ini.index_end,
+#                                         mode='ini')
+#             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+# 
+#             record_mod = records_mod.loc[(STNID,index)]
+#             c4gl_mod = get_record_yaml(file_station_mod, 
+#                                         record_mod.index_start, 
+#                                         record_mod.index_end,
+#                                         mode='mod')
+#             record_afternoon = records_afternoon.loc[(STNID,index)]
+#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+#                                         record_afternoon.index_start, 
+#                                         record_afternoon.index_end,
+#                                         mode='ini')
+
+
+
+# # select the samples of the afternoon list that correspond to the timing of the
+# # morning list
+# records_afternoon = records_afternoon.set_index('ldatetime').loc[records_afternoon.ldatetime)]
+# records_afternoon.index = recods_morning.index
+# 
+# 
+# # create intersectino index
+# index_morning = pd.Index(records_morning.ldatetime.to_date())
+# index_afternoon = pd.Index(records_afternoon.ldatetime.to_date())
+# 
+# for record_morning in records_morning.iterrows():
+#     
+#     c4gl = class4gl(c4gli)
+#     c4gl.run()
+#     c4gl.dump(c4glfile,\
+#               include_input=True,\
+#               timeseries_only=timeseries_only)
+# 
+# # This will cash the observations and model tables per station for
+# # the interface
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=0,\
+#                                    by=2,\
+#                                    subset='ini',
+#                                    refetch_records=True,
+#                                    )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                    path_mod,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='mod',
+#                                    refetch_records=True,
+#                                    )
+# records_eval = get_records(pd.DataFrame([current_station]),\
+#                                    path_obs,\
+#                                    start=1,\
+#                                    by=2,\
+#                                    subset='eval',
+#                                    refetch_records=True,
+#                                    )
+# 
+# 
+# # mod_scores = pd.DataFrame(index=mod_records.index)
+# # for (STNID,index), current_record_mod in mod_records.iterrows():
+# #     print(STNID,index)
+# #     current_station = STN
+# #     current_record_obs_afternoon = obs_records_afternoon.loc[(STNID,index)]
+# #     current_record_obs = obs_records.loc[(STNID,index)]
+# # 
+# #     record_yaml_mod = get_record_yaml_mod(odirexperiments[keyEXP],\
+# #                                           current_station,\
+# #                                           current_record_mod,\
+# #                                          )
+# # 
+# #     record_yaml_obs = \
+# #             get_record_yaml_obs(odirexperiments[keyEXP],\
+# #                                 current_station,\
+# #                                 current_record_obs,\
+# #                                 suffix='.yaml')
+# # 
+# #     record_yaml_obs_afternoon = \
+# #             get_record_yaml_obs(odir,\
+# #                                 current_station,\
+# #                                 current_record_obs_afternoon,\
+# #                                 suffix='_afternoon.yaml')
+# # 
+# #     hmax = np.max([record_yaml_obs_afternoon.pars.h,\
+# #                    record_yaml_mod.h])
+# #     HEIGHTS = {'h':hmax, '2h':2.*hmax, '3000m':3000.}
+# #     
+# # 
+# #     for height,hvalue in HEIGHTS.items():
+# # 
+# #         lt_obs = (record_yaml_obs_afternoon.air_ap.HAGL < hvalue)
+# #         lt_mod = (record_yaml_mod.air_ap.z < hvalue)
+# #         try:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = \
+# #                 rmse(\
+# #                     record_yaml_obs_afternoon.air_ap.theta[lt_obs],\
+# #                     np.interp(\
+# #                         record_yaml_obs_afternoon.air_ap.HAGL[lt_obs],\
+# #                         record_yaml_mod.air_ap.z[lt_mod],\
+# #                         record_yaml_mod.air_ap.theta[lt_mod]\
+# #                     ))
+# #         except ValueError:
+# #             mod_scores.at[(STNID,index),'rmse_'+height] = np.nan
+# #     # # we calculate these things in the interface itself
+# #     # for key in ['q','theta','h']:
+# #     #     mod_records.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_mod.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# #     #     # the actual time of the initial and evaluation sounding can be 
+# #     #     # different, but we consider this as a measurement error for
+# #     #     # the starting and end time of the simulation.
+# #     #     obs_records_afternoon.at[(STNID,index),'d'+key+'dt'] = \
+# #     #                 (record_yaml_obs.pars.__dict__[key] -  \
+# #     #                  record_yaml_obs_afternoon.pars.__dict__[key]\
+# #     #                 )/(record_yaml_obs_afternoon.pars.ldatetime - \
+# #     #                    record_yaml_obs.pars.ldatetime).total_seconds()
+# # 
+# # mod_scores.to_pickle(odirexperiments[keyEXP]+'/'+format(STNID,'05d')+"_mod_scores.pkl")
+# #         
+# #                 
+# #                 
+# # # for EXP,c4glfile in c4glfiles.items():
+# # #     c4glfile.close()            
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# # 
+# #     
+# #     # {'Time[min:sec]': None 
+# #     #  'P[hPa]': None, 
+# #     #  'T[C]': None, 
+# #     #  'U[%]': None, 
+# #     #  'Wsp[m/s]': None, 
+# #     #  'Wdir[Grd]': None,
+# #     #  'Lon[°]', 
+# #     #  'Lat[°]', 
+# #     #  'Altitude[m]', 'GeoPot[m']', 'MRI',
+# #     #        'Unnamed: 11', 'RI', 'Unnamed: 13', 'DewPoint[C]', 'Virt. Temp[C]',
+# #     #        'Rs[m/min]D[kg/m3]Azimut[deg]', 'Elevation[deg]', 'Range[m]']
+# #     # }
+# #     # 
+# #     # #pivotrows =
+# #     # #{
+# # 
+# # 
+# # 
diff --git a/class4gl/setup/setup_igra_20181217.py b/class4gl/setup/setup_igra_20181217.py
new file mode 100644
index 0000000..2733f3d
--- /dev/null
+++ b/class4gl/setup/setup_igra_20181217.py
@@ -0,0 +1,361 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thursday, March 29, 11:30 AM
+
+@author: Hendrik Wouters
+
+The dry-2-dry global radio sounding experiment.
+
+usage:
+    python setup_global.py 
+    where  is an integer indicating the row index of the station list
+    under args.path_output+'/'+fn_stations (see below)
+
+this scripts should be called from the pbs script setup_global.pbs
+
+
+
+dependencies:
+    - pandas
+    - class4gl
+    - data_soundings
+
+
+"""
+
+""" import libraries """
+import pandas as pd
+import sys
+#import copy as cp
+import numpy as np
+#from sklearn.metrics import mean_squared_error
+import logging
+import datetime as dt
+import os
+import math
+
+import argparse
+
+#if __name__ == '__main__':
+parser = argparse.ArgumentParser()
+#parser.add_argument('--timestamp')
+parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
+parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+# parser.add_argument('--first_YYYYMMDD',default="19810101")
+# parser.add_argument('--last_YYYYMMDD',default="20180101")
+parser.add_argument('--startyear',default="1981")
+parser.add_argument('--first_station_row')
+parser.add_argument('--last_station_row')
+parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--station_id') # run a specific station id
+# parser.add_argument('--error_handling',default='dump_on_success')
+# parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
+
+
+# args.path_output = "/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GLOBAL/"
+args = parser.parse_args()
+
+sys.path.insert(0, args.c4gl_path_lib)
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+
+fn_stations = args.path_input+'/igra-stations.txt'
+
+
+#calculate the root mean square error
+
+def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actual = False):
+    """ calculated root mean squared error 
+        
+    
+        INPUT:
+            y_actual: reference dataset
+            y_predicted: predicting dataset
+            z_actual: coordinate values of reference dataset
+            z_predicted: coordinate values of the predicting dataset
+            
+            filternan_actual: throw away reference values that have nans
+    """
+    
+    y_actual_temp = np.array(y_actual)
+    y_predicted_temp = np.array(y_predicted)
+    
+    if z_actual is not None:
+        z_actual_temp = np.array(z_actual)
+    else: 
+        z_actual_temp = None
+        
+    
+    if filternan_actual:
+        y_actual_temp = y_actual_temp[~np.isnan(y_actual_temp)]
+        if z_actual_temp is not None:
+            z_actual_temp = z_actual_temp[~np.isnan(y_actual_temp)]
+    
+    if ((z_actual_temp is not None) or (z_predicted is not None)):    
+        if (z_actual_temp is None) or (z_predicted is None):
+            raise ValueError('Input z_actual and z_predicted need \
+                              to be specified simultaneously.')
+        y_predicted_temp = np.interp(z_actual_temp,z_predicted, y_predicted)
+    
+    else:
+        # this catches the situation that y_predicted is a single value (eg., 
+        # which is the case for evaluating eg., mixed-layer estimates)
+        y_predicted_temp = y_actual_temp*0. + y_predicted_temp
+        
+    rmse_temp = (y_actual_temp - y_predicted_temp)
+    rmse_temp = np.mean(rmse_temp*rmse_temp)
+    return np.sqrt(rmse_temp)
+
+
+from class4gl import class4gl_input, data_global,class4gl
+from data_soundings import wyoming
+#from data_global import data_global
+
+# iniitialize global data
+globaldata = data_global()
+# ...  and load initial data pages
+globaldata.load_datasets(recalc=0)
+
+# read the list of stations with valid ground data (list generated with
+# get_valid_stations.py)
+# args.path_input = "/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/"
+
+# df_stations = pd.read_fwf(fn_stations,names=['Country code',\
+#                                                'ID',\
+#                                                'Name',\
+#                                                'latitude',\
+#                                                'longitude',\
+#                                                'height',\
+#                                                'unknown',\
+#                                                'startyear',\
+#                                                'endyear'])
+# 
+
+# ===============================
+print("getting a list of stations")
+# ===============================
+all_stations = stations(args.path_input,refetch_stations=False)
+df_stations = all_stations.table
+df_stations.columns
+
+if args.station_id is not None:
+    df_stations = df_stations.query('STNID == '+args.station_id)
+else:
+    if args.last_station_row is not None:
+        df_stations = df_stations[:(int(args.last_station_row)+1)]
+    if args.first_station_row is not None:
+        df_stations = df_stations[int(args.first_station_row):]
+
+STNlist = list(df_stations.iterrows())
+
+os.system('mkdir -p '+args.path_output)
+for iSTN,STN in STNlist:  
+    one_run = False
+# for iSTN,STN in STNlist[5:]:  
+    
+    fnout = args.path_output+"/"+format(STN.name,'05d')+"_morning.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_afternoon.yaml"
+    
+
+    # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
+    #                   for EXP in experiments.keys()])
+        
+    with open(fnout,'w') as fileout, \
+         open(fnout_afternoon,'w') as fileout_afternoon:
+        wy_strm = wyoming(PATH=args.path_input, STNM=STN.name)
+        wy_strm.set_STNM(int(STN.name))
+
+        # we consider all soundings from 1981 onwards
+        wy_strm.find_first(year=int(args.startyear))
+        #wy_strm.find(dt.datetime(2004,10,19,6))
+        
+        c4gli = class4gl_input(debug_level=logging.INFO)
+        c4gli_afternoon = class4gl_input(debug_level=logging.INFO)
+        # so we continue as long as we can find a new sounding
+                
+        while wy_strm.current is not None:
+            
+            c4gli.clear()
+            try: 
+                c4gli.get_profile_wyoming(wy_strm)
+                #print(STN['ID'],c4gli.pars.datetime)
+                #c4gli.get_global_input(globaldata)
+
+                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+                logic = dict()
+                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+
+                # Sounding should have taken place after 3 hours before sunrise.
+                # Note that the actual simulation only start at sunrise
+                # (specified by ldatetime_daylight), so the ABL cooling af the time
+                # before sunrise is ignored by the simulation.
+                logic['daylight'] = \
+                    ((c4gli.pars.ldatetime - 
+                      c4gli.pars.lSunrise).total_seconds()/3600. >= -3.)
+                
+                logic['springsummer'] = (c4gli.pars.theta > 278.)
+                
+                # we take 3000 because previous analysis (ie., HUMPPA) has
+                # focussed towards such altitude
+                le3000 = (c4gli.air_balloon.z <= 3000.)
+                logic['10measurements'] = (np.sum(le3000) >= 7) 
+
+                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+                logic['mlerrlow'] = (\
+                        (len(np.where(leh)[0]) > 0) and \
+                        # in cases where humidity is not defined, the mixed-layer
+                        # values get corr
+                        (not np.isnan(c4gli.pars.theta))\
+                                     #and \
+                        #(rmse(c4gli.air_balloon.theta[leh] , \
+                        #      c4gli.pars.theta,filternan_actual=True) < 1.0)\
+                              )
+    
+
+                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+                
+                print('logic:', logic)
+                # the result
+                morning_ok = np.mean(list(logic.values()))
+                print(morning_ok,c4gli.pars.ldatetime)
+
+            except:
+                morning_ok =False
+                print('obtain morning not good')
+
+            # the next sounding will be used either for an afternoon sounding
+            # or for the morning sounding of the next day.
+            wy_strm.find_next()
+            # If the morning is ok, then we try to find a decent afternoon
+            # sounding
+            if morning_ok == 1.:
+                print('MORNING OK!')
+                # we get the current date
+                current_date = dt.date(c4gli.pars.ldatetime.year, \
+                                       c4gli.pars.ldatetime.month, \
+                                       c4gli.pars.ldatetime.day)
+                c4gli_afternoon.clear()
+                print('AFTERNOON PROFILE CLEARED')
+                try:
+                    c4gli_afternoon.get_profile_wyoming(wy_strm)
+                    print('AFTERNOON PROFILE OK')
+
+                    if wy_strm.current is not None:
+                        current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                    else:
+                        # a dummy date: this will be ignored anyway
+                        current_date_afternoon = dt.date(1900,1,1)
+
+                    # we will dump the latest afternoon sounding that fits the
+                    # minimum criteria specified by logic_afternoon
+                    print(current_date,current_date_afternoon)
+                    c4gli_afternoon_for_dump = None
+                    while ((current_date_afternoon == current_date) and \
+                           (wy_strm.current is not None)):
+                        logic_afternoon =dict()
+
+                        logic_afternoon['afternoon'] = \
+                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        # the sounding should have taken place before 1 hours
+                        # before sunset. This is to minimize the chance that a
+                        # stable boundary layer (yielding very low mixed layer
+                        # heights) is formed which can not be represented by
+                        # class.
+                        logic_afternoon['daylight'] = \
+                          ((c4gli_afternoon.pars.ldatetime - \
+                            c4gli_afternoon.pars.lSunset \
+                           ).total_seconds()/3600. <= -1.)
+
+
+                        le3000_afternoon = \
+                            (c4gli_afternoon.air_balloon.z <= 3000.)
+                        logic_afternoon['5measurements'] = \
+                            (np.sum(le3000_afternoon) >= 7) 
+
+                        # we only store the last afternoon sounding that fits these
+                        # minimum criteria
+
+                        afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                        print('logic_afternoon: ',logic_afternoon)
+                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                        if afternoon_ok == 1.:
+                            # # doesn't work :(
+                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                            
+                            # so we just create a new one from the same wyoming profile
+                            c4gli_afternoon_for_dump = class4gl_input()
+                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                        wy_strm.find_next()
+                        c4gli_afternoon.clear()
+                        c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                        if wy_strm.current is not None:
+                            current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                        else:
+                            # a dummy date: this will be ignored anyway
+                            current_date_afternoon = dt.date(1900,1,1)
+
+                        # Only in the case we have a good pair of soundings, we
+                        # dump them to disk
+                    if c4gli_afternoon_for_dump is not None:
+                        c4gli.update(source='pairs',pars={'runtime' : \
+                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                                 c4gli.pars.datetime_daylight).total_seconds())})
+    
+    
+                        print('ALMOST...')
+                        if c4gli.pars.runtime > 3600*4.: # more than 4 hours simulation
+                                
+        
+                            c4gli.get_global_input(globaldata)
+                            print('VERY CLOSE...')
+                            if c4gli.check_source_globaldata() and \
+                                (c4gli.check_source(source='wyoming',\
+                                                   check_only_sections='pars')):
+                                c4gli.dump(fileout)
+                                
+                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                
+                                
+                                # for keyEXP,dictEXP in experiments.items():
+                                #     
+                                #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                #     c4gl = class4gl(c4gli)
+                                #     # c4gl.run()
+                                #     
+                                #     c4gl.dump(c4glfiles[key])
+                                
+                                print('HIT!!!')
+                                one_run = True
+                except:
+                    print('get profile failed')
+                
+    if one_run:
+        #STN.name = STN.name
+        all_records_morning = get_records(pd.DataFrame([STN]),\
+                                      args.path_output,\
+                                      subset='morning',
+                                      refetch_records=True,
+                                      )
+        all_records_afternoon = get_records(pd.DataFrame([STN]),\
+                                      args.path_output,\
+                                      subset='afternoon',
+                                      refetch_records=True,
+                                      )
+    else:
+        os.system('rm '+fnout)
+        os.system('rm '+fnout_afternoon)
+
+    # for c4glfile in c4glfiles:
+    #     c4glfile.close()            
+
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..22efb32
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,4 @@
+# Inside of setup.cfg
+[metadata]
+description-file = README.md
+
diff --git a/setup.py b/setup.py
index 4dcb51d..524bd21 100644
--- a/setup.py
+++ b/setup.py
@@ -1,9 +1,33 @@
 from distutils.core import setup
 
+
+# I followed this tutorial to have both the git repository matched with the pip
+# repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.1dev',
-        packages=['class4gl','bin'],
-        license='GPLv3 licence',
-        long_description=open('README.md').read(),
+        version='0.1.2',
+        license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
+        description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
+        author = 'Hendrik Wouters',                        # Type in your name
+        author_email = 'hendrik.wouters@ugent.be',         # Type in your E-Mail
+        url = 'https://github.com/hendrikwout/class4gl',   # Provide either the link to your github or to your website
+        download_url ='https://github.com/hendrikwout/class4gl/archive/v0.1.tar.gz',
+        # I explain this later on
+        keywords = ['atmospheric boundary layer', 'weather balloons',
+                    'land--atmosphere interactions'],   # Keywords
+        packages=['class4gl'],
+        # packages=find_packages(),
+        install_requires=['beautifulsoup4','pyyaml','pysolar','basemap','xarray'],
+        # long_description=open('README.md').read(),
+        classifiers=[
+                'Development Status :: 4 - Beta',      # Chose either "3 - Alpha", "4
+                #'Intended Audience :: Atmospheric scientists',
+                #'Topic :: modelling of the atmospheric boundary layer',
+                # 'License :: gpl-3.0',   
+                'Programming Language :: Python :: 3',      
+                # 'Programming Language :: Python :: 3.4',
+                # 'Programming Language :: Python :: 3.5',
+                # 'Programming Language :: Python :: 3.6',
+                # 'Programming Language :: Python :: 3.7',
+              ],
 )

From 3090b8c027496a4e0930b58a3228e8ce17698477 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:05:13 +0100
Subject: [PATCH 109/129] readme; changes for release v1.0: multi-processor
 runs without qsub; make vizualization possible without global data.

---
 MANIFEST.in                               |   6 +
 README.md                                 |  52 +-
 class4gl/__init__.py                      |   3 +
 class4gl/class4gl.py                      |  11 +-
 class4gl/interface/interface.py           |  20 +-
 class4gl/interface_functions.py           |   6 +-
 class4gl/interface_multi.py               | 550 ++++++++--------
 class4gl/model.py                         |  30 +-
 class4gl/setup/setup_igra_20181217.py     |   8 +-
 class4gl/simulations/batch_simulations.py | 190 ++++--
 class4gl/simulations/simulations.py       | 752 ++++++++++++----------
 setup.py                                  |   8 +-
 test.png                                  | Bin 92416 -> 0 bytes
 13 files changed, 939 insertions(+), 697 deletions(-)
 delete mode 100644 test.png

diff --git a/MANIFEST.in b/MANIFEST.in
index 174aa24..9ba551d 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,3 +1,9 @@
 recursive-include class4gl *.py
+recursive-include class4gl *.png
+recursive-include class4gl *.pbs
+recursive-exclude class4gl *.pyc
+# include simulations/*
+# include setup/*
+# include processing/*
 
 
diff --git a/README.md b/README.md
index 2d11adc..ad8b3b0 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,50 @@
-# class4gl
-Chemistry Land-surface Atmosphere Soil Slab model (CLASS) | Python version
+[![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/cropped-class4gl_small-1.png)](https://class4gl.eu)
+
+_CLASS4GL_ (Chemistry Land-surface Atmosphere Soil Slab model for Global Studies) is a fast and easy interface to investigate the dynamics of the atmospheric boundary layer from weather balloons worldwide. General info and tutorials for using CLASS4GL are available at class4gl.eu, and video clips about the atmospheric boundary layer physics can be found on the [website of the original CLASS model](classmodel.github.io/).
+
+# Features
+  - _Mine_ appropriate observations from global radio soundings, satellite data, reanalysis and climate models
+  - _Automise_ mass parallel simulations of the atmospheric boundary layer and global sensitivity experiments
+  - _Foster_ a better understanding of land-atmosphere interactions and the drivers of extreme weather globally
+  - _Share_ your data, experiments, and code developments with the research community
+
+# Method
+
+### Description
+
+The framework CLASS4GL is designed to facilitate the investigation of the atmospheric boundary layer evolution in response to different land and atmospheric conditions observed around the world. The core of the platform is the model CLASS that is used to simulate the evolution of the atmospheric boundary layer. Instruction video about the boundary layer processes and how they are considered in the CLASS model can be found as on the [CLASS model website](https://classmodel.github.io/). Observational data from balloons, satellites and reanalysis, are used to constrain and initialize the model. CLASS4GL uses 2 million global balloon soundings from the integrated global radio sounding archive and satellite data from the last 40 years.
+
+### Components
+
+  - A global data module that employs balloon soundings, satellite imagery and reanalysis data
+  - An interface to easily perform multiple simulations of the atmospheric boundary layer in parallel, and multiple batches of global sensitivity experiments
+  - Tools for Pre-and post-processing the data pool of input data and experiments.
+  - A GUI data explorer
+
+The tool is under continuous development, and it can downloaded and installed as described in the tutorials on class4gl.eu/#getstarted.
+
+In case you experience a problem or a bug, please don’t hesitate to contact us class4gl.eu/#contact. You an also open an issue on the github page (https://github.com/hendrikwout/class4gl/issues) . Any feedback will be highly appreciated.
+
+### Data sources
+
+CLASS4GL employs the balloon soundings from the Integrated Global Radiosonde Archive (IGRA) to initialize and validate the CLASS model. The sounding data is supplemented with ancillary data to further constrain the model. Therefore, a default set of gridded global datasets from satellite imagery, reanalysis and and surveys have been used that span a period of 1981–2015. An complete overview of the datasets can be found in the table. However, the default set can be replaced by alternative datasets as long as they are provided in netCDF format.
+
+Schematic overview of CLASS4GL:
+[![N|Solid](https://class4gl.eu//wp-content/uploads/2019/01/image4-1024x794.png)](https://class4gl.eu)
+
+A CLASS4GL data package is available that can be directly used to perform and validate ABL model simulations and sensitivity experiments. The locations of the balloon soundings are performed for different climate regions as shown on the map.
+
+
+These are the 150 stations from IGRA of the reference dataset to perform and validate the ABL model simulations with CLASS4GL (see Sect. 2.2 of the CLASS4GL manuscript). The different climate classes are indicated with the colors according to the Köppen-Geiger climate classification. The markers indicate the locations of the atmospheric profiles from three observation campaigns (ie., HUMPPA, BLLAST and GOAMAZON): [![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/image-1-480x300.png)](https://class4gl.eu)
+
+Data library of CLASS4GL: 
+[![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/image-5-768x492.png)](https://class4gl.eu)
+
+### Reference
+H. Wouters, I. Y. Petrova, C. C. van Heerwaarden, J. Vilà-Guerau de Arellano, A. J. Teuling, J. A. Santanello, V. Meulenberg, D. G. Miralles. A novel framework to investigate atmospheric boundary layer dynamics from balloon soundings worldwide: CLASS4GL v1.0. In preparation.
+
+
+# Get started: 
+see class4gl.eu/#getstarted
+
 
-This is the extension of class to be able to be used with global balloon soundings. 
diff --git a/class4gl/__init__.py b/class4gl/__init__.py
index f9c2212..d192b95 100644
--- a/class4gl/__init__.py
+++ b/class4gl/__init__.py
@@ -1,5 +1,8 @@
 # from . import model,class4gl,interface_multi,data_air,data_global
 from ribtol import *
+from setup import *
+from simulations import *
+from processing import *
 
 __version__ = '0.1.0'
 
diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 2bdf6b4..a0de860 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -835,6 +835,14 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
             air_balloon[column] = air_balloon[column].round(decimal)
             air_ap[column] = air_ap[column].round(decimal)
 
+        # in order to avoid warnings: the ABL values should have the same
+        # rounding as the values profile.
+        dpars['h'] = round(dpars['h'],decimals['z'])
+        dpars['theta'] = round(dpars['theta'],decimals['theta'])
+        dpars['q'] = round(dpars['q'],decimals['q'])
+        dpars['u'] = round(dpars['u'],decimals['u'])
+        dpars['v'] = round(dpars['v'],decimals['v'])
+
         self.update(source='wyoming',\
                     # pars=pars,
                     pars=dpars,\
@@ -1923,12 +1931,9 @@ def dump(self,file,include_input=False,timeseries_only=None):
                 the yaml file, including pars, air_ap, sources etc.
         """
 
-
         if include_input:
             self.input_c4gl.dump(file)
 
-
-
         file.write('---\n')
         index = file.tell()
         file.write('# CLASS4GL input; format version: 0.1\n')
diff --git a/class4gl/interface/interface.py b/class4gl/interface/interface.py
index bb234fc..7ededbd 100644
--- a/class4gl/interface/interface.py
+++ b/class4gl/interface/interface.py
@@ -172,10 +172,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
             # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
             # clearsky = (cc < 0.05)
-            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            # mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
             x, y = obs.values,mod.values
             print(key,len(obs.values))
     
@@ -218,10 +218,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
         # clearsky = (cc < 0.05)
     
-        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        # mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats'].loc[clearsky]['d'+varkey+'dt']
+        mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
     
     
         nbins=40       
@@ -373,7 +373,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         key = list(args.experiments.strip().split(' '))[ikey]
         data_all = pd.DataFrame()
 
-        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_end_obs_stats'].copy())
         tempdatamodstats["source"] = "Soundings"
         tempdatamodstats["source_index"] = "Soundings"
 
@@ -403,7 +403,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
             keylabel = keylabels[ikey]
 
-            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].copy())
             tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
             tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
             tempdatamodstats['STNID']= tempdataini_this.STNID
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index b880ae9..630439e 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -66,7 +66,7 @@ def __prev__(self):
 
 
 #'_afternoon.yaml'
-def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
+def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
     filename = yaml_file.name
     #filename = path_yaml+'/'+format(current_station.name,'05d')+suffix
     #yaml_file = open(filename)
@@ -96,13 +96,13 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='mod'):
     os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start))
 
 
-    if mode =='mod':
+    if mode =='model_output':
         modelout = class4gl()
         modelout.load_yaml_dict(record_dict)
         os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
 
         return modelout
-    elif mode == 'ini':
+    elif mode == 'model_input':
 
  
         # datetimes are incorrectly converted to strings. We need to convert them
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index b5e5c6d..69a2cf9 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -6,10 +6,18 @@
 import sys
 from contextlib import suppress
 from time import sleep
+import copy
+import matplotlib.image as mpimg
 
 
 # sys.path.insert(0, '/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/')
 
+import class4gl
+path_lib = os.path.dirname(class4gl.__file__)
+path_worldmap = path_lib+'/Equirectangular_projection_SW.png'
+img_worldmap= mpimg.imread(path_worldmap)
+
+
 from class4gl import class4gl_input, data_global,class4gl,units
 from interface_functions import *
 # from data_soundings import wyoming
@@ -51,12 +59,12 @@
 os.system('module load Ruby')
 
 class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False,tendencies_revised=False):
+    def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False,tendencies_revised=False):
         """ creates an interactive interface for analysing class4gl experiments
 
         INPUT:
             path_exp : path of the experiment output
-            path_obs : path of the observations 
+            path_forcing : path of the original forcing, which is needed to get the end (afternoon) profiles
             globaldata: global data that is being shown on the map
             obs_filtering: extra data filter considering observation tendencies
                            beyond what the model can capture
@@ -74,7 +82,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
         print(self.obs_filter)
         self.tendencies_revised = tendencies_revised
         self.path_exp = path_exp
-        self.path_obs = path_obs
+        self.path_forcing = path_forcing
         self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
 
         # # get the list of stations
@@ -115,23 +123,23 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
                                            refetch_records=refetch_records
                                            )
         # get its records and load it into the stats frame
-        self.frames['stats']['records_all_stations_mod'] =\
+        self.frames['stats']['records_all_stations_end_mod'] =\
                         get_records(self.frames['stats']['stations'].table,\
                                            self.path_exp,\
-                                           subset='mod',\
+                                           subset='end',\
                                            refetch_records=refetch_records
                                            )
 
-        if self.path_obs is not None:
+        if self.path_forcing is not None:
             # get its records and load it into the stats frame
-            self.frames['stats']['records_all_stations_obs_afternoon'] =\
+            self.frames['stats']['records_all_stations_end_obs'] =\
                             get_records(self.frames['stats']['stations'].table,\
-                                               self.path_obs,\
-                                               subset='afternoon',\
+                                               self.path_forcing,\
+                                               subset='end',\
                                                refetch_records=refetch_records
                                                )
 
-        self.frames['stats']['records_all_stations_mod'].index = \
+        self.frames['stats']['records_all_stations_end_mod'].index = \
             self.frames['stats']['records_all_stations_ini'].index 
 
         
@@ -141,46 +149,46 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
         self.frames['stats']['records_all_stations_ini']['dates'] = \
             self.frames['stats']['records_all_stations_ini']['ldatetime'].dt.date
 
-        if self.path_obs is not None:
-            self.frames['stats']['records_all_stations_obs_afternoon']['dates'] = \
-                self.frames['stats']['records_all_stations_obs_afternoon']['ldatetime'].dt.date
+        if self.path_forcing is not None:
+            self.frames['stats']['records_all_stations_end_obs']['dates'] = \
+                self.frames['stats']['records_all_stations_end_obs']['ldatetime'].dt.date
 
-            self.frames['stats']['records_all_stations_obs_afternoon'].set_index(['STNID','dates'],inplace=True)
+            self.frames['stats']['records_all_stations_end_obs'].set_index(['STNID','dates'],inplace=True)
 
 
             ini_index_dates = self.frames['stats']['records_all_stations_ini'].set_index(['STNID','dates']).index
 
-            self.frames['stats']['records_all_stations_obs_afternoon'] = \
-                self.frames['stats']['records_all_stations_obs_afternoon'].loc[ini_index_dates]
+            self.frames['stats']['records_all_stations_end_obs'] = \
+                self.frames['stats']['records_all_stations_end_obs'].loc[ini_index_dates]
 
-            self.frames['stats']['records_all_stations_obs_afternoon'].index = \
+            self.frames['stats']['records_all_stations_end_obs'].index = \
                 self.frames['stats']['records_all_stations_ini'].index 
 
             self.frames['stats']['viewkeys'] = ['h','theta','q']
             print('Calculating table statistics')
 
             if self.tendencies_revised:
-                self.frames['stats']['records_all_stations_mod_stats'] = \
-                        tendencies_rev(self.frames['stats']['records_all_stations_mod'],\
+                self.frames['stats']['records_all_stations_end_mod_stats'] = \
+                        tendencies_rev(self.frames['stats']['records_all_stations_end_mod'],\
                                            self.frames['stats']['records_all_stations_ini'],\
                                            self.frames['stats']['viewkeys']\
                                   )
-                self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                        tendencies_rev(self.frames['stats']['records_all_stations_obs_afternoon'],\
+                self.frames['stats']['records_all_stations_end_obs_stats'] = \
+                        tendencies_rev(self.frames['stats']['records_all_stations_end_obs'],\
                                            self.frames['stats']['records_all_stations_ini'],\
                                            self.frames['stats']['viewkeys']\
                                   )
 
             else:
-                self.frames['stats']['records_all_stations_mod_stats'] = \
-                        tendencies(self.frames['stats']['records_all_stations_mod'],\
-                                   self.frames['stats']['records_all_stations_obs_afternoon'],\
+                self.frames['stats']['records_all_stations_end_mod_stats'] = \
+                        tendencies(self.frames['stats']['records_all_stations_end_mod'],\
+                                   self.frames['stats']['records_all_stations_end_obs'],\
                                    self.frames['stats']['records_all_stations_ini'],\
                                    self.frames['stats']['viewkeys']\
                                   )
-                self.frames['stats']['records_all_stations_obs_afternoon_stats'] = \
-                        tendencies(self.frames['stats']['records_all_stations_obs_afternoon'],\
-                                   self.frames['stats']['records_all_stations_obs_afternoon'],\
+                self.frames['stats']['records_all_stations_end_obs_stats'] = \
+                        tendencies(self.frames['stats']['records_all_stations_end_obs'],\
+                                   self.frames['stats']['records_all_stations_end_obs'],\
                                    self.frames['stats']['records_all_stations_ini'],\
                                    self.frames['stats']['viewkeys']\
                                   )
@@ -213,18 +221,18 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
         # 
         # 
         # \
-        #        self.frames['stats']['records_all_stations_mod'], \
+        #        self.frames['stats']['records_all_stations_end_mod'], \
 
 
 
-        # self.frames['stats']['records_all_stations_mod_stats_stdrel'] = \
-        #        stdrel(mod = self.frames['stats']['records_all_stations_mod_stats'], \
-        #               obs = self.frames['stats']['records_all_stations_obs_afternoon_stats'], \
+        # self.frames['stats']['records_all_stations_end_mod_stats_stdrel'] = \
+        #        stdrel(mod = self.frames['stats']['records_all_stations_end_mod_stats'], \
+        #               obs = self.frames['stats']['records_all_stations_end_obs_stats'], \
         #               columns = [ 'd'+key+'dt' for key in \
         #                           self.frames['stats']['viewkeys']], \
         #              )
 
-        # self.frames['stats']['records_all_stations_obs_afternoon_stats_stdrel'] = \
+        # self.frames['stats']['records_all_stations_end_obs_stats_stdrel'] = \
         #        stdrel(mod = self.frames['stats']['records_all_stations_ini'], \
         #               obs = self.frames['stats']['records_all_stations_ini'], \
         #               columns = self.frames['stats']['viewkeys'], \
@@ -232,42 +240,42 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
 
         
 
-        if self.path_obs is not None:
+        if self.path_forcing is not None:
             print('filtering pathological data')
-            indextype = self.frames['stats']['records_all_stations_mod_stats'].index.names
+            indextype = self.frames['stats']['records_all_stations_end_mod_stats'].index.names
             # some observational sounding still seem problematic, which needs to be
             # investigated. In the meantime, we filter them
 
             print('hello',self.obs_filter)
-            print ((self.path_obs is not None) and (self.obs_filter))
-            if ((self.path_obs is not None) and (self.obs_filter)) is True:
+            print ((self.path_forcing is not None) and (self.obs_filter))
+            if ((self.path_forcing is not None) and (self.obs_filter)) is True:
                 print('hallohallo')
-            if ((self.path_obs is not None) and (self.obs_filter)) is True:
+            if ((self.path_forcing is not None) and (self.obs_filter)) is True:
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt >  0.250) & 
-                         #(self.frames['stats']['records_all_stations_mod_stats'].dthetadt >  0.25000) & 
-                         #(self.frames['stats']['records_all_stations_mod_stats'].dthetadt <  1.8000) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt <  1.8000) & 
-                         #(self.frames['stats']['records_all_stations_mod_stats'].dhdt >  50.0000) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt >  40.0000) & 
-                         #(self.frames['stats']['records_all_stations_mod_stats'].dhdt <  350.) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dhdt <  400.) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt >  -.00055) & 
-                         #(self.frames['stats']['records_all_stations_mod_stats'].dqdt >  -.00055) & 
-                         (self.frames['stats']['records_all_stations_obs_afternoon_stats'].dqdt <  .0003) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.250) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  400.) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.00055) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.00055) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
 
                          # filter 'extreme' model output -> should be investigated!
-                         (self.frames['stats']['records_all_stations_mod_stats'].dqdt <  .0006) & 
-                         (self.frames['stats']['records_all_stations_mod_stats'].dqdt >  -.0006) & 
-                         (self.frames['stats']['records_all_stations_mod_stats'].dthetadt >  .2) & 
-                         (self.frames['stats']['records_all_stations_mod_stats'].dthetadt <  2.) & 
-                         # (self.frames['stats']['records_all_stations_mod_stats'].dqdt <  .0003) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
+                         # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
-                         ~np.isnan(self.frames['stats']['records_all_stations_mod_stats'].dthetadt) & 
-                         ~np.isnan(self.frames['stats']['records_all_stations_obs_afternoon_stats'].dthetadt))
+                         ~np.isnan(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt) & 
+                         ~np.isnan(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt))
 
                 for key in self.frames['stats'].keys():
                     if (type(self.frames['stats'][key]) == pd.DataFrame) and \
@@ -275,7 +283,7 @@ def __init__(self,path_exp,path_obs=None,globaldata=None,refetch_records=False,r
                         self.frames['stats'][key] = self.frames['stats'][key][valid]
                 print("WARNING WARNING!: "+ str(len(valid) - np.sum(valid))+' soundings are filtered')
 
-        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_mod'].index
+        self.frames['stats']['records_all_stations_index'] = self.frames['stats']['records_all_stations_end_mod'].index
 
 
         print("filtering stations from interface that have no records")
@@ -355,9 +363,9 @@ def update_station(self):
 
         # create the value table of the records of the current station
         tab_suffixes = \
-                ['_mod','_ini','_ini_pct']
-        if self.path_obs is not None:
-            tab_suffixes=tab_suffixes+['_obs_afternoon','_mod_stats','_obs_afternoon_stats']
+                ['_end_mod','_ini','_ini_pct']
+        if self.path_forcing is not None:
+            tab_suffixes=tab_suffixes+['_end_obs','_end_mod_stats','_end_obs_stats']
 
         for tab_suffix in tab_suffixes:
             self.frames['stats']['records_current_station'+tab_suffix] = \
@@ -365,11 +373,11 @@ def update_station(self):
 
         # go to first record of current station
         self.frames['stats']['records_iterator'] = \
-                        records_iterator(self.frames['stats']['records_current_station_mod'])
+                        records_iterator(self.frames['stats']['records_current_station_end_mod'])
         (self.frames['stats']['STNID'] , \
         self.frames['stats']['current_record_chunk'] , \
         self.frames['stats']['current_record_index']) , \
-        self.frames['stats']['current_record_mod'] = \
+        self.frames['stats']['current_record_end_mod'] = \
                         self.frames['stats']['records_iterator'].__next__()
 
         for key in self.frames['stats'].keys():
@@ -382,24 +390,24 @@ def update_station(self):
         self.frames['profiles']['current_station_file_ini'] = \
             open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
 
-        if 'current_station_file_mod' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_mod'].close()
-        self.frames['profiles']['current_station_file_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_afternoon'].close()
-        if self.path_obs is not None:
-            self.frames['profiles']['current_station_file_afternoon'] = \
-                open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+        if 'current_station_file_end_mod' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_end_mod'].close()
+        self.frames['profiles']['current_station_file_end_mod'] = \
+            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
+        if 'current_station_file_end' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_end'].close()
+        if self.path_forcing is not None:
+            self.frames['profiles']['current_station_file_end'] = \
+                open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
         # for the profiles we make a distinct record iterator, so that the
         # stats iterator can move independently
         self.frames['profiles']['records_iterator'] = \
-                        records_iterator(self.frames['profiles']['records_current_station_mod'])
+                        records_iterator(self.frames['profiles']['records_current_station_end_mod'])
         (self.frames['profiles']['STNID'] , \
         self.frames['profiles']['current_record_chunk'] , \
         self.frames['profiles']['current_record_index']) , \
-        self.frames['profiles']['current_record_mod'] = \
+        self.frames['profiles']['current_record_end_mod'] = \
                         self.frames['profiles']['records_iterator'].__next__()
 
 
@@ -416,16 +424,16 @@ def next_record(self,event=None,jump=1):
             (self.frames['profiles']['STNID'] , \
             self.frames['profiles']['current_record_chunk'] , \
             self.frames['profiles']['current_record_index']) , \
-            self.frames['profiles']['current_record_mod'] = \
+            self.frames['profiles']['current_record_end_mod'] = \
                       self.frames['profiles']['records_iterator'].__next__(jump)
         # except (StopIteration):
         #     self.frames['profiles']['records_iterator'].close()
         #     del( self.frames['profiles']['records_iterator'])
         #     self.frames['profiles']['records_iterator'] = \
-        #                 self.frames['profiles']['records_current_station_mod'].iterrows()
+        #                 self.frames['profiles']['records_current_station_end_mod'].iterrows()
         #     (self.frames['profiles']['STNID'] , \
         #     self.frames['profiles']['current_record_index']) , \
-        #     self.frames['profiles']['current_record_mod'] = \
+        #     self.frames['profiles']['current_record_end_mod'] = \
         #                     self.frames['profiles']['records_iterator'].__next__()
 
         for key in self.frames['profiles'].keys():
@@ -444,16 +452,16 @@ def next_record(self,event=None,jump=1):
             self.frames['profiles']['current_station_file_ini'] = \
                 open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
 
-            if 'current_station_file_mod' in self.frames['profiles'].keys():
-                self.frames['profiles']['current_station_file_mod'].close()
-            self.frames['profiles']['current_station_file_mod'] = \
-                open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
+            if 'current_station_file_end_mod' in self.frames['profiles'].keys():
+                self.frames['profiles']['current_station_file_end_mod'].close()
+            self.frames['profiles']['current_station_file_end_mod'] = \
+                open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
 
-            if self.path_obs is not None:
-                if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                    self.frames['profiles']['current_station_file_afternoon'].close()
-                self.frames['profiles']['current_station_file_afternoon'] = \
-                    open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+            if self.path_forcing is not None:
+                if 'current_station_file_end' in self.frames['profiles'].keys():
+                    self.frames['profiles']['current_station_file_end'].close()
+                self.frames['profiles']['current_station_file_end'] = \
+                    open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
         self.update_record()
 
@@ -466,20 +474,20 @@ def update_record(self):
                   (self.frames['profiles']['STNID'] , \
                   self.frames['profiles']['current_record_chunk'],\
                   self.frames['profiles']['current_record_index'])]
-        if self.path_obs is not None:
-            self.frames['profiles']['current_record_obs_afternoon'] =  \
-                self.frames['profiles']['records_current_station_obs_afternoon'].loc[\
+        if self.path_forcing is not None:
+            self.frames['profiles']['current_record_end_obs'] =  \
+                self.frames['profiles']['records_current_station_end_obs'].loc[\
                       (self.frames['profiles']['STNID'] , \
                       self.frames['profiles']['current_record_chunk'] , \
                       self.frames['profiles']['current_record_index'])]
 
-            self.frames['profiles']['current_record_mod_stats'] = \
-                    self.frames['profiles']['records_all_stations_mod_stats'].loc[(\
+            self.frames['profiles']['current_record_end_mod_stats'] = \
+                    self.frames['profiles']['records_all_stations_end_mod_stats'].loc[(\
                         self.frames['profiles']['STNID'], \
                         self.frames['profiles']['current_record_chunk'], \
                         self.frames['profiles']['current_record_index'])]
-            self.frames['profiles']['current_record_obs_afternoon_stats'] = \
-                    self.frames['profiles']['records_all_stations_obs_afternoon_stats'].loc[(\
+            self.frames['profiles']['current_record_end_obs_stats'] = \
+                    self.frames['profiles']['records_all_stations_end_obs_stats'].loc[(\
                         self.frames['profiles']['STNID'],\
                         self.frames['profiles']['current_record_chunk'],\
                         self.frames['profiles']['current_record_index'])]
@@ -496,15 +504,15 @@ def update_record(self):
 
         # select first 
         #self.frames['profiles']['current_record_index'], \
-        #self.frames['profiles']['record_yaml_mod'] = \
+        #self.frames['profiles']['record_yaml_end_mod'] = \
         #   get_record_yaml(self.frames['profiles']['current_station']['filename'],\
         #                   self.frames['stats']['current_record_index'])
-        self.frames['profiles']['record_yaml_mod'] = \
+        self.frames['profiles']['record_yaml_end_mod'] = \
            get_record_yaml(
-               self.frames['profiles']['current_station_file_mod'], \
-               self.frames['profiles']['current_record_mod'].index_start,
-               self.frames['profiles']['current_record_mod'].index_end,
-               mode='mod')
+               self.frames['profiles']['current_station_file_end_mod'], \
+               self.frames['profiles']['current_record_end_mod'].index_start,
+               self.frames['profiles']['current_record_end_mod'].index_end,
+               mode='model_output')
                                 
         record_ini = self.frames['profiles']['records_all_stations_ini'].loc[
                        (self.frames['stats']['STNID'] , \
@@ -516,20 +524,20 @@ def update_record(self):
                self.frames['profiles']['current_station_file_ini'], \
                record_ini.index_start,
                record_ini.index_end,
-                mode='ini')
+                mode='model_input')
 
-        if self.path_obs is not None:
-            record_afternoon = self.frames['profiles']['records_all_stations_obs_afternoon'].loc[
+        if self.path_forcing is not None:
+            record_end = self.frames['profiles']['records_all_stations_end_obs'].loc[
                            (self.frames['stats']['STNID'] , \
                             self.frames['stats']['current_record_chunk'] , \
                             self.frames['stats']['current_record_index'])]
 
-            self.frames['profiles']['record_yaml_obs_afternoon'] = \
+            self.frames['profiles']['record_yaml_end_obs'] = \
                get_record_yaml(
-                   self.frames['profiles']['current_station_file_afternoon'], \
-                   record_afternoon.index_start,
-                   record_afternoon.index_end,
-                    mode='ini')
+                   self.frames['profiles']['current_station_file_end'], \
+                   record_end.index_start,
+                   record_end.index_end,
+                    mode='model_input')
 
 
         key = self.frames['worldmap']['inputkey']
@@ -546,7 +554,7 @@ def update_record(self):
         else:
             if "fig" in self.__dict__.keys():
                 self.refresh_plot_interface(only=['stats_lightupdate',
-                                                  'worldmap_stations',
+                                                  'worldmap',
                                                   'profiles'])
 
     def abline(self,slope, intercept,axis):
@@ -570,9 +578,9 @@ def plot(self):
         btns = {} #buttons
 
         # frames, which sets attributes for a group of axes, buttens, 
-        if self.path_obs is not None:
+        if self.path_forcing is not None:
 
-            for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_mod_stats'].columns)):
+            for ikey,key in enumerate(list(self.frames['stats']['records_all_stations_end_mod_stats'].columns)):
                 label = 'stats_'+str(key)
                 axes[label] = fig.add_subplot(\
                                 len(self.frames['stats']['viewkeys']),\
@@ -640,9 +648,11 @@ def plot(self):
         axes[label].lat = None
         axes[label].lon = None
 
-        label = 'worldmap_colorbar'
-        axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
-        axes[label].fields = {}
+        
+        if self.globaldata is not None:
+            label = 'worldmap_colorbar'
+            axes[label] = fig.add_axes([0.25,0.44,0.40,0.05])
+            axes[label].fields = {}
 
         # we make a overlying axes for the animations on the map, so that we don't need to redraw the whole map over and over again
         label = 'worldmap_stations'
@@ -661,7 +671,10 @@ def plot(self):
         buttons_upper = 0.28
         buttons_left = 0.25
 
-        button_types = ['dataset','datetime','level','station','record']
+        if self.globaldata is not None:
+            button_types = ['datetime','level','station','record']
+        else:
+            button_types = ['dataset','station','record']
         
         for ibutton_type,button_type in enumerate(button_types):
             label='bprev'+button_type
@@ -671,8 +684,13 @@ def plot(self):
                 button_width,\
                 button_height\
                                                      ])
-            btns[label] = Button(axes[label], 'Previous '+button_type)
-            btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+            if button_type !='dataset':
+                btns[label] = Button(axes[label], 'Previous '+button_type)
+                btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+            else:
+                btns[label] = Button(axes[label], 'Previous input var')
+                btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+
 
             label='bnext'+button_type
             axes[label] = fig.add_axes([
@@ -681,8 +699,12 @@ def plot(self):
                 button_width,\
                 button_height\
                                                      ])
-            btns[label] = Button(axes[label], 'Next '+button_type)
-            btns[label].on_clicked(getattr(self, 'next_'+button_type))
+            if button_type !='dataset':
+                btns[label] = Button(axes[label], 'Next '+button_type)
+                btns[label].on_clicked(getattr(self, 'next_'+button_type))
+            else:
+                btns[label] = Button(axes[label], 'Next input var')
+                btns[label].on_clicked(getattr(self, 'next_'+button_type))
 
         
         # label = 'bprev_dataset'
@@ -799,7 +821,7 @@ def plot(self):
     #         current_yamlgen.close()
     #         current_yamlgen = yaml.load_all(current_file)
     #         current_file.seek(current_tell)
-    #         current_record_mod = current_yamlgen.__next__()
+    #         current_record_end_mod = current_yamlgen.__next__()
     #     current_yamlgen.close()
 
     #     return records
@@ -920,8 +942,17 @@ def sel_dataset(self,inputkey):
             'after')# get nearest datetime of the current dataset to the profile
 
         print('seldata0')
+
         if 'level' not in self.frames['worldmap'].keys():
-            levels = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev']
+
+            if self.globaldata is not None:
+                if 'lev' in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims):
+                    levels = self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev']
+                else:
+                    levels = np.array([0])
+            else:
+                levels = np.array([0])
+
             self.frames['worldmap']['level'] = np.max(levels)
             print('seldata1')
 
@@ -936,27 +967,28 @@ def sel_dataset(self,inputkey):
             print('seldata3')
 
 
-        print('seldata4')
+            print('seldata4')
         self.sel_level(self.frames['worldmap']['level'])
 
 
 
     def sel_level(self,level):
 
-        if 'lev' not in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims):
-            raise ValueError('lev dimension not in dataset '+self.frames['worldmap']['inputkey'])
+        # if 'lev' not in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims):
+        #     raise ValueError('lev dimension not in dataset '+self.frames['worldmap']['inputkey'])
 
         print('seldata5')
 
-
-        if level > (np.max(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
-            raise ValueError('Level '+str(level)+' exceed those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
-        if level < (np.min(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
-            raise ValueError('Level '+str(level)+' is lower than those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
-        print('seldata6')
-        self.frames['worldmap']['level'] = level
-
-        print(level)
+        if self.globaldata is not None:
+            if 'lev' in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims):
+                if level > (np.max(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
+                    raise ValueError('Level '+str(level)+' exceed those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
+                if level < (np.min(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page['lev'])):
+                    raise ValueError('Level '+str(level)+' is lower than those of the current dataset: '+str(self.globaldata.datasets[frames['worldmap']['inputkey']].page['lev']))
+                print('seldata6')
+                self.frames['worldmap']['level'] = level
+
+            print(level)
         if "fig" in self.__dict__.keys():
             self.refresh_plot_interface(only=['worldmap','stats_lightupdate','stats_colorbar']) 
 
@@ -1086,13 +1118,22 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                 # x,y = self.gmap(lons,lats)
                 # #self.cont_map = self.axmap.contourf(x,y,field.T,cmap=gmapcm)
                 # self.cont_map = self.axmap.pcolormesh(x,y,field.T,cmap=gmapcm)
+        else:
+            # simplified version for only showing a simple image
+            label = 'worldmap'
+            axes[label].lat = np.arange(-90.,91.,1.)[::-1]
+            axes[label].lon = np.arange(-180.,181.,1.)[:]
+            axes[label].fields[label] = axes[label].imshow(img_worldmap)
+            axes[label].axis('off')
+
+
 
-        if (self.path_obs is not None) and \
+        if (self.path_forcing is not None) and \
            (self.frames['worldmap']['inputkey'] in self.frames['stats']['records_all_stations_ini_pct'].keys()) and \
-           (self.path_obs is not None) and \
+           (self.path_forcing is not None) and \
            ((only is None) or ('stats' in only) or ('stats_lightupdate' in only)):
 
-            statskeys_out = list(self.frames['stats']['records_all_stations_mod_stats'].columns)
+            statskeys_out = list(self.frames['stats']['records_all_stations_end_mod_stats'].columns)
             store_xlim = {}
             store_ylim = {}
             for ikey, key in enumerate(statskeys_out):
@@ -1107,12 +1148,12 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             key = 'dthetadt'
             x = self.frames['stats']['records_all_stations_ini']['datetime']
             #print(x)
-            y = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
+            y = self.frames['stats']['records_all_stations_end_obs_stats'][key]
             #print(y)
             z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
             #print(z)
 
-            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_mod'])))
+            alpha_cloud_pixels = 1./(1.+1./(0.15 * 10000. / len(self.frames['stats']['records_all_stations_end_mod'])))
             self.axes[label].data[label] = self.axes[label].scatter(x.values,
                                                                     y.values,
                                                                     c=z.values,
@@ -1124,13 +1165,13 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
             
             x = self.frames['stats']['records_current_station_ini']['datetime']
-            y = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
+            y = self.frames['stats']['records_current_station_end_obs_stats'][key]
             z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
             self.axes[label].data[label+'_current_station_hover'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=5,picker=5,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
 
 
             x = self.frames['profiles']['records_current_station_ini']['datetime']
-            y = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
+            y = self.frames['profiles']['records_current_station_end_obs_stats'][key]
             z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
 
             self.axes[label].data[label+'_current_station'] = self.axes[label].scatter(x.values,y.values,c=z.values,cmap=self.statsviewcmap,s=20,picker=20,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.8)
@@ -1141,8 +1182,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             for ikey, key in enumerate(statskeys_out):
 
                 # show data of all stations
-                x = self.frames['stats']['records_all_stations_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_all_stations_mod_stats'][key]
+                x = self.frames['stats']['records_all_stations_end_obs_stats'][key]
+                y = self.frames['stats']['records_all_stations_end_mod_stats'][key]
                 z = self.frames['stats']['records_all_stations_ini_pct'][self.frames['worldmap']['inputkey'] ]
                 qvalmax = x.quantile(0.999)
                 qvalmin = x.quantile(0.001)
@@ -1162,16 +1203,16 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                     self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
                          self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.4,lw=4)
 
-                x = self.frames['stats']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['stats']['records_current_station_mod_stats'][key]
+                x = self.frames['stats']['records_current_station_end_obs_stats'][key]
+                y = self.frames['stats']['records_current_station_end_mod_stats'][key]
                 z = self.frames['stats']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
                 self.axes['stats_'+key].data['stats_'+key+'_current_station_hover'] = \
                        self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
                                 cmap=self.statsviewcmap,\
                                 s=10,picker=10,label=key,vmin=0.,vmax=1.,edgecolor='k',linewidth=0.3)
 
-                x = self.frames['profiles']['records_current_station_obs_afternoon_stats'][key]
-                y = self.frames['profiles']['records_current_station_mod_stats'][key]
+                x = self.frames['profiles']['records_current_station_end_obs_stats'][key]
+                y = self.frames['profiles']['records_current_station_end_mod_stats'][key]
                 z = self.frames['profiles']['records_current_station_ini_pct'][self.frames['worldmap']['inputkey'] ]
                 self.axes['stats_'+key].data['stats_'+key+'_current_station'] = \
                        self.axes['stats_'+key].scatter(x.values,y.values, c=z.values,\
@@ -1183,8 +1224,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                     self.axes['stats_'+key].data['stats_'+key+'_fit'] = \
                          self.axes['stats_'+key].plot(x, fit[0] * x + fit[1], color='k',alpha=0.8,lw=3)
 
-                x = self.frames['stats']['current_record_obs_afternoon_stats'][key]
-                y = self.frames['stats']['current_record_mod_stats'][key]
+                x = self.frames['stats']['current_record_end_obs_stats'][key]
+                y = self.frames['stats']['current_record_end_mod_stats'][key]
                 z = self.frames['stats']['current_record_ini_pct'][self.frames['worldmap']['inputkey'] ]
 
                 text = 'EXT: '+ format(x,'2.4f')+ ', MOD: ' + format(y,'2.4f')
@@ -1204,9 +1245,9 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                 # axes['stats_'+key].set_title('relative deviation per station of '+ key)
                 self.axes['stats_'+key].set_title(key+ ' ['+self.units[key]+']')
                 # # highlight data for curent station
-                # self.frames['stats']['records_all_stations_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
+                # self.frames['stats']['records_all_stations_end_mod_stats'].iloc[self.frames['stats']['records_all_stations_index'].get_level_values('STNID') == self.frames['stats']['current_station'].name]
 
-                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_mod'],'2.4f')
+                #text = 'EXT: '+format(seltablestatsstdrel_statannotate[key+'_ext'],'2.4f')+ ', MOD: '+format(seltablestatsstdrel_statannotate[key+'_end_mod'],'2.4f')
 
                 if ikey == len(statskeys_out)-1:
                     self.axes['stats_'+key].set_xlabel('external')
@@ -1240,10 +1281,9 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
         #print('r1')
         if (only is None) or ('worldmap' in only) or ('worldmap_stations' in only):
-            #print('r2')
             label = 'worldmap_stations'
             axes[label].clear()
-            
+            #print('r2')
             stations = self.frames['worldmap']['stations'].table
             globaldata = self.globaldata
             
@@ -1261,6 +1301,7 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                     xlist.append(x)
                     ylist.append(y)
                 #picker is needed to make it clickable (pick_event)
+                print(label)
                 axes[label].data[label] = axes[label].scatter(xlist,ylist,
                                                               c='r', s=15,
                                                               picker = 15,
@@ -1306,7 +1347,7 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                     text = 'STNID: '+ format(STNID,'10.0f') + \
                             ', LAT: '+format(STN['latitude'],'3.3f')+ \
                             ', LON: '+format(STN['longitude'],'3.3f')+ \
-                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_mod'].shape[0]) \
+                            ', #SOUNDINGS: '+str(self.frames['stats']['records_current_station_end_mod'].shape[0]) \
 
                             #+', VAL: '+format(VAL,'.3e')
 
@@ -1332,8 +1373,8 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
                     # #pos = sc.get_offsets()[ind["ind"][0]]
                     # 
-                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_mod'])
-                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_mod'])
+                    # axes[label.data[label+'statannotate'].xy = (seltablestatsstdrel_statannotate[key+'_ext'],seltablestatsstdrel_statannotate[key+'_end_mod'])
+                    # text = 'STN: '+str(int(axes['statsview0'].focus['STNID']))+', DT: '+str(axes['statsview0'].focus['DT'])+', EXT: '+str(seltablestatsstdrel_statannotate[key+'_ext'])+', MOD: '+str(seltablestatsstdrel_statannotate[key+'_end_mod'])
                     # axes[label].data[label+'statannotate'].set_text(text)
                     #axes[label].data[label+'statannotate'].get_bbox_patch().set_facecolor(statsviewcmap(seltablestatspct_statannotate[cmapkey]))
                     # axes[label].data[label+'statannotate'].get_bbox_patch().set_alpha(0.4)
@@ -1360,7 +1401,7 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                 self.frames['profiles']['record_yaml_ini'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
                 # +\
                 # ' -> '+ \
-                # self.frames['profiles']['record_yaml_obs_afternoon'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
+                # self.frames['profiles']['record_yaml_end_obs'].pars.datetime.strftime("%Y/%m/%d %H:%M"))
             
             
             
@@ -1373,12 +1414,12 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             # #axes[label].set_title(self.morning_sounding.datetime.strftime("%Y/%m/%d %H:%M") + ' -> '+self.evening_sounding.datetime.strftime("%Y/%m/%d %H:%M"))
             # 
             #print(self.frames['profiles']['record_yaml_ini'].pars.h)
-            #print(self.frames['profiles']['record_yaml_obs_afternoon'].pars.h)
-            #print(self.frames['profiles']['record_yaml_mod'].out['h'].values[-1])
+            #print(self.frames['profiles']['record_yaml_end_obs'].pars.h)
+            #print(self.frames['profiles']['record_yaml_end_mod'].out['h'].values[-1])
             hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                           self.frames['profiles']['record_yaml_mod'].out.h[-1]])
-            if self.path_obs is not None:
-                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_obs_afternoon'].pars.h])
+                           self.frames['profiles']['record_yaml_end_mod'].out.h[-1]])
+            if self.path_forcing is not None:
+                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_end_obs'].pars.h])
 
 
                 zidxmax = int(np.where((self.frames['profiles']['record_yaml_ini'].air_balloon.z.values
@@ -1405,43 +1446,43 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
 
 
             #print('r15')
-            if self.path_obs is not None:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values
+            if self.path_forcing is not None:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values
                                     < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values)))
                 zco = range(zidxmax)
 
                               
-                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_obs'].air_balloon.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values[zco],"r*", \
                                  label="obs "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 self.frames['profiles']['record_yaml_end_obs'].pars.ldatetime.strftime("%H:%M")\
                                  +'LT')
 
                 #print('r16')
 
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values)))
                 zco = range(zidxmax)
 
-                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_obs'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values[zco],"r:", \
                                  label="fit "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 self.frames['profiles']['record_yaml_end_obs'].pars.ldatetime.strftime("%H:%M")\
                                  +'LT')
 
             #print('r17')
-            #print(self.frames['profiles']['record_yaml_mod'].air_ap.z)
+            #print(self.frames['profiles']['record_yaml_end_mod'].air_ap.z)
             #print(hmax)
-            valid_mod = len(self.frames['profiles']['record_yaml_mod'].air_ap.z)>= 4
+            valid_mod = len(self.frames['profiles']['record_yaml_end_mod'].air_ap.z)>= 4
             if valid_mod:
 
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values)))
                 zco = range(zidxmax)
 
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.theta.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_mod'].air_ap.theta.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values[zco],"r-", \
                                  label="mod "+\
                                  (self.frames['profiles']['record_yaml_ini'].pars.ldatetime
                                  +dt.timedelta(seconds=self.frames['profiles']['record_yaml_ini'].pars.runtime)).strftime("%H:%M")\
@@ -1469,12 +1510,12 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             # 
             if valid_mod:
                 hmax = np.nanmax([self.frames['profiles']['record_yaml_ini'].pars.h,\
-                               self.frames['profiles']['record_yaml_mod'].out.h[-1]])
+                               self.frames['profiles']['record_yaml_end_mod'].out.h[-1]])
             else:
                 hmax = self.frames['profiles']['record_yaml_ini'].pars.h
 
-            if self.path_obs is not None:
-                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_obs_afternoon'].pars.h])
+            if self.path_forcing is not None:
+                hmax = np.nanmax([hmax,self.frames['profiles']['record_yaml_end_obs'].pars.h])
             # 
             #print('r20')
 
@@ -1500,38 +1541,38 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
                              self.frames['profiles']['record_yaml_ini'].pars.ldatetime.strftime("%H:%M")\
                              +'LT')
 
-            if self.path_obs is not None:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values)))
+            if self.path_forcing is not None:
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values)))
                 zco = range(zidxmax)
 
 
-                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_balloon.z.values[zco],"r*", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_obs'].air_balloon.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_obs'].air_balloon.z.values[zco],"r*", \
                                  label="obs "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 self.frames['profiles']['record_yaml_end_obs'].pars.ldatetime.strftime("%H:%M")\
                                  +'LT')
 
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values)))
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values)))
                 zco = range(zidxmax)
 
                 #print('r23')
-                axes[label].plot(self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].air_ap.z.values[zco],"r:", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_obs'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_obs'].air_ap.z.values[zco],"r:", \
                                  label="fit "+\
-                                 self.frames['profiles']['record_yaml_obs_afternoon'].pars.ldatetime.strftime("%H:%M")\
+                                 self.frames['profiles']['record_yaml_end_obs'].pars.ldatetime.strftime("%H:%M")\
                                  +'LT')
 
             #print('r24')
             if valid_mod:
-                zidxmax = int(np.where((self.frames['profiles']['record_yaml_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
-                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_mod'].air_ap.z.values)))
+                zidxmax = int(np.where((self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values < 2.*hmax))[0][-1])+2
+                zidxmax = np.min((zidxmax,len(self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values)))
                 zco = range(zidxmax)
-                axes[label].plot(self.frames['profiles']['record_yaml_mod'].air_ap.q.values[zco], \
-                                 self.frames['profiles']['record_yaml_mod'].air_ap.z.values[zco],"r-", \
+                axes[label].plot(self.frames['profiles']['record_yaml_end_mod'].air_ap.q.values[zco], \
+                                 self.frames['profiles']['record_yaml_end_mod'].air_ap.z.values[zco],"r-", \
                                  label="fit ")#+\
-                             #self.frames['profiles']['record_yaml_mod'].pars.ldatetime.strftime("%H:%M")\
+                             #self.frames['profiles']['record_yaml_end_mod'].pars.ldatetime.strftime("%H:%M")\
                              #+'LT')
             #print('r25')
             #axes[label].legend()
@@ -1577,10 +1618,10 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
  
 
             #print('r26')
-            time = self.frames['profiles']['record_yaml_mod'].out.time
+            time = self.frames['profiles']['record_yaml_end_mod'].out.time
             for ilabel,label in enumerate(['h','theta','q']):
                 axes["out:"+label].clear()
-                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_mod'].out.__dict__[label],label=label)
+                axes["out:"+label].plot(time,self.frames['profiles']['record_yaml_end_mod'].out.__dict__[label],label=label)
                 axes["out:"+label].set_ylabel(label)
                 if ilabel == 2:
                     axes["out:"+label].set_xlabel('local sun time [h]')
@@ -1589,11 +1630,11 @@ def refresh_plot_interface(self,only=None,statsnewdata=True,**args):
             label = 'SEB'
             axes[label].clear()
             
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Swin - self.frames['profiles']['record_yaml_mod'].out.Swout,label='Sw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.H,label='H')
-            axes[label].plot(time,self.frames['profiles']['record_yaml_mod'].out.Lwin - self.frames['profiles']['record_yaml_mod'].out.Lwout,label='Lw')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.G,label='G')
-            axes[label].plot(time,-self.frames['profiles']['record_yaml_mod'].out.LE,label='LE')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_end_mod'].out.Swin - self.frames['profiles']['record_yaml_end_mod'].out.Swout,label='Sw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_end_mod'].out.H,label='H')
+            axes[label].plot(time,self.frames['profiles']['record_yaml_end_mod'].out.Lwin - self.frames['profiles']['record_yaml_end_mod'].out.Lwout,label='Lw')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_end_mod'].out.G,label='G')
+            axes[label].plot(time,-self.frames['profiles']['record_yaml_end_mod'].out.LE,label='LE')
             axes[label].hlines(0.,*axes[label].get_xlim(),'k')
             axes[label].set_ylabel('energy flux [$\mathrm{W/m^2}$]')
             axes[label].set_xlabel('local sun time [$\mathrm{h}$]')
@@ -1682,7 +1723,7 @@ def on_pick(self,event):
         #d.set_offset_position('data')
         xy = d.get_offsets()
         x, y =  xy[:,0],xy[:,1]
-        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_mod']*3600.,'ro', markersize=5, picker=5,label=key)
+        #axes[-1].plot(seltableoutput[key+'_obs']*3600.,seltableoutput[key+'_end_mod']*3600.,'ro', markersize=5, picker=5,label=key)
 
         #print("p2")
         if len(ind) > 0:
@@ -1693,8 +1734,8 @@ def on_pick(self,event):
             #    #seltablestatsstdrel = self.seltablestatsstdrel
             #    #seltablestatspct = self.seltablestatspct
 
-            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+            #    #self.set_statsviewfocus('STNID' seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_end_mod'] == pos[1] )  ].STNID.iloc[0]
+            #    #self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_end_mod'] == pos[1] )  ].DT.iloc[0]
             #    
             #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
             #    self.set_profilefocus(STNID=self.axes['statsview0'].focus['STNID'],DT=self.axes['statsview0'].focus['DT'])
@@ -1704,8 +1745,7 @@ def on_pick(self,event):
             #el
             if (label == 'worldmap') or (label == 'worldmap_stations'):
                 self.hover_active = False
-                if (self.frames['worldmap']['STNID'] !=
-                    self.frames['profiles']['STNID']):
+                if (self.frames['worldmap']['STNID'] != self.frames['profiles']['STNID']):
                 # WE ALREADY HAVE the correct station from worldmap/stats because of the hovering!!
                 # so we just need to perform update_station
                     self.update_station()
@@ -1734,19 +1774,19 @@ def on_pick(self,event):
                     self.frames['profiles']['current_station_file_ini'] = \
                         open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
 
-                    if 'current_station_file_mod' in self.frames['profiles'].keys():
-                        self.frames['profiles']['current_station_file_mod'].close()
-                    self.frames['profiles']['current_station_file_mod'] = \
-                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_mod.yaml','r')
-                    if self.path_obs is not None:
-                        if 'current_station_file_afternoon' in self.frames['profiles'].keys():
-                            self.frames['profiles']['current_station_file_afternoon'].close()
-                        self.frames['profiles']['current_station_file_afternoon'] = \
-                            open(self.path_obs+'/'+format(STNID,"05d")+'_afternoon.yaml','r')
+                    if 'current_station_file_end_mod' in self.frames['profiles'].keys():
+                        self.frames['profiles']['current_station_file_end_mod'].close()
+                    self.frames['profiles']['current_station_file_end_mod'] = \
+                        open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
+                    if self.path_forcing is not None:
+                        if 'current_station_file_end' in self.frames['profiles'].keys():
+                            self.frames['profiles']['current_station_file_end'].close()
+                        self.frames['profiles']['current_station_file_end'] = \
+                            open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
                     # go to hovered record of current station
                     self.frames['profiles']['records_iterator'] = \
-                                    records_iterator(self.frames['profiles']['records_current_station_mod'])
+                                    records_iterator(self.frames['profiles']['records_current_station_end_mod'])
                     # ... and go to the record of the profile window (last one that
                     # was picked by the user)
                     found = False
@@ -1764,16 +1804,16 @@ def on_pick(self,event):
                         except StopIteration:
                             EOF = True
                     if found:
-                        self.frames['stats']['current_record_mod'] = record
+                        self.frames['stats']['current_record_end_mod'] = record
                         self.frames['stats']['current_record_chunk'] = chunk
                         self.frames['stats']['current_record_index'] = index
                     # # for the profiles we make a distinct record iterator, so that the
                     # # stats iterator can move independently
                     # self.frames['profiles']['records_iterator'] = \
-                    #                 records_iterator(self.frames['profiles']['records_current_station_mod'])
+                    #                 records_iterator(self.frames['profiles']['records_current_station_end_mod'])
                     # (self.frames['profiles']['STNID'] , \
                     # self.frames['profiles']['current_record_index']) , \
-                    # self.frames['profiles']['current_record_mod'] = \
+                    # self.frames['profiles']['current_record_end_mod'] = \
                     #                 self.frames['profiles']['records_iterator'].__next__()
 
 
@@ -1817,8 +1857,8 @@ def on_plot_hover(self,event):
                     #    seltablestatsstdrel = self.seltablestatsstdrel
                     #    seltablestatspct = self.seltablestatspct
 
-                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].STNID.iloc[0]
-                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_mod'] == pos[1] )  ].DT.iloc[0]
+                    #    self.set_statsviewfocus('STNID'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_end_mod'] == pos[1] )  ].STNID.iloc[0]
+                    #    self.set_statsviewfocus('DT'] = seltablestatsstdrel[(seltablestatsstdrel[selkey+'_ext'] == pos[0]) & (seltablestatsstdrel[selkey+'_end_mod'] == pos[1] )  ].DT.iloc[0]
                     #    self.axes['worldmap'].focus['STNID'] = self.axes['statsview0'].focus['STNID']
                     #    #self.goto_datetime_worldmap(self.axes['statsview0'].focus['DT'],'after')
                     #    self.hover_active = True
@@ -1828,25 +1868,25 @@ def on_plot_hover(self,event):
                     #el
                     #print(label[:5])
                     if (label[:5] == 'stats') or (label == 'times'):
-                        # records_mod = self.frames['stats']['records_current_station_mod'][selkey]
-                        # records_obs = self.frames['stats']['records_current_station_obs_afternoon'][selkey]
+                        # records_end_mod = self.frames['stats']['records_current_station_end_mod'][selkey]
+                        # records_obs = self.frames['stats']['records_current_station_end_obs'][selkey]
                         
-                        if self.path_obs is not None:
+                        if self.path_forcing is not None:
                             if label[:5] == 'stats':
-                                records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                                records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                                records_end_mod_stats = self.frames['stats']['records_all_stations_end_mod_stats']
+                                records_obs_stats = self.frames['stats']['records_all_stations_end_obs_stats']
                                 (self.frames['stats']['STNID'] ,
                                  self.frames['stats']['current_record_chunk'], 
                                  self.frames['stats']['current_record_index']) = \
-                                    records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                                    records_end_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_end_mod_stats[selkey] == pos[1])].index[0]
                             # elif label[:5] == 'stats':
-                            #     # records_mod_stats = self.frames['stats']['records_all_stations_mod_stats']
-                            #     records_obs_stats = self.frames['stats']['records_all_stations_obs_afternoon_stats']
+                            #     # records_end_mod_stats = self.frames['stats']['records_all_stations_end_mod_stats']
+                            #     records_obs_stats = self.frames['stats']['records_all_stations_end_obs_stats']
                             #     records_datetimes = self.frames['stats']['records_all_stations_ini']
                             #     (self.frames['stats']['STNID'] ,
                             #      self.frames['stats']['current_record_chunk'], 
                             #      self.frames['stats']['current_record_index']) = \
-                            #         records_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_mod_stats[selkey] == pos[1])].index[0]
+                            #         records_end_mod_stats[(records_obs_stats[selkey] == pos[0]) & (records_end_mod_stats[selkey] == pos[1])].index[0]
 
 
                         self.frames['stats']['stations_iterator'] = stations_iterator(self.frames['worldmap']['stations']) 
@@ -1888,10 +1928,10 @@ def on_plot_hover(self,event):
 
 
                         tab_suffixes = \
-                                ['_mod','_ini','_ini_pct']
-                        if self.path_obs is not None:
+                                ['_end_mod','_ini','_ini_pct']
+                        if self.path_forcing is not None:
                             tab_suffixes += \
-                                ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                                ['_end_mod_stats','_end_obs','_end_obs_stats']
                             
                         for tab_suffix in tab_suffixes:
                             self.frames['stats']['records_current_station'+tab_suffix] = \
@@ -1901,7 +1941,7 @@ def on_plot_hover(self,event):
 
                         # go to hovered record of current station
                         self.frames['stats']['records_iterator'] = \
-                                        records_iterator(self.frames['stats']['records_current_station_mod'])
+                                        records_iterator(self.frames['stats']['records_current_station_end_mod'])
 
 
                         # ... and go to the record of the profile window (last one that
@@ -1922,16 +1962,16 @@ def on_plot_hover(self,event):
                                 EOF = True
                         if found:
                             #print('h5')
-                            self.frames['stats']['current_record_mod'] = record
+                            self.frames['stats']['current_record_end_mod'] = record
                             self.frames['stats']['current_record_chunk'] = chunk
                             self.frames['stats']['current_record_index'] = index
 
                         #print(self.frames['stats']['STNID'],self.frames['stats']['current_record_index'])
                         tab_suffixes = \
                                 ['_ini','_ini_pct']
-                        if self.path_obs is not None:
+                        if self.path_forcing is not None:
                             tab_suffixes += \
-                                ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                                ['_end_mod_stats','_end_obs','_end_obs_stats']
                         for tab_suffix in tab_suffixes:
                             #print(tab_suffix)
                             #print(self.frames['stats']['records_current_station'+tab_suffix])
@@ -2020,8 +2060,8 @@ def on_plot_hover(self,event):
                                 self.frames['stats'][key] = self.frames['worldmap'][key]
                                 
                             ## fetch records of current station...
-                            #self.frames['stats']['records_current_station_mod'] =\
-                            #   get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+                            #self.frames['stats']['records_current_station_end_mod'] =\
+                            #   get_records_end_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
 
                             # ... and their indices
                             self.frames['stats']['records_current_station_index'] = \
@@ -2030,10 +2070,10 @@ def on_plot_hover(self,event):
                                      self.frames['stats']['current_station'].name)
 
                             tab_suffixes = \
-                                    ['_mod','_ini','_ini_pct']
-                            if self.path_obs is not None:
+                                    ['_end_mod','_ini','_ini_pct']
+                            if self.path_forcing is not None:
                                 tab_suffixes += \
-                                    ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                                    ['_end_mod_stats','_end_obs','_end_obs_stats']
 
                             for tab_suffix in tab_suffixes:
                                 self.frames['stats']['records_current_station'+tab_suffix] = \
@@ -2044,7 +2084,7 @@ def on_plot_hover(self,event):
                             #self.frames['stats']['records_iterator'].close()
                             del(self.frames['stats']['records_iterator'])
                             self.frames['stats']['records_iterator'] = \
-                                self.frames['stats']['records_current_station_mod'].iterrows()
+                                self.frames['stats']['records_current_station_end_mod'].iterrows()
 
 
 
@@ -2054,14 +2094,14 @@ def on_plot_hover(self,event):
                             (self.frames['stats']['STNID'] , \
                              self.frames['stats']['current_record_chunk'] , \
                              self.frames['stats']['current_record_index']) , \
-                            self.frames['stats']['current_record_mod'] = \
+                            self.frames['stats']['current_record_end_mod'] = \
                                 self.frames['stats']['records_iterator'].__next__()
                         
                             tab_suffixes = \
                                     ['_ini','_ini_pct']
-                            if self.path_obs is not None:
+                            if self.path_forcing is not None:
                                 tab_suffixes += \
-                                    ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                                    ['_end_mod_stats','_end_obs','_end_obs_stats']
 
                             for tab_suffix in tab_suffixes:
                                 self.frames['stats']['current_record'+tab_suffix] =  \
@@ -2104,8 +2144,8 @@ def on_plot_hover(self,event):
                 self.frames['stats']['current_station'] = \
                         self.frames['profiles']['current_station']
                 #print('h3a*')
-                self.frames['stats']['records_current_station_mod'] = \
-                        self.frames['profiles']['records_current_station_mod']
+                self.frames['stats']['records_current_station_end_mod'] = \
+                        self.frames['profiles']['records_current_station_end_mod']
                 #print('h3b*')
 
                 # the next lines recreate the records iterator. Probably it's
@@ -2116,7 +2156,7 @@ def on_plot_hover(self,event):
                 #self.frames['stats']['records_iterator'].close()
                 del(self.frames['stats']['records_iterator'])
                 self.frames['stats']['records_iterator'] = \
-                    self.frames['stats']['records_current_station_mod'].iterrows()
+                    self.frames['stats']['records_current_station_end_mod'].iterrows()
                 #print('h4*')
 
                 # ... and go to the record of the profile window (last one that
@@ -2139,7 +2179,7 @@ def on_plot_hover(self,event):
                         EOF = True
                 if found:
                     #print('h5*')
-                    self.frames['stats']['current_record_mod'] = record
+                    self.frames['stats']['current_record_end_mod'] = record
                     self.frames['stats']['current_record_chunk'] = chunk
                     self.frames['stats']['current_record_index'] = index
 
@@ -2148,8 +2188,8 @@ def on_plot_hover(self,event):
 
 
                 # # fetch records of current station...
-                # self.frames['stats']['records_current_station_mod'] =\
-                #    get_records_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
+                # self.frames['stats']['records_current_station_end_mod'] =\
+                #    get_records_end_mod(pd.DataFrame([self.frames['stats']['current_station']]),self.path_exp)
 
                 # ... and their indices
                 self.frames['stats']['records_current_station_index'] = \
@@ -2160,9 +2200,9 @@ def on_plot_hover(self,event):
                 
                 tab_suffixes = \
                         ['_ini','_ini_pct']
-                if self.path_obs is not None:
+                if self.path_forcing is not None:
                     tab_suffixes += \
-                        ['_mod_stats','_obs_afternoon','_obs_afternoon_stats']
+                        ['_end_mod_stats','_end_obs','_end_obs_stats']
 
                 for tab_suffix in tab_suffixes:
                     self.frames['stats']['records_current_station'+tab_suffix] = \
@@ -2233,7 +2273,7 @@ def on_plot_hover(self,event):
                 #                 self.frames['stats']['records_current_station'].iterrows()
                 # (self.frames['stats']['STNID'] , \
                 # self.frames['stats']['current_record_index']) , \
-                # self.frames['stats']['current_record_mod'] = \
+                # self.frames['stats']['current_record_end_mod'] = \
                 #                 self.frames['stats']['records_iterator'].__next__()
                 
 
diff --git a/class4gl/model.py b/class4gl/model.py
index 92792f5..21e86c3 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -426,21 +426,14 @@ def init(self):
 
                 
                 if self.air_ap[var][1] != self.air_ap[var][0]:
-                    raise ValueError("Error input profile consistency: two \
-                                     lowest profile levels for "+var+" should \
-                                     be equal.")
+                    raise ValueError("Error input profile consistency: two lowest profile levels for "+var+" should be equal.")
                 
                 # initialize the value from its profile when available
                 value_old = self.__dict__[var]
                 value_new = self.air_ap[var][indexh[0][0]]
                 
                 if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided \
-                                     ("+str(value_old)+ "kg kg-1), \
-                                     but it is now overwritten by the first \
-                                     level (index 0) of air_ap]var\ which is \
-                                     different (" +str(value_new)+"K).")
-                                        
+                    warnings.warn("Warning:  input was provided ("+str(value_old)+ "kg kg-1), but it is now overwritten by the first level (index 0) of air_ap.var which is different (" +str(value_new)+"kg kg-1).") 
                 self.__dict__[var] = value_new
 
                 # make a profile of the stratification 
@@ -952,18 +945,15 @@ def run_mixed_layer(self):
         if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
             l_entrainment = False
             warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! temperature jump is at the lower limit \
-                          and is not growing: entrainment is disabled for this (sub)timestep.") 
+                          " Warning! temperature jump is at the lower limit and is not growing: entrainment is disabled for this (sub)timestep.") 
         elif dtheta_pre < 0.1:
             dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
             l_entrainment = True
             warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          " Warning! Potential temperature jump at mixed- \
-                          layer height would become too low limiting timestep \
-                          from "+ str(self.dtmax)+' to '+str(dtmax_new))
+                          " Warning! Potential temperature jump at mixed-layer height would become too low. So I'm limiting the timestep from "+ str(self.dtmax)+' to '+str(dtmax_new))
             self.dtmax = min(self.dtmax,dtmax_new)
             warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "next subtimestep, entrainment will be disabled")
+                          " Next subtimestep, entrainment will also be disabled")
             #self.dthetatend = (0.1 - self.dtheta)/self.dtcur 
 
 
@@ -1232,12 +1222,7 @@ def integrate_mixed_layer(self):
 
             if itop > 1:
                     warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
-                          "Warning! Temperature profile was too steep. \
-                                  Modifying profile: "+ \
-                                  str(itop - 1)+ " measurements were dropped \
-                                  and replaced with its average \
-                                  Modifying profile. \
-                                  mean with next profile point(s).") 
+                          " Warning! Temperature profile was too steep.  Modifying profile: "+ str(itop - 1)+ " measurements were dropped and replaced with their average.") 
 
 
             self.air_ap = pd.concat((air_ap_head,\
@@ -1277,8 +1262,7 @@ def integrate_mixed_layer(self):
             in_ml = (self.air_ac.p >= self.P_h)
 
             if in_ml.sum() == 0:
-                warnings.warn(" no circulation points in the mixed layer \
-                              found. We just take the bottom one.")
+                warnings.warn(" no circulation points in the mixed layer found. We just take the bottom one.")
                 in_ml = self.air_ac.index == (len(self.air_ac) - 1)
             for var in ['t','q','u','v']:
 
diff --git a/class4gl/setup/setup_igra_20181217.py b/class4gl/setup/setup_igra_20181217.py
index 2733f3d..03da366 100644
--- a/class4gl/setup/setup_igra_20181217.py
+++ b/class4gl/setup/setup_igra_20181217.py
@@ -153,8 +153,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     one_run = False
 # for iSTN,STN in STNlist[5:]:  
     
-    fnout = args.path_output+"/"+format(STN.name,'05d')+"_morning.yaml"
-    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_afternoon.yaml"
+    fnout = args.path_output+"/"+format(STN.name,'05d')+"_ini.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_end.yaml"
     
 
     # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
@@ -344,12 +344,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #STN.name = STN.name
         all_records_morning = get_records(pd.DataFrame([STN]),\
                                       args.path_output,\
-                                      subset='morning',
+                                      subset='ini',
                                       refetch_records=True,
                                       )
         all_records_afternoon = get_records(pd.DataFrame([STN]),\
                                       args.path_output,\
-                                      subset='afternoon',
+                                      subset='end',
                                       refetch_records=True,
                                       )
     else:
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index 5260a95..61c7da2 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -11,15 +11,21 @@
 
 import argparse
 
+from time import sleep
+
 parser = argparse.ArgumentParser()
 #if __name__ == '__main__':
 parser.add_argument('--exec') # chunk simulation script
-parser.add_argument('--first_station_row')
+parser.add_argument('--first_station_row',
+                    help='starting row number of stations table')
 parser.add_argument('--last_station_row')
 parser.add_argument('--pbs_string',default=' -l walltime=2:0:0')
-parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--station_id',
+                    help="process a specific station id")
 parser.add_argument('--error_handling')
-parser.add_argument('--subset_forcing',default='morning') 
+parser.add_argument('--multi_processing_mode',default='pythonpool')
+parser.add_argument('--cpu_count',type=int,default=2)
+parser.add_argument('--subset_forcing',default='ini') 
                                         # this tells which yaml subset
                                         # to initialize with.
                                         # Most common options are
@@ -27,24 +33,37 @@
 
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
-parser.add_argument('--runtime')
+parser.add_argument('--runtime',
+                    help="set the runtime of the simulation in seconds, or get it from the daytime difference in the profile pairs 'from_profile_pair' (default)")
 # delete folders of experiments before running them
-parser.add_argument('--cleanup_experiments',default=False)
-parser.add_argument('--experiments')
-parser.add_argument('--split_by',default=50)# station soundings are split
-                                            # up in chunks
+parser.add_argument('--cleanup_output_directories',
+                    default="False",
+                    help="clean up output directories before executing the experiments")
+parser.add_argument('--experiments', 
+                    help="IDs of experiments, as a space-seperated list (default: 'BASE')")
+parser.add_argument('--experiments_names', 
+                    help="Alternative output names that are given to the experiments. By default, these are the same as --experiments") 
+parser.add_argument('--split_by',
+                    default=50,
+                    type=int,
+                    help="the maxmimum number of soundings that are contained in each output file of a station. -1 means unlimited. The default for array experiments is 50.")
+
+parser.add_argument('--c4gl_path_lib',help="the path of the CLASS4GL program.")#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+parser.add_argument('--path_forcing',
+                    help='directory of forcing data to initialize and constrain the ABL model simulations'
+                   )
+parser.add_argument('--path_experiments',
+                    help='output directory in which the experiments as subdirectories are stored')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--path_forcing') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--path_experiments') #,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 
 
 #arguments only used for update_yaml.py
-parser.add_argument('--path_dataset') 
-parser.add_argument('--global_keys') 
-args = parser.parse_args()
+#parser.add_argument('--path_dataset') 
+#parser.add_argument('--global_keys') 
+batch_args = parser.parse_args()
 
-sys.path.insert(0, args.c4gl_path_lib)
+if batch_args.c4gl_path_lib is not None:
+    sys.path.insert(0, batch_args.c4gl_path_lib)
 from class4gl import class4gl_input, data_global,class4gl
 from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
 from class4gl import blh,class4gl_input
@@ -64,72 +83,141 @@
 
 
 # #SET = 'GLOBAL'
-# SET = args.dataset
+# SET = batch_args.dataset
 
-# path_forcingSET = args.path_forcing+'/'+SET+'/'
+# path_forcingSET = batch_args.path_forcing+'/'+SET+'/'
 
-print("getting all stations from --path_forcing")
+print("getting all stations from "+batch_args.path_forcing)
 # these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+all_stations = stations(batch_args.path_forcing,suffix=batch_args.subset_forcing,refetch_stations=False)
 
 print('defining all_stations_select')
 # these are all the stations that are supposed to run by the whole batch (all
 # chunks). We narrow it down according to the station(s) specified.
-if args.station_id is not None:
+if batch_args.station_id is not None:
     print("Selecting stations by --station_id")
     stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    STNID,run_station = stations_iter.set_STNID(STNID=int(batch_args.station_id))
     all_stations_select = pd.DataFrame([run_station])
 else:
     print("Selecting stations from a row range in the table [--first_station_row,--last_station_row]")
     all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
+    if batch_args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(batch_args.last_station)+1)]
+    if batch_args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(batch_args.first_station):]
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
 print("getting all records of the whole batch")
 all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,\
+                                         batch_args.path_forcing,\
+                                         subset=batch_args.subset_forcing,\
                                          refetch_records=False,\
                                         )
 
-print('splitting batch in --split_by='+args.split_by+' jobs.')
+print('splitting batch in --split_by='+str(batch_args.split_by)+' jobs.')
 totalchunks = 0
 for istation,current_station in all_stations_select.iterrows():
     records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-    chunks_current_station = math.ceil(float(len(records_morning_station_select))/float(args.split_by))
+    chunks_current_station = math.ceil(float(len(records_morning_station_select))/float(batch_args.split_by))
     totalchunks +=chunks_current_station
 
-print('total chunks (= size of array-job) per experiment: ' + str(totalchunks))
-
-#if sys.argv[1] == 'qsub':
-# with qsub
-
-print(args.experiments.strip().split(" "))
+print('total chunks of simulations (= size of array-job) per experiment: ' + str(totalchunks))
 
-for EXP in args.experiments.strip().split(" "):
-    if args.cleanup_experiments:
-        os.system("rm -R "+args.path_experiments+'/'+EXP)
+experiments = batch_args.experiments.strip(' ').split(' ')
+if batch_args.experiments_names is not None:
+    experiments_names = batch_args.experiments_names.strip(' ').split(' ')
+    if len(experiments_names) != len(experiments):
+        raise ValueError('Lenght of --experiments_names is different from --experiments')
+else:
+    experiments_names = experiments
 
-    # C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
-    command = 'qsub '+args.pbs_string+' '+args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
-                str(totalchunks-1)+" -v C4GLJOB_experiments="+str(EXP)
-    # propagate arguments towards the job script
-    for argkey in args.__dict__.keys():
-        if ((argkey not in ['experiments','pbs_string','cleanup_experiments']) and \
-            # default values are specified in the simulation script, so
-            # excluded here
-            (args.__dict__[argkey] is not None)
-           ):
-                command +=',C4GLJOB_'+argkey+'='+args.__dict__[argkey]
+odir_exists = False
 
-    print('Submitting array job for experiment '+EXP+': '+command)
-    os.system(command)
+cleanup = (batch_args.cleanup_output_directories == 'True')
 
+if not cleanup:
+    for expname in experiments_names:
+        if os.path.exists(batch_args.path_experiments+'/'+expname):
+            print("Output directory already exists: "+batch_args.path_experiments+'/'+expname+". ")
+            odir_exists = True
+if odir_exists:
+    raise IOError("At least one of the output directories exists. Please use '--cleanup_output_directories True' to delete any output directory.")
+else:
+    for iexp,expname in enumerate(experiments_names):
+        if cleanup:
+            if os.path.exists(batch_args.path_experiments+'/'+expname):
+                print("Warning! Output directory '"+batch_args.path_experiments+'/'+expname+"' exists! I'm removing it in 10 seconds!' Press ctrl-c to abort.")
+                sleep(10)
+                os.system("rm -R "+batch_args.path_experiments+'/'+expname)
+        if batch_args.multi_processing_mode == 'qsub':
+    
+            # C4GLJOB_timestamp="+dt.datetime.now().isoformat()+",
+            command = 'qsub '+batch_args.pbs_string+' '+batch_args.c4gl_path_lib+'/simulations/batch_simulations.pbs -t 0-'+\
+                        str(totalchunks-1)+" -v C4GLJOB_experiments="+str(experiments[iexp])+",C4GLJOB_experiments_names="+str(expname)
+            # propagate arguments towards the job script
+            for argkey in batch_args.__dict__.keys():
+                if ((argkey not in ['multi_processing_mode','cpu_count','experiments','experiments_names','pbs_string','cleanup_output_directories']) and \
+                    # default values are specified in the simulation script, so
+                    # excluded here
+                    (batch_args.__dict__[argkey] is not None)
+                   ):
+                        command +=',C4GLJOB_'+argkey+'='+str(batch_args.__dict__[argkey])
+    
+            print('Submitting array job for experiment '+expname+': '+command)
+            os.system(command)
+
+        elif batch_args.multi_processing_mode == 'pythonpool':
+            from multiprocessing import Pool                                       
+            
+            # # load moodule from absolute path
+            # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
+            import importlib.util
+            print(batch_args.exec)
+            spec = importlib.util.spec_from_file_location("module.name",
+                                                          batch_args.exec)
+            task_module = importlib.util.module_from_spec(spec)
+            spec.loader.exec_module(task_module)
+            print('hello')
+            print(batch_args.exec)
+            
+
+            args_dict_current = {**batch_args.__dict__}
+
+            # we avoid to pass Nones, so that defaults are taken from the child
+            # script
+            for removekey,nonevalue in batch_args.__dict__.items():
+                if nonevalue is None:
+                    args_dict_current.pop(removekey)
+
+            # remove keys that are not relevant in the child script, so not
+            # passed (or those that are redefined in the host script manually)
+            for key in ['exec','multi_processing_mode','cpu_count','experiments','experiments_names','pbs_string','cleanup_output_directories']:
+                if key in args_dict_current:
+                    args_dict_current.pop(key)
+
+            args_dict_current['experiments'] = experiments[iexp]
+            args_dict_current['experiments_names'] = expname
+
+            print(args_dict_current)
+            all_tasks = []
+            for ichunk in range(totalchunks):
+                all_tasks.append({'global_chunk_number':str(ichunk),**args_dict_current}) 
+
+            print(pd.DataFrame(all_tasks)) 
+            def parallelize(analysis, filenames, processes):
+                '''
+                Call `analysis` for each file in the sequence `filenames`, using
+                up to `processes` parallel processes. Wait for them all to complete
+                and then return a list of results.
+                '''
+                return Pool(processes).map(analysis, filenames, chunksize = 1)
+    
+            def execute_kwargs(x):
+                return task_module.execute(**x)
+
+            parallelize(execute_kwargs,all_tasks,int(batch_args.cpu_count))
 
     #os.system(command)
 # elif sys.argv[1] == 'wsub':
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 20fc34f..3087fe3 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -9,375 +9,443 @@
 import pytz
 import math
 
-import argparse
 
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-#parser.add_argument('--timestamp')
-parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--first_station_row')
-parser.add_argument('--last_station_row')
-parser.add_argument('--station_id') # run a specific station id
-parser.add_argument('--error_handling',default='dump_on_success')
-parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
-parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
-                                                      # to initialize with.
-                                                      # Most common options are
-                                                      # 'morning' and 'ini'.
+arguments = []
 
+#parser.add_argument('--timestamp')
+arguments.append(dict(arg='--path_forcing',\
+                    help='directory of forcing data to initialize and constrain the ABL model simulations'))
+arguments.append(dict(arg='--path_experiments',
+                    help='output directory in which the experiments as subdirectories are stored'))#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+arguments.append(dict(arg='--first_station_row',\
+                    help='starting row number of stations table'))
+arguments.append(dict(arg='--last_station_row',\
+                    help='ending row number of stations table'))
+arguments.append(dict(arg='--station_id',\
+                    help="process a specific station id"))
+arguments.append(dict(arg='--error_handling',\
+                    default='dump_on_success',\
+                    help="type of error handling: either\n - 'dump_on_success' (default)\n - 'dump_always'"))
+arguments.append(dict(arg='--diag_tropo',\
+                    default=['advt','advq','advu','advv'],\
+                    help="field to diagnose the mean in the troposphere (<= 3000m)"))
+arguments.append(dict(arg='--subset_forcing',
+                    default='ini', 
+                    help="This indicates which yaml subset to initialize with.  Most common options are 'ini' (default) and 'morning'."))
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
-parser.add_argument('--runtime',default='from_afternoon_profile')
-
-parser.add_argument('--experiments')
-parser.add_argument('--split_by',default=-1)# station soundings are split
-                                            # up in chunks
-
-#parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
+arguments.append(dict(arg='--runtime',
+                    default='from_input',
+                    help="set the runtime of the simulation in seconds, or get it from the daytime difference in the profile pairs 'from_input' (default)"))
 
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+arguments.append(dict(arg='--experiments', help="IDs of experiments, as a space-seperated list (default: 'BASE')"))
+arguments.append(dict(arg='--split_by',\
+                    type=int,
+                    help="the maxmimum number of soundings that are contained in each output file of a station. -1 means unlimited (default). In case of arrays experiments, this is usually overwritten by 50."))
 
+#arguments.append(dict(arg='--station-chunk',default=0)
+arguments.append(dict(arg='--c4gl_path_lib',help="the path of the CLASS4GL program"))#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+arguments.append(dict(arg='--global_chunk_number',help="this is the batch number of the expected series of experiments according to split_by"))
+arguments.append(dict(arg='--station_chunk_number',help="this is the batch number according to split_by in case of considering one station"))
+arguments.append(dict(arg='--experiments_names', help="Alternative output names that are given to the experiments. By default, these are the same as --experiments") )
 
-EXP_DEFS  =\
-{
-  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
-  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
 
-# ========================
-print("getting a list of stations")
-# ========================
 
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    #parser.add_argument('--timestamp')
+    for argument in arguments:
+        name = argument.pop('arg')
+        parser.add_argument(name,**argument)
 
-# ====================================
-print('defining all_stations_select')
-# ====================================
-
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
-
-
-
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
+    args = parser.parse_args()
 else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print(all_stations_select)
-print("getting all records of the whole batch")
-all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,
-                                         refetch_records=False,
-                                         )
+    class Namespace:
+        def __init__(self,**kwargs):
+            self.__dict__.update(kwargs)
+
+    args = Namespace()
+    for argument in arguments:
+        if 'default' in argument.keys():
+            args.__dict__[argument['arg'].strip('-')] = argument['default']
+        else:
+            args.__dict__[argument['arg'].strip('-')] = None
+    print(args.__dict__)
+        
 
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+def execute(**kwargs):
+    # note that with args, we actually mean the same as those specified with
+    # the argparse module above
 
+    # overwrite the args according to the kwargs when the procedure is called
+    # as module function
+    for key,value in kwargs.items():
+        args.__dict__[key]  = value
 
-    if not (int(args.split_by) > 0) :
-            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    print("-- begin arguments --")
+    for key,value in args.__dict__.items():
+         print(key,': ',value)
+    print("-- end arguments ----")
 
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-            print('chunks_current_station',chunks_current_station)
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk = int(args.global_chunk_number) - totalchunks 
-        
-            totalchunks +=chunks_current_station
+    # load specified class4gl library
+    if args.c4gl_path_lib is not None:
+        sys.path.insert(0, args.c4gl_path_lib)
+    
+    from class4gl import class4gl_input, data_global,class4gl
+    from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+    from class4gl import blh,class4gl_input
+    
+    # this is a variant of global run in which the output of runs are still written
+    # out even when the run crashes.
+    
+    # #only include the following timeseries in the model output
+    # timeseries_only = \
+    # ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+    #  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+    #  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+    #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+    #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+    
+    
+    EXP_DEFS  =\
+    {
+      'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    
+      'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
         
-
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
+      'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+        'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
+      'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+      'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+    }
+    
+    # ========================
+    print("getting a list of stations")
+    # ========================
+    
+    # these are all the stations that are found in the input dataset
+    all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+    
+    # ====================================
+    print('defining all_stations_select')
+    # ====================================
+    
+    # these are all the stations that are supposed to run by the whole batch (all
+    # chunks). We narrow it down according to the station(s) specified.
+    
+    
+    
+    if args.station_id is not None:
+        print("Selecting station by ID")
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        all_stations_select = pd.DataFrame([run_station])
     else:
-        if args.split_by != -1:
-            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
-        
-
-#print(all_stations)
-print('Fetching initial/forcing records')
-records_morning = get_records(run_stations,\
-                              args.path_forcing,\
-                              subset=args.subset_forcing,
-                              refetch_records=False,
-                              )
-
-# note that if runtime is an integer number, we don't need to get the afternoon
-# profiles. 
-if args.runtime == 'from_afternoon_profile':
-    print('Fetching afternoon records for determining the simulation runtimes')
-    records_afternoon = get_records(run_stations,\
-                                    args.path_forcing,\
-                                    subset='afternoon',
-                                    refetch_records=False,
-                                    )
-    
-    # print(records_morning.index)
-    # print(records_afternoon.index)
-    # align afternoon records with the noon records, and set same index
-    print('hello')
-    print(len(records_afternoon))
-    print(len(records_morning))
-
-    print("aligning morning and afternoon records")
-    records_morning['dates'] = records_morning['ldatetime'].dt.date
-    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
-    records_afternoon.set_index(['STNID','dates'],inplace=True)
-    ini_index_dates = records_morning.set_index(['STNID','dates']).index
-    records_afternoon = records_afternoon.loc[ini_index_dates]
-    records_afternoon.index = records_morning.index
-
-experiments = args.experiments.strip(' ').split(' ')
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = args.path_experiments+'/'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        print(istation,current_station)
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
+        print("Selecting stations from a row range in the table")
+        all_stations_select = pd.DataFrame(all_stations.table)
+        if args.last_station_row is not None:
+            all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            all_stations_select = all_station_select.iloc[int(args.first_station):]
+    print("station numbers included in the whole batch "+\
+          "(all chunks):",list(all_stations_select.index))
+    
+    print(all_stations_select)
+    print("getting all records of the whole batch")
+    all_records_morning_select = get_records(all_stations_select,\
+                                             args.path_forcing,\
+                                             subset=args.subset_forcing,
+                                             refetch_records=False,
+                                             )
+    
+    # only run a specific chunck from the selection
+    if args.global_chunk_number is not None:
+        if args.station_chunk_number is not None:
+            raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+    
+        if (args.split_by is None) or (args.split_by <= 0):
+                raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    
+        run_station_chunk = None
+        print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+        totalchunks = 0
+        stations_iter = all_stations_select.iterrows()
+        in_current_chunk = False
+        try:
+            while not in_current_chunk:
+                istation,current_station = stations_iter.__next__()
+                all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+                chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+                print('chunks_current_station',chunks_current_station)
+                in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+            
+                if in_current_chunk:
+                    run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                    run_station_chunk = int(args.global_chunk_number) - totalchunks 
+            
+                totalchunks +=chunks_current_station
+            
+    
+        except StopIteration:
+           raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+        print("station = ",list(run_stations.index))
+        print("station chunk number:",run_station_chunk)
+    
+    # if no global chunk is specified, then run the whole station selection in one run, or
+    # a specific chunk for each selected station according to # args.station_chunk_number
+    else:
+        run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+        if args.station_chunk_number is not None:
+            run_station_chunk = int(args.station_chunk_number)
+            print("station(s) that is processed.",list(run_stations.index))
+            print("chunk number: ",run_station_chunk)
         else:
-            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
-            if os.path.isfile(fn_morning):
-                file_morning = open(fn_morning)
-            else:
-                fn_morning = \
-                     args.path_forcing+'/'+format(current_station.name,'05d')+\
-                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_morning = open(fn_morning)
-
-            if args.runtime == 'from_afternoon_profile':
-                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-            print('starting station chunk number: '\
-                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-
-            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-
-            isim = 0
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                    print('starting '+str(isim+1)+' out of '+\
-                      str(len(records_morning_station_chunk) )+\
-                      ' (station total: ',str(len(records_morning_station)),')')  
-                
+            if args.split_by is not None:
+                raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
+            run_station_chunk = 0
+            print("stations that are processed.",list(run_stations.index))
             
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    if args.diag_tropo is not None:
-                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
-                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                        profile_tropo = c4gli_morning.air_ac[seltropo]
-                        for var in args.diag_tropo:#['t','q','u','v',]:
-                            if var[:3] == 'adv':
-                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                            else:
-                                print("warning: tropospheric variable "+var+" not recognized")
-                    
+    
+    #print(all_stations)
+    print('Fetching initial/forcing records')
+    records_morning = get_records(run_stations,\
+                                  args.path_forcing,\
+                                  subset=args.subset_forcing,
+                                  refetch_records=False,
+                                  )
+    
+    # note that if runtime is an integer number, we don't need to get the afternoon
+    # profiles. 
+    if args.runtime == 'from_profile_pair':
+        print('Fetching afternoon records for determining the simulation runtimes')
+        records_afternoon = get_records(run_stations,\
+                                        args.path_forcing,\
+                                        subset='end',
+                                        refetch_records=False,
+                                        )
+        
+        # print(records_morning.index)
+        # print(records_afternoon.index)
+        # align afternoon records with the noon records, and set same index
+        print('hello')
+        print(len(records_afternoon))
+        print(len(records_morning))
+    
+        print("aligning morning and afternoon records")
+        records_morning['dates'] = records_morning['ldatetime'].dt.date
+        records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+        records_afternoon.set_index(['STNID','dates'],inplace=True)
+        ini_index_dates = records_morning.set_index(['STNID','dates']).index
+        records_afternoon = records_afternoon.loc[ini_index_dates]
+        records_afternoon.index = records_morning.index
+    
+    experiments = args.experiments.strip(' ').split(' ')
+    if args.experiments_names is not None:
+        experiments_names = args.experiments_names.strip(' ').split(' ')
+        if len(experiments_names) != len(experiments):
+            raise ValueError('Lenght of --experiments_names is different from --experiments')
+    
+    else:
+        experiments_names = experiments
+    
+    for iexpname,expid in enumerate(experiments):
+        expname = experiments_names[iexpname]
+        exp = EXP_DEFS[expid]
+        path_exp = args.path_experiments+'/'+expname+'/'
+    
+        os.system('mkdir -p '+path_exp)
+        for istation,current_station in run_stations.iterrows():
+            print(istation,current_station)
+            records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+            start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
+            end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
+            if start_record >= (len(records_morning_station)):
+                print("warning: outside of profile number range for station "+\
+                      str(current_station)+". Skipping chunk number for this station.")
+            else:
+                fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+                if os.path.isfile(fn_morning):
+                    file_morning = open(fn_morning)
+                else:
+                    fn_morning = \
+                         args.path_forcing+'/'+format(current_station.name,'05d')+\
+                         '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                    file_morning = open(fn_morning)
+    
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
+                fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_ini.yaml'
+                fn_end_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_end.yaml'
+                file_ini = open(fn_ini,'w')
+                file_end_mod = open(fn_end_mod,'w')
+    
+                #iexp = 0
+                onerun = False
+                print('starting station chunk number: '\
+                      +str(run_station_chunk)+' (chunk size:',args.split_by,')')
+    
+                records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+    
+                isim = 0
+                for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                        print('starting '+str(isim+1)+' out of '+\
+                          str(len(records_morning_station_chunk) )+\
+                          ' (station total: ',str(len(records_morning_station)),')')  
                     
-                    if args.runtime == 'from_afternoon_profile':
-                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                        c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                          record_afternoon.index_start, 
-                                                          record_afternoon.index_end,
-                                                        mode='ini')
-                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())
-                    elif args.runtime == 'from_input':
-                        runtime = c4gli_morning.pars.runtime
-                    else:
-                        runtime = int(args.runtime)
-
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        runtime})
-                    c4gli_morning.update(source=expname, pars=exp)
-                    if expname[-3:] == 'SM2':
-                        c4gli_morning.update(source=expname, pars={'wg': c4gli_morning.pars.wg - (c4gli_morning.pars.wg - c4gli_morning.pars.wwilt)/2.})
-                        c4gli_morning.update(source=expname, pars={'w2': c4gli_morning.pars.w2 - (c4gli_morning.pars.w2 - c4gli_morning.pars.wwilt)/2.})
-
-                    c4gl = class4gl(c4gli_morning)
-
-                    if args.error_handling == 'dump_always':
-                        try:
-                            print('checking data sources')
-                            if not c4gli_morning.check_source_globaldata():
-                                print('Warning: some input sources appear invalid')
-                            c4gl.run()
-                            print('run succesful')
-                        except:
-                            print('run not succesful')
-                        onerun = True
-
-                        c4gli_morning.dump(file_ini)
+                
+                        c4gli_morning = get_record_yaml(file_morning, 
+                                                        record_morning.index_start, 
+                                                        record_morning.index_end,
+                                                        mode='model_input')
+                        if args.diag_tropo is not None:
+                            print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                            seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                            profile_tropo = c4gli_morning.air_ac[seltropo]
+                            for var in args.diag_tropo:#['t','q','u','v',]:
+                                if var[:3] == 'adv':
+                                    mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                    c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                                else:
+                                    print("warning: tropospheric variable "+var+" not recognized")
                         
                         
-                        c4gl.dump(file_mod,\
-                                  include_input=False,\
-                                  #timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    # in this case, only the file will dumped if the runs were
-                    # successful
-                    elif args.error_handling == 'dump_on_success':
-                       try:
-                            print('checking data sources')
-                            if not c4gli_morning.check_source_globaldata():
-                                print('Warning: some input sources appear invalid')
-                            c4gl.run()
-                            print('run succesfull')
+                        if args.runtime == 'from_profile_pair':
+                            record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                            c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                              record_afternoon.index_start, 
+                                                              record_afternoon.index_end,
+                                                            mode='model_input')
+                            runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                                 c4gli_morning.pars.datetime_daylight).total_seconds())
+                        elif args.runtime == 'from_input':
+                            runtime = c4gli_morning.pars.runtime
+                        else:
+                            runtime = int(args.runtime)
+    
+                
+                        c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                            runtime})
+                        c4gli_morning.update(source=expname, pars=exp)
+    
+                        c4gl = class4gl(c4gli_morning)
+    
+                        if args.error_handling == 'dump_always':
+                            try:
+                                print('checking data sources')
+                                if not c4gli_morning.check_source_globaldata():
+                                    print('Warning: some input sources appear invalid')
+                                c4gl.run()
+                                print('run succesful')
+                            except:
+                                print('run not succesful')
+                            onerun = True
+    
+                            print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
                             c4gli_morning.dump(file_ini)
                             
                             
-                            c4gl.dump(file_mod,\
+                            c4gl.dump(file_end_mod,\
                                       include_input=False,\
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                       except:
-                           print('run not succesfull')
-                    isim += 1
-
-
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            if args.runtime == 'from_afternoon_profile':
-                file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
+                        # in this case, only the file will dumped if the runs were
+                        # successful
+                        elif args.error_handling == 'dump_on_success':
+                           try:
+                                print('checking data sources')
+                                if not c4gli_morning.check_source_globaldata():
+                                    print('Warning: some input sources appear invalid')
+                                c4gl.run()
+                                print('run succesful')
+                                c4gli_morning.dump(file_ini)
+                                
+                                
+                                print("dumping to "+str(file_ini)) 
+                                c4gl.dump(file_end_mod,\
+                                          include_input=False,\
+                                          #timeseries_only=timeseries_only,\
+                                         )
+                                onerun = True
+                           except:
+                               print('run not succesful')
+                        isim += 1
+    
+    
+                file_ini.close()
+                file_end_mod.close()
+                file_morning.close()
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon.close()
+        
+                if onerun:
+                    records_ini = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='ini',
+                                                               refetch_records=True,
+                                                               )
+                    records_end_mod = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='end',\
+                                                               refetch_records=True,\
+                                                               )
+                else:
+                    # remove empty files
+                    os.system('rm '+fn_ini)
+                    os.system('rm '+fn_end_mod)
+        
+        # # align afternoon records with initial records, and set same index
+        # records_afternoon.index = records_afternoon.ldatetime.dt.date
+        # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+        # records_afternoon.index = records_ini.index
+        
+        # stations_for_iter = stations(path_exp)
+        # for STNID,station in stations_iterator(stations_for_iter):
+        #     records_current_station_index = \
+        #             (records_ini.index.get_level_values('STNID') == STNID)
+        #     file_current_station_end_mod = STNID
+        # 
+        #     with \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
+        #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        #         for (STNID,index),record_ini in records_iterator(records_ini):
+        #             c4gli_ini = get_record_yaml(file_station_ini, 
+        #                                         record_ini.index_start, 
+        #                                         record_ini.index_end,
+        #                                         mode='ini')
+        #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+        # 
+        #             record_end_mod = records_end_mod.loc[(STNID,index)]
+        #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
+        #                                         record_end_mod.index_start, 
+        #                                         record_end_mod.index_end,
+        #                                         mode='mod')
+        #             record_afternoon = records_afternoon.loc[(STNID,index)]
+        #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+        #                                         record_afternoon.index_start, 
+        #                                         record_afternoon.index_end,
+        #                                         mode='ini')
+    
 
+if __name__ == '__main__':
+    #execute(**vars(args))
+    execute()
diff --git a/setup.py b/setup.py
index 524bd21..9dc7e6b 100644
--- a/setup.py
+++ b/setup.py
@@ -1,11 +1,12 @@
 from distutils.core import setup
+from setuptools import find_packages
 
 
 # I followed this tutorial to have both the git repository matched with the pip
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.1.2',
+        version='0.1.18',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name
@@ -15,8 +16,9 @@
         # I explain this later on
         keywords = ['atmospheric boundary layer', 'weather balloons',
                     'land--atmosphere interactions'],   # Keywords
-        packages=['class4gl'],
-        # packages=find_packages(),
+        # packages=['class4gl'],
+        install_package_data = True,
+        packages=find_packages("."),
         install_requires=['beautifulsoup4','pyyaml','pysolar','basemap','xarray'],
         # long_description=open('README.md').read(),
         classifiers=[
diff --git a/test.png b/test.png
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From 1ddbc876d3fe4cdf1dbbcd1bc872deab98dfae37 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:09:31 +0100
Subject: [PATCH 110/129] make readme

---
 README.md | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index ad8b3b0..fa727c4 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-[![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/cropped-class4gl_small-1.png)](https://class4gl.eu)
+[![CLASS4GL Logo](https://class4gl.eu/wp-content/uploads/2019/01/cropped-class4gl_small-1.png)](https://class4gl.eu)
 
 _CLASS4GL_ (Chemistry Land-surface Atmosphere Soil Slab model for Global Studies) is a fast and easy interface to investigate the dynamics of the atmospheric boundary layer from weather balloons worldwide. General info and tutorials for using CLASS4GL are available at class4gl.eu, and video clips about the atmospheric boundary layer physics can be found on the [website of the original CLASS model](classmodel.github.io/).
 
@@ -29,16 +29,13 @@ In case you experience a problem or a bug, please don’t hesitate to contact us
 
 CLASS4GL employs the balloon soundings from the Integrated Global Radiosonde Archive (IGRA) to initialize and validate the CLASS model. The sounding data is supplemented with ancillary data to further constrain the model. Therefore, a default set of gridded global datasets from satellite imagery, reanalysis and and surveys have been used that span a period of 1981–2015. An complete overview of the datasets can be found in the table. However, the default set can be replaced by alternative datasets as long as they are provided in netCDF format.
 
-Schematic overview of CLASS4GL:
-[![N|Solid](https://class4gl.eu//wp-content/uploads/2019/01/image4-1024x794.png)](https://class4gl.eu)
+[![Schematic overview of CLASS4GL](https://class4gl.eu//wp-content/uploads/2019/01/image4-1024x794.png)](https://class4gl.eu)
 
 A CLASS4GL data package is available that can be directly used to perform and validate ABL model simulations and sensitivity experiments. The locations of the balloon soundings are performed for different climate regions as shown on the map.
 
+[![150 stations from IGRA of the reference dataset to perform and validate the ABL model simulations with CLASS4GL (see Sect. 2.2 of the CLASS4GL manuscript). The different climate classes are indicated with the colors according to the Köppen-Geiger climate classification. The markers indicate the locations of the atmospheric profiles from three observation campaigns (ie., HUMPPA, BLLAST and GOAMAZON)](https://class4gl.eu/wp-content/uploads/2019/01/image-1-480x300.png)](https://class4gl.eu)
 
-These are the 150 stations from IGRA of the reference dataset to perform and validate the ABL model simulations with CLASS4GL (see Sect. 2.2 of the CLASS4GL manuscript). The different climate classes are indicated with the colors according to the Köppen-Geiger climate classification. The markers indicate the locations of the atmospheric profiles from three observation campaigns (ie., HUMPPA, BLLAST and GOAMAZON): [![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/image-1-480x300.png)](https://class4gl.eu)
-
-Data library of CLASS4GL: 
-[![N|Solid](https://class4gl.eu/wp-content/uploads/2019/01/image-5-768x492.png)](https://class4gl.eu)
+[![Data library of CLASS4GL](https://class4gl.eu/wp-content/uploads/2019/01/image-5-768x492.png)](https://class4gl.eu)
 
 ### Reference
 H. Wouters, I. Y. Petrova, C. C. van Heerwaarden, J. Vilà-Guerau de Arellano, A. J. Teuling, J. A. Santanello, V. Meulenberg, D. G. Miralles. A novel framework to investigate atmospheric boundary layer dynamics from balloon soundings worldwide: CLASS4GL v1.0. In preparation.

From a07c2a37c27a4baeb661039ebd3f7e935b693a1f Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:11:20 +0100
Subject: [PATCH 111/129] make readme

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 9dc7e6b..9ced95d 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.1.18',
+        version='0.1.19',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 1ba4ab41ff43ed9d7a12f65da4c6bbb9c753dd20 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:17:25 +0100
Subject: [PATCH 112/129] make readme

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index fa727c4..95bafa7 100644
--- a/README.md
+++ b/README.md
@@ -29,13 +29,13 @@ In case you experience a problem or a bug, please don’t hesitate to contact us
 
 CLASS4GL employs the balloon soundings from the Integrated Global Radiosonde Archive (IGRA) to initialize and validate the CLASS model. The sounding data is supplemented with ancillary data to further constrain the model. Therefore, a default set of gridded global datasets from satellite imagery, reanalysis and and surveys have been used that span a period of 1981–2015. An complete overview of the datasets can be found in the table. However, the default set can be replaced by alternative datasets as long as they are provided in netCDF format.
 
-[![Schematic overview of CLASS4GL](https://class4gl.eu//wp-content/uploads/2019/01/image4-1024x794.png)](https://class4gl.eu)
+[Schematic overview of CLASS4GL](https://class4gl.eu//wp-content/uploads/2019/01/image4-1024x794.png)
 
 A CLASS4GL data package is available that can be directly used to perform and validate ABL model simulations and sensitivity experiments. The locations of the balloon soundings are performed for different climate regions as shown on the map.
 
-[![150 stations from IGRA of the reference dataset to perform and validate the ABL model simulations with CLASS4GL (see Sect. 2.2 of the CLASS4GL manuscript). The different climate classes are indicated with the colors according to the Köppen-Geiger climate classification. The markers indicate the locations of the atmospheric profiles from three observation campaigns (ie., HUMPPA, BLLAST and GOAMAZON)](https://class4gl.eu/wp-content/uploads/2019/01/image-1-480x300.png)](https://class4gl.eu)
+[150 stations from IGRA of the reference dataset to perform and validate the ABL model simulations with CLASS4GL (see Sect. 2.2 of the CLASS4GL manuscript). The different climate classes are indicated with the colors according to the Köppen-Geiger climate classification. The markers indicate the locations of the atmospheric profiles from three observation campaigns (ie., HUMPPA, BLLAST and GOAMAZON)](https://class4gl.eu/wp-content/uploads/2019/01/image-1-480x300.png)]
 
-[![Data library of CLASS4GL](https://class4gl.eu/wp-content/uploads/2019/01/image-5-768x492.png)](https://class4gl.eu)
+[Data library of CLASS4GL](https://class4gl.eu/wp-content/uploads/2019/01/image-5-768x492.png)
 
 ### Reference
 H. Wouters, I. Y. Petrova, C. C. van Heerwaarden, J. Vilà-Guerau de Arellano, A. J. Teuling, J. A. Santanello, V. Meulenberg, D. G. Miralles. A novel framework to investigate atmospheric boundary layer dynamics from balloon soundings worldwide: CLASS4GL v1.0. In preparation.

From ffe0ed4e4bd382194d4d899cfa2cc0d64d7f5e50 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:17:46 +0100
Subject: [PATCH 113/129] make readme

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 9ced95d..25784e7 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.1.19',
+        version='0.1.20',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 39bf91d6c28d852010f5b8486185dc4fff5cecf0 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:19:26 +0100
Subject: [PATCH 114/129] make readme

---
 class4gl/Equirectangular_projection_SW.png | Bin 0 -> 1767055 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 class4gl/Equirectangular_projection_SW.png

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From 66f2556d530547dcd85c6ea67ee35fe901bb5f49 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:21:12 +0100
Subject: [PATCH 115/129] image

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 95bafa7..073c734 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,6 @@ H. Wouters, I. Y. Petrova, C. C. van Heerwaarden, J. Vilà-Guerau de Arellano, A
 
 
 # Get started: 
-see class4gl.eu/#getstarted
+see https://class4gl.eu/#getstarted
 
 

From ddc922972da174f49ad80db18a311379ebe50e89 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Thu, 24 Jan 2019 19:22:57 +0100
Subject: [PATCH 116/129] image

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 073c734..2eb73e1 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 [![CLASS4GL Logo](https://class4gl.eu/wp-content/uploads/2019/01/cropped-class4gl_small-1.png)](https://class4gl.eu)
 
-_CLASS4GL_ (Chemistry Land-surface Atmosphere Soil Slab model for Global Studies) is a fast and easy interface to investigate the dynamics of the atmospheric boundary layer from weather balloons worldwide. General info and tutorials for using CLASS4GL are available at class4gl.eu, and video clips about the atmospheric boundary layer physics can be found on the [website of the original CLASS model](classmodel.github.io/).
+_CLASS4GL_ (Chemistry Land-surface Atmosphere Soil Slab model for Global Studies) is a fast and easy interface to investigate the dynamics of the atmospheric boundary layer from weather balloons worldwide. General info and tutorials for using CLASS4GL are available at https://class4gl.eu, and video clips about the atmospheric boundary layer physics can be found on the [website of the original CLASS model](classmodel.github.io/).
 
 # Features
   - _Mine_ appropriate observations from global radio soundings, satellite data, reanalysis and climate models

From 7c72dfa06dda0b42db36400c876ab4e562d15ccd Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 29 Jan 2019 19:14:41 +0100
Subject: [PATCH 117/129] fix next record

---
 class4gl/interface/interface_new_koeppen.py | 38 +++++++-------
 class4gl/interface/interface_stations.py    | 39 +++++++-------
 class4gl/interface_functions.py             |  2 +
 class4gl/interface_multi.py                 | 57 ++++++++++++++-------
 4 files changed, 78 insertions(+), 58 deletions(-)

diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index f9a2a3e..a950d86 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -311,10 +311,10 @@ def brightness(rrggbb):
         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
             # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
             # clearsky = (cc < 0.05)
-            # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
-            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            # mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+            # obs = c4gldata[key].frames['stats']['records_all_stations_obs_end_obs_stats'].loc[clearsky]['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
 
             print ('filtering classes that have sufficient samples: ', include_koeppen)
             filter_classes = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
@@ -387,10 +387,10 @@ def brightness(rrggbb):
                 if koeppen.amount >= 200:
                     print(ikoeppen,':',koeppen)
                     kgc_select = (c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] == koeppen['KGCID'])
-                    koeppen_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'][kgc_select]
-                    koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'][kgc_select]
+                    koeppen_end_mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt'][kgc_select]
+                    koeppen_obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt'][kgc_select]
     
-                    #axes[varkey].scatter(koeppen_obs,koeppen_mod,marker=symbols[ikoeppen],color=colors[ikey])
+                    #axes[varkey].scatter(koeppen_obs,koeppen_end_mod,marker=symbols[ikoeppen],color=colors[ikey])
                              #  label=key+", "+\
                              #                    'R = '+str(round(PR[0],3))+', '+\
                              #                    'RMSE = '+str(round(RMSE,5))+', '+\
@@ -403,8 +403,8 @@ def brightness(rrggbb):
                 # #                              'RMSE = '+str(round(RMSE,5))+', '+\
                 # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
                     
-                    dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                                   pearsonr(koeppen_mod,koeppen_obs)[0],
+                    dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
+                                   pearsonr(koeppen_end_mod,koeppen_obs)[0],
                                    marker='o',linewidth=0.5,
                                             mfc=koeppen.color,mec='black',#koeppen.color,
                                             zorder=300+icolor,
@@ -412,8 +412,8 @@ def brightness(rrggbb):
                                    # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                                    # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
                                    )
-                    dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                                   pearsonr(koeppen_mod,koeppen_obs)[0],
+                    dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
+                                   pearsonr(koeppen_end_mod,koeppen_obs)[0],
                                    marker='o',linewidth=0.5,
                                             mfc=koeppen.color,mec='black',#koeppen.color,
                                             zorder=301+icolor, ms=1
@@ -422,8 +422,8 @@ def brightness(rrggbb):
                                    )
 
 
-                    # dias[varkey].add_sample(koeppen_mod.std()/koeppen_obs.std(),
-                    #                pearsonr(koeppen_mod,koeppen_obs)[0],
+                    # dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
+                    #                pearsonr(koeppen_end_mod,koeppen_obs)[0],
                     #                         marker='o',linewidth=0.5, mfc='none',mec=str(koeppen.color),
                     #                         zorder=600+icolor,
                     #                ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
@@ -526,11 +526,11 @@ def brightness(rrggbb):
         # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
         # clearsky = (cc < 0.05)
     
-        # mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats'].loc[clearsky]['d'+varkey+'dt']
-        # obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].loc[clearsky]['d'+varkey+'dt']
+        # mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].loc[clearsky]['d'+varkey+'dt']
+        # obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats'].loc[clearsky]['d'+varkey+'dt']
     
-        mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-        obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
+        obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
         print ('filtering classes that have sufficient samples: ', include_koeppen)
         filter_classess = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
         mod = mod.loc[filter_classes]
@@ -748,7 +748,7 @@ def brightness(rrggbb):
         key = list(args.experiments.strip().split(' '))[ikey]
         data_all = pd.DataFrame()
 
-        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats'].copy())
+        tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_end_obs_stats'].copy())
         
 
         tempdatamodstats["source"] = "soundings"
@@ -780,7 +780,7 @@ def brightness(rrggbb):
         for ikey,key in enumerate(list(args.experiments.strip().split(' '))):
             keylabel = keylabels[ikey]
 
-            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_mod_stats'].copy())
+            tempdatamodstats = pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].copy())
             tempdataini_this= pd.DataFrame(c4gldata[key].frames['stats']['records_all_stations_ini'].copy())
 
             tempdatamodstats['dates']= tempdataini_this.ldatetime.dt.date
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index a7dbd21..a5bbbac 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -146,11 +146,15 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
         #print(obs.std())
-        obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+        obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
         STD_OBS = obs.std()
         dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
+        dias[varkey].add_grid(zorder=-100.)
         dias[varkey]._ax.axis["left"].label.set_text(\
             "Normalized standard deviation")
+        i += 1
+    i = 1                                                                           
+    for varkey in ['h','theta','q']:                                                    
         if i == 1:
             axes[varkey].annotate('Normalized standard deviation',\
                         xy= (0.05,0.36),
@@ -175,16 +179,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
         dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
         #dia._ax.set_title(season.capitalize())
+        i += 1
     
-        dias[varkey].add_grid()
-    
-    
-        #dia.ax.plot(x99,y99,color='k')
-    
-        
         for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-            mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt']
-            obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt']
+            mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
+            obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
             x, y = obs.values,mod.values
             print(key,len(obs.values))
     
@@ -268,7 +267,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                            marker='o', ms=5, ls='',
                            #mfc='k', mec='k', # B&W
                            mfc=colors[ikey], mec=colors[ikey], # Colors
-                           label=key)
+                           label=key,zorder=100)
     
         # put ticker position, see
         # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
@@ -284,10 +283,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             istation = 0
             for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
                 indices =  (c4gldata[key].frames['stats']['records_all_stations_index'].get_level_values('STNID') == current_station.name)
-                station_mod = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].iloc[indices]
-                station_obs = c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].iloc[indices]
+                station_end_mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt'].iloc[indices]
+                station_obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt'].iloc[indices]
     
-                axes[varkey].scatter(station_obs,station_mod,marker=symbols[istation],color=colors[ikey])
+                axes[varkey].scatter(station_obs,station_end_mod,marker=symbols[istation],color=colors[ikey])
                          #  label=key+", "+\
                          #                    'R = '+str(round(PR[0],3))+', '+\
                          #                    'RMSE = '+str(round(RMSE,5))+', '+\
@@ -300,12 +299,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # #                              'RMSE = '+str(round(RMSE,5))+', '+\
             # #                              'BIAS = '+str(round(BIAS,5)),s=1.,color=colors[ikey])
                 
-                dias[varkey].add_sample(station_mod.std()/station_obs.std(),
-                               pearsonr(station_mod,station_obs)[0],#annotate=symbols[istation],
+                dias[varkey].add_sample(station_end_mod.std()/station_obs.std(),
+                               pearsonr(station_end_mod,station_obs)[0],#annotate=symbols[istation],
                                marker=symbols[istation], ms=5, ls='',
                                mfc='k', mec='k', # B&W
                                #mfc=colors[ikey], mec=colors[ikey], # Colors
-                               label=key)
+                               label=key,zorder=100)
 
                 istation += 1
     
@@ -332,11 +331,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         i +=1
           
         axes[varkey].set_aspect('equal')
-        low  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].min()
-        high  = c4gldata[key].frames['stats']['records_all_stations_mod_stats']['d'+varkey+'dt'].max()
+        low  = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt'].min()
+        high  = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt'].max()
 
-        low  = np.min([low,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].min()])
-        high  = np.max([high,c4gldata[key].frames['stats']['records_all_stations_obs_afternoon_stats']['d'+varkey+'dt'].max()])
+        low  = np.min([low,c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt'].min()])
+        high  = np.max([high,c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt'].max()])
 
         low = low - (high - low)*0.1
         high = high + (high - low)*0.1
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 630439e..6e79de6 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -59,6 +59,8 @@ def __next__(self,jump=1):
         self.ix = (self.ix+jump) 
         if self.ix >= len(self.records.index):
             raise StopIteration
+        if self.ix < 0:
+            raise StopIteration
 
         return self.records.index[self.ix], self.records.iloc[self.ix]
     def __prev__(self):
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 69a2cf9..2f44fe4 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -254,23 +254,23 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.250) & 
+                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.250) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
+                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  400.) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.00055) & 
+                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.00055) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.00055) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+                     #    (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
 
-                         # filter 'extreme' model output -> should be investigated!
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
+                     #     # filter 'extreme' model output -> should be investigated!
+                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
+                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
@@ -331,8 +331,10 @@ def sel_station(self,STNID=None,rownumber=None):
             self.update_station()
 
 
+    def next_station_event(self,event=None,**kwargs):
+        self.next_station(**kwargs)
 
-    def next_station(self,event=None,jump=1):
+    def next_station(self,jump=1):
         with suppress(StopIteration):
             self.frames['worldmap']['STNID'],\
             self.frames['worldmap']['current_station'] \
@@ -347,8 +349,11 @@ def next_station(self,event=None,jump=1):
 
         self.update_station()
 
-    def prev_station(self,event=None):
-        self.next_station(jump = -1,event=event)
+    def prev_station_event(self,event=None,**kwargs):
+        self.prev_station(**kwargs)
+
+    def prev_station(self):
+        self.next_station(jump = -1)
     def update_station(self):
         for key in ['STNID','current_station','stations_iterator']: 
             self.frames['stats'][key] = self.frames['worldmap'][key] 
@@ -416,7 +421,10 @@ def update_station(self):
 
         self.update_record()
 
-    def next_record(self,event=None,jump=1):
+    def next_record_event(self,event=None,**kwargs):
+        self.next_record(**kwargs)
+
+    def next_record(self,jump=1):
         
         old_chunk =  self.frames['profiles']['current_record_chunk']
 
@@ -465,8 +473,11 @@ def next_record(self,event=None,jump=1):
 
         self.update_record()
 
+    def prev_record_event(self,event=None,**kwargs):
+        self,prev_record(**kwargs)
+
     def prev_record(self,event=None):
-        self.next_record(jump=-1,event=event)
+        self.next_record(jump=-1)
 
     def update_record(self):
         self.frames['profiles']['current_record_ini'] =  \
@@ -686,10 +697,10 @@ def plot(self):
                                                      ])
             if button_type !='dataset':
                 btns[label] = Button(axes[label], 'Previous '+button_type)
-                btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+                btns[label].on_clicked(getattr(self, 'prev_'+button_type+'_event'))
             else:
                 btns[label] = Button(axes[label], 'Previous input var')
-                btns[label].on_clicked(getattr(self, 'prev_'+button_type))
+                btns[label].on_clicked(getattr(self, 'prev_'+button_type+'_event'))
 
 
             label='bnext'+button_type
@@ -701,10 +712,10 @@ def plot(self):
                                                      ])
             if button_type !='dataset':
                 btns[label] = Button(axes[label], 'Next '+button_type)
-                btns[label].on_clicked(getattr(self, 'next_'+button_type))
+                btns[label].on_clicked(getattr(self, 'next_'+button_type+'_event'))
             else:
                 btns[label] = Button(axes[label], 'Next input var')
-                btns[label].on_clicked(getattr(self, 'next_'+button_type))
+                btns[label].on_clicked(getattr(self, 'next_'+button_type+'_event'))
 
         
         # label = 'bprev_dataset'
@@ -907,6 +918,8 @@ def goto_datetime_worldmap(self,DT,shift=None):
             #else:
             #    self.frames['worldmap'].pop('DT')
 
+    def next_datetime_event(self,event=None,**kwargs):
+        self.next_datetime(**kwargs)
     def next_datetime(self,event=None):
         if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
             # for now we don't go to different files, so we cannot go to
@@ -916,6 +929,8 @@ def next_datetime(self,event=None):
             if "fig" in self.__dict__.keys():
                 self.refresh_plot_interface(only='worldmap') 
 
+    def prev_datetime_event(self,event=None,**kwargs):
+        self.prev_datetime(**kwargs)
     def prev_datetime(self,event=None):
         if 'time' in self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.variables[self.frames['worldmap']['inputkey']].dims:
             # for now we don't go to different files, so we cannot go to
@@ -994,6 +1009,8 @@ def sel_level(self,level):
 
         print('seldata7')
 
+    def next_level_event(self,event=None,**kwargs):
+        self.next_level(self,**kwargs)
     def next_level(self,event=None,jump=1):
         if 'lev' not in list(self.globaldata.datasets[self.frames['worldmap']['inputkey']].page.dims.keys()):
             raise ValueError('lev dimension not in dataset'+self.frames['worldmap']['inputkey'])
@@ -1002,7 +1019,9 @@ def next_level(self,event=None,jump=1):
         level =  ((level + jump - min(levels)) % (max(levels)-min(levels))) + min(levels)
         self.sel_level(level)
 
-    def prev_level(self,event=None):
+    def prev_level_event(self,event=None,**kwargs):
+        self.prev_level(self,**kwargs)
+    def prev_level(self):
         self.next_level(jump=-1)
 
         #self.frames['worldmap']['level'] = level: 

From d5dd32ab0ca3b77d23edfdf2729fc83e60ded270 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 29 Jan 2019 19:15:11 +0100
Subject: [PATCH 118/129] fix next record etc.

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 25784e7..9e06a36 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.1.20',
+        version='0.9.1',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From d1aede505169831a3976e8c6dffb01baa1d8ec70 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Fri, 1 Feb 2019 19:00:41 +0100
Subject: [PATCH 119/129] fix interface

---
 class4gl/interface/interface_show_profiles.py |   2 -
 class4gl/interface_functions.py               |  12 +-
 class4gl/interface_multi.py                   |  24 +-
 class4gl/setup/setup_goamazon.py              | 172 ++--
 class4gl/simulations/simulations.py           | 716 +++++++-------
 class4gl/simulations/simulations_iter.py      | 918 ++++++++++--------
 6 files changed, 968 insertions(+), 876 deletions(-)

diff --git a/class4gl/interface/interface_show_profiles.py b/class4gl/interface/interface_show_profiles.py
index 734e8b9..c15c5ea 100644
--- a/class4gl/interface/interface_show_profiles.py
+++ b/class4gl/interface/interface_show_profiles.py
@@ -1,4 +1,3 @@
-'''
 import numpy as np
 
 import pandas as pd
@@ -212,7 +211,6 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             #axes[varkey].set_title(latex['d'+varkey+'dt']+' '+units_final,fontsize=12.)                                     
 
 
-'''
 
 
 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 6e79de6..dd9a007 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -77,6 +77,8 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
     #print('going to next observation',filename)
     yaml_file.seek(index_start)
 
+    print('index_start',index_start)
+    print('index_end',index_end)
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
     os.system('mkdir -p '+TEMPDIR)
@@ -241,13 +243,19 @@ class stations_iterator(object):
     def __init__(self,stations):
         self.stations = stations
         self.ix = -1 
+        print('self.ix',self.ix)
+        print(stations.table)
     def __iter__(self):
         return self
     def __next__(self,jump=1):
-        self.ix = (self.ix+jump) 
-        if ((self.ix >= len(self.stations.table.index)) or (self.ix < 0 )):
+        print('next jump',jump)
+        print('self.ix',self.ix)
+        if ((self.ix+jump >= len(self.stations.table.index)) or (self.ix+jump < 0 )):
             raise StopIteration
+        self.ix = (self.ix+jump) 
+        print('self.ix',self.ix)
         self.ix = np.mod(self.ix,len(self.stations.table)) 
+        print('self.ix',self.ix)
         return self.stations.table.index[self.ix], self.stations.table.iloc[self.ix]
     def set_row(self,row):
         self.ix = row
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 2f44fe4..f2a1a2e 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -399,10 +399,10 @@ def update_station(self):
             self.frames['profiles']['current_station_file_end_mod'].close()
         self.frames['profiles']['current_station_file_end_mod'] = \
             open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
-        if 'current_station_file_end' in self.frames['profiles'].keys():
-            self.frames['profiles']['current_station_file_end'].close()
+        if 'current_station_file_end_obs' in self.frames['profiles'].keys():
+            self.frames['profiles']['current_station_file_end_obs'].close()
         if self.path_forcing is not None:
-            self.frames['profiles']['current_station_file_end'] = \
+            self.frames['profiles']['current_station_file_end_obs'] = \
                 open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
         # for the profiles we make a distinct record iterator, so that the
@@ -466,9 +466,9 @@ def next_record(self,jump=1):
                 open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
 
             if self.path_forcing is not None:
-                if 'current_station_file_end' in self.frames['profiles'].keys():
-                    self.frames['profiles']['current_station_file_end'].close()
-                self.frames['profiles']['current_station_file_end'] = \
+                if 'current_station_file_end_obs' in self.frames['profiles'].keys():
+                    self.frames['profiles']['current_station_file_end_obs'].close()
+                self.frames['profiles']['current_station_file_end_obs'] = \
                     open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
         self.update_record()
@@ -545,7 +545,7 @@ def update_record(self):
 
             self.frames['profiles']['record_yaml_end_obs'] = \
                get_record_yaml(
-                   self.frames['profiles']['current_station_file_end'], \
+                   self.frames['profiles']['current_station_file_end_obs'], \
                    record_end.index_start,
                    record_end.index_end,
                     mode='model_input')
@@ -940,10 +940,14 @@ def prev_datetime(self,event=None):
             if "fig" in self.__dict__.keys():
                 self.refresh_plot_interface(only='worldmap') 
 
+    def next_dataset_event(self,event=None,**kwargs):
+        self.next_dataset(**kwargs)
     def next_dataset(self,event=None):
         ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
         ikey = (ikey + 1) % len(self.frames['worldmap']['inputkeys'])
         self.sel_dataset(self.frames['worldmap']['inputkeys'][ikey])
+    def prev_dataset_event(self,event=None,**kwargs):
+        self.prev_dataset(**kwargs)
     def prev_dataset(self,event=None):
         ikey = self.frames['worldmap']['inputkeys'].index(self.frames['worldmap']['inputkey'])
         ikey = (ikey - 1) % len(self.frames['worldmap']['inputkeys'])
@@ -1798,9 +1802,9 @@ def on_pick(self,event):
                     self.frames['profiles']['current_station_file_end_mod'] = \
                         open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
                     if self.path_forcing is not None:
-                        if 'current_station_file_end' in self.frames['profiles'].keys():
-                            self.frames['profiles']['current_station_file_end'].close()
-                        self.frames['profiles']['current_station_file_end'] = \
+                        if 'current_station_file_end_obs' in self.frames['profiles'].keys():
+                            self.frames['profiles']['current_station_file_end_obs'].close()
+                        self.frames['profiles']['current_station_file_end_obs'] = \
                             open(self.path_forcing+'/'+format(STNID,"05d")+'_end.yaml','r')
 
                     # go to hovered record of current station
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
index 2315353..e0e83aa 100644
--- a/class4gl/setup/setup_goamazon.py
+++ b/class4gl/setup/setup_goamazon.py
@@ -70,11 +70,11 @@ def replace_iter(iterable, search, replace):
 HOUR_FILES = {}
 for iDT, DT in enumerate(DTS):
     morning_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*cdf')
     if len(possible_files)>0:
         morning_file= possible_files[0]
     afternoon_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*.cdf')
+    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*cdf')
     if len(possible_files)>0:
         afternoon_file= possible_files[0]
 
@@ -291,6 +291,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # only select samples monotonically increasing with height
         air_ap_tail_orig = pd.DataFrame(air_ap_tail)
         air_ap_tail = pd.DataFrame()
+        print(air_ap_tail_orig)
         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
         for ibottom in range(1,len(air_ap_tail_orig)):
             if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
@@ -343,7 +344,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # this is the real longitude that will be used to extract ground data
         
         dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=+4)
         dpars['doy'] = dpars['datetime'].timetuple().tm_yday
         
         dpars['SolarAltitude'] = \
@@ -415,7 +416,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         
         print('updating...')
         print(column)
-        c4gli.update(source='humppa',\
+        c4gli.update(source='goamazon',\
                     # pars=pars,
                     pars=dpars,\
                     air_balloon=air_balloon,\
@@ -445,28 +446,38 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         return c4gli
 
 
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+# path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GOAMAZON2/'
 
-
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn =pair['morning'][1]
-    print(humpafn)
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
-
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_end.yaml','w') 
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_ini.yaml','w') 
+ipair = 0
 for date,pair  in HOUR_FILES.items(): 
+    
     humpafn = pair['afternoon'][1]
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+    balloon_file_afternoon = xr.open_dataset(humpafn)
+    balloon_file_morning = xr.open_dataset(humpafn)
+    print(ipair)
+    if (\
+        (balloon_file_morning.pres.shape[0] > 10) and \
+        (balloon_file_afternoon.pres.shape[0] > 10)\
+        ):
+
+        c4gli_afternoon = humppa_parser(balloon_file_afternoon,file_afternoon,date,pair['afternoon'][0])
+        print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+        ipair += 1
+
+        print(pair['morning'])
+        humpafn =pair['morning'][1]
+        print(humpafn)
+
+        c4gli_morning = humppa_parser(balloon_file_morning,file_morning,date,pair['morning'][0])
+        print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+
+print(ipair)
 file_afternoon.close()
+file_morning.close()
+
  
 
 # file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
@@ -492,17 +503,16 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
 # 
 # file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
 
-
 records_morning = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='morning',
+                                           subset='ini',
                                            refetch_records=True,
                                            )
 print('records_morning_ldatetime',records_morning.ldatetime)
 
 records_afternoon = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='afternoon',
+                                           subset='end',
                                            refetch_records=True,
                                            )
 
@@ -512,62 +522,62 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
 records_afternoon.index = records_morning.index
 path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
 
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
+# os.system('mkdir -p '+path_exp)
+# file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_ini.yaml')
+# file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_end.yaml')
+# file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+# file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+# 
+# for (STNID,chunk,index),record_morning in records_morning.iterrows():
+#     record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+# 
+#     c4gli_morning = get_record_yaml(file_morning, 
+#                                     record_morning.index_start, 
+#                                     record_morning.index_end,
+#                                     mode='ini')
+#     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+#     
+#     
+#     c4gli_afternoon = get_record_yaml(file_afternoon, 
+#                                       record_afternoon.index_start, 
+#                                       record_afternoon.index_end,
+#                                     mode='ini')
+# 
+#     c4gli_morning.update(source='pairs',pars={'runtime' : \
+#                         int((c4gli_afternoon.pars.datetime_daylight - 
+#                              c4gli_morning.pars.datetime_daylight).total_seconds())})
+#     c4gli_morning.update(source='manual',
+#                          pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+#     c4gli_morning.dump(file_ini)
+#     
+#     c4gl = class4gl(c4gli_morning)
+#     c4gl.run()
+#     
+#     c4gl.dump(file_mod,\
+#               include_input=False,\
+#               timeseries_only=timeseries_only)
+# file_ini.close()
+# file_mod.close()
+# file_morning.close()
+# file_afternoon.close()
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='ini',
+#                                            refetch_records=True,
+#                                            )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='mod',
+#                                            refetch_records=True,
+#                                            )
+# 
+# records_mod.index = records_ini.index
+# 
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+# records_afternoon.index = records_ini.index
 
 
 """
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 3087fe3..8d9a652 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -74,378 +74,378 @@ def __init__(self,**kwargs):
     print(args.__dict__)
         
 
-def execute(**kwargs):
-    # note that with args, we actually mean the same as those specified with
-    # the argparse module above
-
-    # overwrite the args according to the kwargs when the procedure is called
-    # as module function
-    for key,value in kwargs.items():
-        args.__dict__[key]  = value
-
-    print("-- begin arguments --")
-    for key,value in args.__dict__.items():
-         print(key,': ',value)
-    print("-- end arguments ----")
-
-    # load specified class4gl library
-    if args.c4gl_path_lib is not None:
-        sys.path.insert(0, args.c4gl_path_lib)
-    
-    from class4gl import class4gl_input, data_global,class4gl
-    from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-    from class4gl import blh,class4gl_input
-    
-    # this is a variant of global run in which the output of runs are still written
-    # out even when the run crashes.
-    
-    # #only include the following timeseries in the model output
-    # timeseries_only = \
-    # ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-    #  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-    #  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-    #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-    #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-    
-    
-    EXP_DEFS  =\
-    {
-      'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    
-      'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+# def execute(**kwargs):
+# note that with args, we actually mean the same as those specified with
+# the argparse module above
+
+# overwrite the args according to the kwargs when the procedure is called
+# as module function
+# for key,value in kwargs.items():
+#     args.__dict__[key]  = value
+
+print("-- begin arguments --")
+for key,value in args.__dict__.items():
+     print(key,': ',value)
+print("-- end arguments ----")
+
+# load specified class4gl library
+if args.c4gl_path_lib is not None:
+    sys.path.insert(0, args.c4gl_path_lib)
+
+from class4gl import class4gl_input, data_global,class4gl
+from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+from class4gl import blh,class4gl_input
+
+# this is a variant of global run in which the output of runs are still written
+# out even when the run crashes.
+
+# #only include the following timeseries in the model output
+# timeseries_only = \
+# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+
+
+EXP_DEFS  =\
+{
+  'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+
+  'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+    
+  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
+  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+}
+
+# ========================
+print("getting a list of stations")
+# ========================
+
+# these are all the stations that are found in the input dataset
+all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+
+# ====================================
+print('defining all_stations_select')
+# ====================================
+
+# these are all the stations that are supposed to run by the whole batch (all
+# chunks). We narrow it down according to the station(s) specified.
+
+
+
+if args.station_id is not None:
+    print("Selecting station by ID")
+    stations_iter = stations_iterator(all_stations)
+    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+    all_stations_select = pd.DataFrame([run_station])
+else:
+    print("Selecting stations from a row range in the table")
+    all_stations_select = pd.DataFrame(all_stations.table)
+    if args.last_station_row is not None:
+        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+    if args.first_station_row is not None:
+        all_stations_select = all_station_select.iloc[int(args.first_station):]
+print("station numbers included in the whole batch "+\
+      "(all chunks):",list(all_stations_select.index))
+
+print(all_stations_select)
+print("getting all records of the whole batch")
+all_records_morning_select = get_records(all_stations_select,\
+                                         args.path_forcing,\
+                                         subset=args.subset_forcing,
+                                         refetch_records=False,
+                                         )
+
+# only run a specific chunck from the selection
+if args.global_chunk_number is not None:
+    if args.station_chunk_number is not None:
+        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+
+    if (args.split_by is None) or (args.split_by <= 0):
+            raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+
+    run_station_chunk = None
+    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+    totalchunks = 0
+    stations_iter = all_stations_select.iterrows()
+    in_current_chunk = False
+    try:
+        while not in_current_chunk:
+            istation,current_station = stations_iter.__next__()
+            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+            print('chunks_current_station',chunks_current_station)
+            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
         
-      'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-      'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-      'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-      'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-      'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-      'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-      'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-      'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-      'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-      'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-        'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
-      'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-      'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-      'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-      'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-      'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-      'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-    }
-    
-    # ========================
-    print("getting a list of stations")
-    # ========================
-    
-    # these are all the stations that are found in the input dataset
-    all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
-    
-    # ====================================
-    print('defining all_stations_select')
-    # ====================================
-    
-    # these are all the stations that are supposed to run by the whole batch (all
-    # chunks). We narrow it down according to the station(s) specified.
-    
-    
-    
-    if args.station_id is not None:
-        print("Selecting station by ID")
-        stations_iter = stations_iterator(all_stations)
-        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-        all_stations_select = pd.DataFrame([run_station])
-    else:
-        print("Selecting stations from a row range in the table")
-        all_stations_select = pd.DataFrame(all_stations.table)
-        if args.last_station_row is not None:
-            all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-        if args.first_station_row is not None:
-            all_stations_select = all_station_select.iloc[int(args.first_station):]
-    print("station numbers included in the whole batch "+\
-          "(all chunks):",list(all_stations_select.index))
-    
-    print(all_stations_select)
-    print("getting all records of the whole batch")
-    all_records_morning_select = get_records(all_stations_select,\
-                                             args.path_forcing,\
-                                             subset=args.subset_forcing,
-                                             refetch_records=False,
-                                             )
-    
-    # only run a specific chunck from the selection
-    if args.global_chunk_number is not None:
-        if args.station_chunk_number is not None:
-            raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
-    
-        if (args.split_by is None) or (args.split_by <= 0):
-                raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
-    
-        run_station_chunk = None
-        print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-        totalchunks = 0
-        stations_iter = all_stations_select.iterrows()
-        in_current_chunk = False
-        try:
-            while not in_current_chunk:
-                istation,current_station = stations_iter.__next__()
-                all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-                chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-                print('chunks_current_station',chunks_current_station)
-                in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-            
-                if in_current_chunk:
-                    run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                    run_station_chunk = int(args.global_chunk_number) - totalchunks 
-            
-                totalchunks +=chunks_current_station
-            
-    
-        except StopIteration:
-           raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-        print("station = ",list(run_stations.index))
-        print("station chunk number:",run_station_chunk)
-    
-    # if no global chunk is specified, then run the whole station selection in one run, or
-    # a specific chunk for each selected station according to # args.station_chunk_number
-    else:
-        run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-        if args.station_chunk_number is not None:
-            run_station_chunk = int(args.station_chunk_number)
-            print("station(s) that is processed.",list(run_stations.index))
-            print("chunk number: ",run_station_chunk)
-        else:
-            if args.split_by is not None:
-                raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
-            run_station_chunk = 0
-            print("stations that are processed.",list(run_stations.index))
-            
-    
-    #print(all_stations)
-    print('Fetching initial/forcing records')
-    records_morning = get_records(run_stations,\
-                                  args.path_forcing,\
-                                  subset=args.subset_forcing,
-                                  refetch_records=False,
-                                  )
-    
-    # note that if runtime is an integer number, we don't need to get the afternoon
-    # profiles. 
-    if args.runtime == 'from_profile_pair':
-        print('Fetching afternoon records for determining the simulation runtimes')
-        records_afternoon = get_records(run_stations,\
-                                        args.path_forcing,\
-                                        subset='end',
-                                        refetch_records=False,
-                                        )
+            if in_current_chunk:
+                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                run_station_chunk = int(args.global_chunk_number) - totalchunks 
         
-        # print(records_morning.index)
-        # print(records_afternoon.index)
-        # align afternoon records with the noon records, and set same index
-        print('hello')
-        print(len(records_afternoon))
-        print(len(records_morning))
-    
-        print("aligning morning and afternoon records")
-        records_morning['dates'] = records_morning['ldatetime'].dt.date
-        records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
-        records_afternoon.set_index(['STNID','dates'],inplace=True)
-        ini_index_dates = records_morning.set_index(['STNID','dates']).index
-        records_afternoon = records_afternoon.loc[ini_index_dates]
-        records_afternoon.index = records_morning.index
-    
-    experiments = args.experiments.strip(' ').split(' ')
-    if args.experiments_names is not None:
-        experiments_names = args.experiments_names.strip(' ').split(' ')
-        if len(experiments_names) != len(experiments):
-            raise ValueError('Lenght of --experiments_names is different from --experiments')
-    
+            totalchunks +=chunks_current_station
+        
+
+    except StopIteration:
+       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+    print("station = ",list(run_stations.index))
+    print("station chunk number:",run_station_chunk)
+
+# if no global chunk is specified, then run the whole station selection in one run, or
+# a specific chunk for each selected station according to # args.station_chunk_number
+else:
+    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+    if args.station_chunk_number is not None:
+        run_station_chunk = int(args.station_chunk_number)
+        print("station(s) that is processed.",list(run_stations.index))
+        print("chunk number: ",run_station_chunk)
     else:
-        experiments_names = experiments
-    
-    for iexpname,expid in enumerate(experiments):
-        expname = experiments_names[iexpname]
-        exp = EXP_DEFS[expid]
-        path_exp = args.path_experiments+'/'+expname+'/'
-    
-        os.system('mkdir -p '+path_exp)
-        for istation,current_station in run_stations.iterrows():
-            print(istation,current_station)
-            records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-            start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
-            end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
-            if start_record >= (len(records_morning_station)):
-                print("warning: outside of profile number range for station "+\
-                      str(current_station)+". Skipping chunk number for this station.")
+        if args.split_by is not None:
+            raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
+        run_station_chunk = 0
+        print("stations that are processed.",list(run_stations.index))
+        
+
+#print(all_stations)
+print('Fetching initial/forcing records')
+records_morning = get_records(run_stations,\
+                              args.path_forcing,\
+                              subset=args.subset_forcing,
+                              refetch_records=False,
+                              )
+
+# note that if runtime is an integer number, we don't need to get the afternoon
+# profiles. 
+if args.runtime == 'from_profile_pair':
+    print('Fetching afternoon records for determining the simulation runtimes')
+    records_afternoon = get_records(run_stations,\
+                                    args.path_forcing,\
+                                    subset='end',
+                                    refetch_records=False,
+                                    )
+    
+    # print(records_morning.index)
+    # print(records_afternoon.index)
+    # align afternoon records with the noon records, and set same index
+    print('hello')
+    print(len(records_afternoon))
+    print(len(records_morning))
+
+    print("aligning morning and afternoon records")
+    records_morning['dates'] = records_morning['ldatetime'].dt.date
+    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+    records_afternoon.set_index(['STNID','dates'],inplace=True)
+    ini_index_dates = records_morning.set_index(['STNID','dates']).index
+    records_afternoon = records_afternoon.loc[ini_index_dates]
+    records_afternoon.index = records_morning.index
+
+experiments = args.experiments.strip(' ').split(' ')
+if args.experiments_names is not None:
+    experiments_names = args.experiments_names.strip(' ').split(' ')
+    if len(experiments_names) != len(experiments):
+        raise ValueError('Lenght of --experiments_names is different from --experiments')
+
+else:
+    experiments_names = experiments
+
+for iexpname,expid in enumerate(experiments):
+    expname = experiments_names[iexpname]
+    exp = EXP_DEFS[expid]
+    path_exp = args.path_experiments+'/'+expname+'/'
+
+    os.system('mkdir -p '+path_exp)
+    for istation,current_station in run_stations.iterrows():
+        print(istation,current_station)
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
+        end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
+        if start_record >= (len(records_morning_station)):
+            print("warning: outside of profile number range for station "+\
+                  str(current_station)+". Skipping chunk number for this station.")
+        else:
+            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+            if os.path.isfile(fn_morning):
+                file_morning = open(fn_morning)
             else:
-                fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
-                if os.path.isfile(fn_morning):
-                    file_morning = open(fn_morning)
-                else:
-                    fn_morning = \
-                         args.path_forcing+'/'+format(current_station.name,'05d')+\
-                         '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                    file_morning = open(fn_morning)
-    
-                if args.runtime == 'from_profile_pair':
-                    file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
-                fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                         str(int(run_station_chunk))+'_ini.yaml'
-                fn_end_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                         str(int(run_station_chunk))+'_end.yaml'
-                file_ini = open(fn_ini,'w')
-                file_end_mod = open(fn_end_mod,'w')
-    
-                #iexp = 0
-                onerun = False
-                print('starting station chunk number: '\
-                      +str(run_station_chunk)+' (chunk size:',args.split_by,')')
-    
-                records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-    
-                isim = 0
-                for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                        print('starting '+str(isim+1)+' out of '+\
-                          str(len(records_morning_station_chunk) )+\
-                          ' (station total: ',str(len(records_morning_station)),')')  
-                    
+                fn_morning = \
+                     args.path_forcing+'/'+format(current_station.name,'05d')+\
+                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                file_morning = open(fn_morning)
+
+            if args.runtime == 'from_profile_pair':
+                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
+            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_ini.yaml'
+            fn_end_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                     str(int(run_station_chunk))+'_end.yaml'
+            file_ini = open(fn_ini,'w')
+            file_end_mod = open(fn_end_mod,'w')
+
+            #iexp = 0
+            onerun = False
+            print('starting station chunk number: '\
+                  +str(run_station_chunk)+' (chunk size:',args.split_by,')')
+
+            records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+            isim = 0
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    print('starting '+str(isim+1)+' out of '+\
+                      str(len(records_morning_station_chunk) )+\
+                      ' (station total: ',str(len(records_morning_station)),')')  
                 
-                        c4gli_morning = get_record_yaml(file_morning, 
-                                                        record_morning.index_start, 
-                                                        record_morning.index_end,
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='model_input')
+                    if args.diag_tropo is not None:
+                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                        profile_tropo = c4gli_morning.air_ac[seltropo]
+                        for var in args.diag_tropo:#['t','q','u','v',]:
+                            if var[:3] == 'adv':
+                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                            else:
+                                print("warning: tropospheric variable "+var+" not recognized")
+                    
+                    
+                    if args.runtime == 'from_profile_pair':
+                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                        c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                          int(record_afternoon.index_start),
+                                                          int(record_afternoon.index_end),
                                                         mode='model_input')
-                        if args.diag_tropo is not None:
-                            print('add tropospheric parameters on advection and subsidence (for diagnosis)')
-                            seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                            profile_tropo = c4gli_morning.air_ac[seltropo]
-                            for var in args.diag_tropo:#['t','q','u','v',]:
-                                if var[:3] == 'adv':
-                                    mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                                    c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                                else:
-                                    print("warning: tropospheric variable "+var+" not recognized")
+                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                             c4gli_morning.pars.datetime_daylight).total_seconds())
+                    elif args.runtime == 'from_input':
+                        runtime = c4gli_morning.pars.runtime
+                    else:
+                        runtime = int(args.runtime)
+
+            
+                    c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                        runtime})
+                    c4gli_morning.update(source=expname, pars=exp)
+
+                    c4gl = class4gl(c4gli_morning)
+
+                    if args.error_handling == 'dump_always':
+                        try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
+                            c4gl.run()
+                            print('run succesful')
+                        except:
+                            print('run not succesful')
+                        onerun = True
+
+                        print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
+                        c4gli_morning.dump(file_ini)
                         
                         
-                        if args.runtime == 'from_profile_pair':
-                            record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                            c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                              record_afternoon.index_start, 
-                                                              record_afternoon.index_end,
-                                                            mode='model_input')
-                            runtime = int((c4gli_afternoon.pars.datetime_daylight - 
-                                                 c4gli_morning.pars.datetime_daylight).total_seconds())
-                        elif args.runtime == 'from_input':
-                            runtime = c4gli_morning.pars.runtime
-                        else:
-                            runtime = int(args.runtime)
-    
-                
-                        c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                            runtime})
-                        c4gli_morning.update(source=expname, pars=exp)
-    
-                        c4gl = class4gl(c4gli_morning)
-    
-                        if args.error_handling == 'dump_always':
-                            try:
-                                print('checking data sources')
-                                if not c4gli_morning.check_source_globaldata():
-                                    print('Warning: some input sources appear invalid')
-                                c4gl.run()
-                                print('run succesful')
-                            except:
-                                print('run not succesful')
-                            onerun = True
-    
-                            print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
+                        c4gl.dump(file_end_mod,\
+                                  include_input=False,\
+                                  #timeseries_only=timeseries_only,\
+                                 )
+                        onerun = True
+                    # in this case, only the file will dumped if the runs were
+                    # successful
+                    elif args.error_handling == 'dump_on_success':
+                       try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
+                            c4gl.run()
+                            print('run succesful')
                             c4gli_morning.dump(file_ini)
                             
                             
+                            print("dumping to "+str(file_ini)) 
                             c4gl.dump(file_end_mod,\
                                       include_input=False,\
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                        # in this case, only the file will dumped if the runs were
-                        # successful
-                        elif args.error_handling == 'dump_on_success':
-                           try:
-                                print('checking data sources')
-                                if not c4gli_morning.check_source_globaldata():
-                                    print('Warning: some input sources appear invalid')
-                                c4gl.run()
-                                print('run succesful')
-                                c4gli_morning.dump(file_ini)
-                                
-                                
-                                print("dumping to "+str(file_ini)) 
-                                c4gl.dump(file_end_mod,\
-                                          include_input=False,\
-                                          #timeseries_only=timeseries_only,\
-                                         )
-                                onerun = True
-                           except:
-                               print('run not succesful')
-                        isim += 1
-    
-    
-                file_ini.close()
-                file_end_mod.close()
-                file_morning.close()
-                if args.runtime == 'from_profile_pair':
-                    file_afternoon.close()
-        
-                if onerun:
-                    records_ini = get_records(pd.DataFrame([current_station]),\
-                                                               path_exp,\
-                                                               getchunk = int(run_station_chunk),\
-                                                               subset='ini',
-                                                               refetch_records=True,
-                                                               )
-                    records_end_mod = get_records(pd.DataFrame([current_station]),\
-                                                               path_exp,\
-                                                               getchunk = int(run_station_chunk),\
-                                                               subset='end',\
-                                                               refetch_records=True,\
-                                                               )
-                else:
-                    # remove empty files
-                    os.system('rm '+fn_ini)
-                    os.system('rm '+fn_end_mod)
-        
-        # # align afternoon records with initial records, and set same index
-        # records_afternoon.index = records_afternoon.ldatetime.dt.date
-        # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-        # records_afternoon.index = records_ini.index
-        
-        # stations_for_iter = stations(path_exp)
-        # for STNID,station in stations_iterator(stations_for_iter):
-        #     records_current_station_index = \
-        #             (records_ini.index.get_level_values('STNID') == STNID)
-        #     file_current_station_end_mod = STNID
-        # 
-        #     with \
-        #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-        #     open(path_exp+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
-        #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-        #         for (STNID,index),record_ini in records_iterator(records_ini):
-        #             c4gli_ini = get_record_yaml(file_station_ini, 
-        #                                         record_ini.index_start, 
-        #                                         record_ini.index_end,
-        #                                         mode='ini')
-        #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-        # 
-        #             record_end_mod = records_end_mod.loc[(STNID,index)]
-        #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
-        #                                         record_end_mod.index_start, 
-        #                                         record_end_mod.index_end,
-        #                                         mode='mod')
-        #             record_afternoon = records_afternoon.loc[(STNID,index)]
-        #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-        #                                         record_afternoon.index_start, 
-        #                                         record_afternoon.index_end,
-        #                                         mode='ini')
-    
+                       except:
+                           print('run not succesful')
+                    isim += 1
 
-if __name__ == '__main__':
-    #execute(**vars(args))
-    execute()
+
+            file_ini.close()
+            file_end_mod.close()
+            file_morning.close()
+            if args.runtime == 'from_profile_pair':
+                file_afternoon.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+                records_end_mod = get_records(pd.DataFrame([current_station]),\
+                                                           path_exp,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='end',\
+                                                           refetch_records=True,\
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+                os.system('rm '+fn_end_mod)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_exp)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_end_mod = STNID
+    # 
+    #     with \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_exp+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_end_mod = records_end_mod.loc[(STNID,index)]
+    #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
+    #                                         record_end_mod.index_start, 
+    #                                         record_end_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
+
+# if __name__ == '__main__':
+#     #execute(**vars(args))
+#     execute()
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 5c1bd2b..8b60b86 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -9,382 +9,385 @@
 import pytz
 import math
 
-import argparse
 
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-#parser.add_argument('--timestamp')
-parser.add_argument('--path_forcing')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--path_experiments')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--first_station_row')
-parser.add_argument('--last_station_row')
-parser.add_argument('--station_id') # run a specific station id
-parser.add_argument('--error_handling',default='dump_on_success')
-parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
-parser.add_argument('--subset_forcing',default='morning') # this tells which yaml subset
-                                                      # to initialize with.
-                                                      # Most common options are
-                                                      # 'morning' and 'ini'.
+arguments = []
 
+#parser.add_argument('--timestamp')
+arguments.append(dict(arg='--path_forcing',\
+                    help='directory of forcing data to initialize and constrain the ABL model simulations'))
+arguments.append(dict(arg='--path_experiments',
+                    help='output directory in which the experiments as subdirectories are stored'))#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+arguments.append(dict(arg='--first_station_row',\
+                    help='starting row number of stations table'))
+arguments.append(dict(arg='--last_station_row',\
+                    help='ending row number of stations table'))
+arguments.append(dict(arg='--station_id',\
+                    help="process a specific station id"))
+arguments.append(dict(arg='--error_handling',\
+                    default='dump_on_success',\
+                    help="type of error handling: either\n - 'dump_on_success' (default)\n - 'dump_always'"))
+arguments.append(dict(arg='--diag_tropo',\
+                    default=['advt','advq','advu','advv'],\
+                    help="field to diagnose the mean in the troposphere (<= 3000m)"))
+arguments.append(dict(arg='--subset_forcing',
+                    default='ini', 
+                    help="This indicates which yaml subset to initialize with.  Most common options are 'ini' (default) and 'morning'."))
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
-parser.add_argument('--runtime',default='from_afternoon_profile')
-
-parser.add_argument('--experiments')
-parser.add_argument('--split_by',default=-1)# station soundings are split
-                                            # up in chunks
-
-#parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-EXP_DEFS  =\
-{
-  'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    'GLOBAL_ADV_SHR_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
-  'GLOBAL_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'IOPS_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'IOPS_AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
+arguments.append(dict(arg='--runtime',
+                    default='from_input',
+                    help="set the runtime of the simulation in seconds, or get it from the daytime difference in the profile pairs 'from_input' (default)"))
 
-# #SET = 'GLOBAL'
-# SET = args.dataset
+arguments.append(dict(arg='--experiments', help="IDs of experiments, as a space-seperated list (default: 'BASE')"))
+arguments.append(dict(arg='--split_by',\
+                    type=int,
+                    help="the maxmimum number of soundings that are contained in each output file of a station. -1 means unlimited (default). In case of arrays experiments, this is usually overwritten by 50."))
 
-# ========================
-print("getting a list of stations")
-# ========================
+#arguments.append(dict(arg='--station-chunk',default=0)
+arguments.append(dict(arg='--c4gl_path_lib',help="the path of the CLASS4GL program"))#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+arguments.append(dict(arg='--global_chunk_number',help="this is the batch number of the expected series of experiments according to split_by"))
+arguments.append(dict(arg='--station_chunk_number',help="this is the batch number according to split_by in case of considering one station"))
+arguments.append(dict(arg='--experiments_names', help="Alternative output names that are given to the experiments. By default, these are the same as --experiments") )
 
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
 
-# ====================================
-print('defining all_stations_select')
-# ====================================
 
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    #parser.add_argument('--timestamp')
+    for argument in arguments:
+        name = argument.pop('arg')
+        parser.add_argument(name,**argument)
 
-
-
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
+    args = parser.parse_args()
 else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print(all_stations_select)
-print("getting all records of the whole batch")
-all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,
-                                         refetch_records=False,
-                                         )
+    class Namespace:
+        def __init__(self,**kwargs):
+            self.__dict__.update(kwargs)
+
+    args = Namespace()
+    for argument in arguments:
+        if 'default' in argument.keys():
+            args.__dict__[argument['arg'].strip('-')] = argument['default']
+        else:
+            args.__dict__[argument['arg'].strip('-')] = None
+    print(args.__dict__)
+        
 
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+def execute(**kwargs):
+    # note that with args, we actually mean the same as those specified with
+    # the argparse module above
 
+    # overwrite the args according to the kwargs when the procedure is called
+    # as module function
+    for key,value in kwargs.items():
+        args.__dict__[key]  = value
 
-    if not (int(args.split_by) > 0) :
-            raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    print("-- begin arguments --")
+    for key,value in args.__dict__.items():
+         print(key,': ',value)
+    print("-- end arguments ----")
 
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-            print('chunks_current_station',chunks_current_station)
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk = int(args.global_chunk_number) - totalchunks 
-        
-            totalchunks +=chunks_current_station
+    # load specified class4gl library
+    if args.c4gl_path_lib is not None:
+        sys.path.insert(0, args.c4gl_path_lib)
+    
+    from class4gl import class4gl_input, data_global,class4gl
+    from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+    from class4gl import blh,class4gl_input
+    
+    # this is a variant of global run in which the output of runs are still written
+    # out even when the run crashes.
+    
+    # #only include the following timeseries in the model output
+    # timeseries_only = \
+    # ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+    #  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+    #  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+    #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+    #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+    
+    
+    EXP_DEFS  =\
+    {
+      'BASE_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    
+      'NOADV_ITER':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
         
-
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
+      'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'W_ITER':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'AC_ITER': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    }
+    
+    
+    # #SET = 'GLOBAL'
+    # SET = args.dataset
+    
+    # ========================
+    print("getting a list of stations")
+    # ========================
+    
+    # these are all the stations that are found in the input dataset
+    all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+    
+    # ====================================
+    print('defining all_stations_select')
+    # ====================================
+    
+    # these are all the stations that are supposed to run by the whole batch (all
+    # chunks). We narrow it down according to the station(s) specified.
+    
+    
+    
+    if args.station_id is not None:
+        print("Selecting station by ID")
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        all_stations_select = pd.DataFrame([run_station])
+    else:
+        print("Selecting stations from a row range in the table")
+        all_stations_select = pd.DataFrame(all_stations.table)
+        if args.last_station_row is not None:
+            all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            all_stations_select = all_station_select.iloc[int(args.first_station):]
+    print("station numbers included in the whole batch "+\
+          "(all chunks):",list(all_stations_select.index))
+    
+    print(all_stations_select)
+    print("getting all records of the whole batch")
+    all_records_morning_select = get_records(all_stations_select,\
+                                             args.path_forcing,\
+                                             subset=args.subset_forcing,
+                                             refetch_records=False,
+                                             )
+    
+    # only run a specific chunck from the selection
+    if args.global_chunk_number is not None:
+        if args.station_chunk_number is not None:
+            raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+    
+    
+        if not (int(args.split_by) > 0) :
+                raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    
+        run_station_chunk = None
+        print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+        totalchunks = 0
+        stations_iter = all_stations_select.iterrows()
+        in_current_chunk = False
+        try:
+            while not in_current_chunk:
+                istation,current_station = stations_iter.__next__()
+                all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+                chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+                print('chunks_current_station',chunks_current_station)
+                in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+            
+                if in_current_chunk:
+                    run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                    run_station_chunk = int(args.global_chunk_number) - totalchunks 
+            
+                totalchunks +=chunks_current_station
+            
+    
+        except StopIteration:
+           raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+        print("station = ",list(run_stations.index))
+        print("station chunk number:",run_station_chunk)
+    
+    # if no global chunk is specified, then run the whole station selection in one run, or
+    # a specific chunk for each selected station according to # args.station_chunk_number
     else:
-        if args.split_by != -1:
-            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
+        run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+        if args.station_chunk_number is not None:
+            run_station_chunk = int(args.station_chunk_number)
+            print("station(s) that is processed.",list(run_stations.index))
+            print("chunk number: ",run_station_chunk)
+        else:
+            if args.split_by != -1:
+                raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
+            run_station_chunk = 0
+            print("stations that are processed.",list(run_stations.index))
+            
+    
+    #print(all_stations)
+    print('Fetching initial/forcing records')
+    records_morning = get_records(run_stations,\
+                                  args.path_forcing,\
+                                  subset=args.subset_forcing,
+                                  refetch_records=False,
+                                  )
+    
+    # note that if runtime is an integer number, we don't need to get the afternoon
+    # profiles. 
+    if args.runtime == 'from_profile_pair':
+        print('Fetching afternoon records for determining the simulation runtimes')
+        records_afternoon = get_records(run_stations,\
+                                        args.path_forcing,\
+                                        subset='end',
+                                        refetch_records=False,
+                                        )
         
+        # print(records_morning.index)
+        # print(records_afternoon.index)
+        # align afternoon records with the noon records, and set same index
+        print(len(records_afternoon))
+        print(len(records_morning))
+    
+        print("aligning morning and afternoon records")
+        records_morning['dates'] = records_morning['ldatetime'].dt.date
+        records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+        records_afternoon.set_index(['STNID','dates'],inplace=True)
+        ini_index_dates = records_morning.set_index(['STNID','dates']).index
+        records_afternoon = records_afternoon.loc[ini_index_dates]
+        records_afternoon.index = records_morning.index
+    
 
-#print(all_stations)
-print('Fetching initial/forcing records')
-records_morning = get_records(run_stations,\
-                              args.path_forcing,\
-                              subset=args.subset_forcing,
-                              refetch_records=False,
-                              )
-
-# note that if runtime is an integer number, we don't need to get the afternoon
-# profiles. 
-if args.runtime == 'from_afternoon_profile':
-    print('Fetching afternoon records for determining the simulation runtimes')
-    records_afternoon = get_records(run_stations,\
-                                    args.path_forcing,\
-                                    subset='afternoon',
-                                    refetch_records=False,
-                                    )
-    
-    # print(records_morning.index)
-    # print(records_afternoon.index)
-    # align afternoon records with the noon records, and set same index
-    print('hello')
-    print(len(records_afternoon))
-    print(len(records_morning))
-
-    print("aligning morning and afternoon records")
-    records_morning['dates'] = records_morning['ldatetime'].dt.date
-    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
-    records_afternoon.set_index(['STNID','dates'],inplace=True)
-    ini_index_dates = records_morning.set_index(['STNID','dates']).index
-    records_afternoon = records_afternoon.loc[ini_index_dates]
-    records_afternoon.index = records_morning.index
-
-experiments = args.experiments.strip(' ').split(' ')
-for expname in experiments:
-    exp = EXP_DEFS[expname]
-    path_exp = args.path_experiments+'/'+expname+'/'
 
-    os.system('mkdir -p '+path_exp)
-    records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-    for istation,current_station in run_stations.iterrows():
-        if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
-        else:
-
-            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
-            if os.path.isfile(fn_morning):
-                file_morning = open(fn_morning)
+    experiments = args.experiments.strip(' ').split(' ')
+    if args.experiments_names is not None:
+        experiments_names = args.experiments_names.strip(' ').split(' ')
+        if len(experiments_names) != len(experiments):
+            raise ValueError('Lenght of --experiments_names is different from --experiments')
+    
+    else:
+        experiments_names = experiments
+    
+    for iexpname,expid in enumerate(experiments):
+        expname = experiments_names[iexpname]
+        exp = EXP_DEFS[expid]
+        path_exp = args.path_experiments+'/'+expname+'/'
+    
+        os.system('mkdir -p '+path_exp)
+        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        for istation,current_station in run_stations.iterrows():
+            if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
+                print("warning: outside of profile number range for station "+\
+                      str(current_station)+". Skipping chunk number for this station.")
             else:
-                fn_morning = \
-                     args.path_forcing+'/'+format(current_station.name,'05d')+\
-                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_morning = open(fn_morning)
-
-            if args.runtime == 'from_afternoon_profile':
-                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_mod.yaml'
-            file_ini = open(fn_ini,'w')
-            file_mod = open(fn_mod,'w')
-
-            #iexp = 0
-            onerun = False
-            print('starting station chunk number: '\
-                  +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-
-
-            isim = 0
-            records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                #if iexp == 11:
-                
-            
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='ini')
-                    if args.diag_tropo is not None:
-                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                        profile_tropo = c4gli_morning.air_ac[seltropo]
-                        for var in args.diag_tropo:#['t','q','u','v',]:
-                            if var[:3] == 'adv':
-                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                            else:
-                                print("warning: tropospheric variable "+var+" not recognized")
-                    
-                    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-                    
+    
+                fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+                print('fn_morning',fn_morning)
+                if os.path.isfile(fn_morning):
+                    file_morning = open(fn_morning)
+                else:
+                    fn_morning = \
+                         args.path_forcing+'/'+format(current_station.name,'05d')+\
+                         '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                    file_morning = open(fn_morning)
+    
+                fn_afternoon = args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml'
+                print(fn_afternoon)
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon = open(fn_afternoon)
+                fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_ini.yaml'
+                print('fn_ini',fn_ini)
+                fn_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_end.yaml'
+                file_ini = open(fn_ini,'w')
+                file_mod = open(fn_mod,'w')
+    
+                #iexp = 0
+                onerun = False
+                print('starting station chunk number: '\
+                      +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
+    
+    
+                isim = 0
+                records_morning_station_chunk = records_morning_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+                for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    #if iexp == 11:
                     
-                    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                      record_afternoon.index_start, 
-                                                      record_afternoon.index_end,
-                                                    mode='ini')
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-                    c4gli_morning.update(source=expname, pars=exp)
+                
+                        c4gli_morning = get_record_yaml(file_morning, 
+                                                        record_morning.index_start, 
+                                                        record_morning.index_end,
+                                                        mode='model_input')
+                        if args.diag_tropo is not None:
+                            seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                            profile_tropo = c4gli_morning.air_ac[seltropo]
+                            for var in args.diag_tropo:#['t','q','u','v',]:
+                                if var[:3] == 'adv':
+                                    mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                    c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                                else:
+                                    print("warning: tropospheric variable "+var+" not recognized")
+                        
+                        #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+                        
+                        if args.runtime == 'from_profile_pair':
+                            record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                            c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                              int(record_afternoon.index_start), 
+                                                              int(record_afternoon.index_end),
+                                                            mode='model_input')
+                            runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                                 c4gli_morning.pars.datetime_daylight).total_seconds())
+                        elif args.runtime == 'from_input':
+                            runtime = c4gli_morning.pars.runtime
+                        else:
+                            runtime = int(args.runtime)
+    
+                
+                        c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                            runtime})
 
-                    c4gl = class4gl(c4gli_morning)
-                    
-                    #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
-                    EFobs = c4gli_morning.pars.EF
-                    
-                    b = c4gli_morning.pars.wwilt
-                    c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
-                    
-                    
-                    try:
-                        #fb = f(b)
-                        c4gli_morning.pars.wg = b
-                        c4gli_morning.pars.w2 = b
+                        c4gli_morning.update(source=expname, pars=exp)
+    
                         c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fb = EFmod - EFobs
-                        EFmodb = EFmod
-                        c4glb = c4gl
-                        c4gli_morningb = c4gli_morning
                         
-                        #fc = f(c)
-                        c4gli_morning.pars.wg = c
-                        c4gli_morning.pars.w2 = c
-                        c4gl = class4gl(c4gli_morning)
-                        c4gl.run()
-                        EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
-                        fc = EFmod - EFobs
-                        print (EFmodb,EFobs,fb)
-                        print (EFmod,EFobs,fc)
-                        c4glc = c4gl
-                        c4gli_morningc = c4gli_morning
-                        i=0
+                        #EFobs = c4gli_morning.pars.BR /(c4gli_morning.pars.BR+1.)
+                        EFobs = c4gli_morning.pars.EF
                         
-
-                        if fc*fb > 0.:
-                            if abs(fb) < abs(fc):
-                                c4gl = c4glb
-                                c4gli_morning = c4gli_morningb
-                            else:
-                                c4gl = c4glc
-                                c4gli_morning = c4gli_morningc
-                            print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                        b = c4gli_morning.pars.wwilt
+                        c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
                         
-                        else:
-                            print('starting ITERATION!!!')
-                            cn  = c - fc/(fc-fb)*(c-b)
-                            
-                            
-                            #fcn = f(cn)
-                            c4gli_morning.pars.wg = np.asscalar(cn)
-                            c4gli_morning.pars.w2 = np.asscalar(cn)
+                        
+                        try:
+                            #fb = f(b)
+                            c4gli_morning.pars.wg = b
+                            c4gli_morning.pars.w2 = b
                             c4gl = class4gl(c4gli_morning)
                             c4gl.run()
-                            fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                            EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                            fb = EFmod - EFobs
+                            EFmodb = EFmod
+                            c4glb = c4gl
+                            c4gli_morningb = c4gli_morning
                             
-                            tol = 0.02
-                            ftol = 10.
-                            maxiter = 10
+                            #fc = f(c)
+                            c4gli_morning.pars.wg = c
+                            c4gli_morning.pars.w2 = c
+                            c4gl = class4gl(c4gli_morning)
+                            c4gl.run()
+                            EFmod = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum())
+                            fc = EFmod - EFobs
+                            print (EFmodb,EFobs,fb)
+                            print (EFmod,EFobs,fc)
+                            c4glc = c4gl
+                            c4gli_morningc = c4gli_morning
+                            i=0
                             
-                            is1=0
-                            is1max=1
-                            while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
-                                if fc * fcn > 0:
-                                    temp = c
-                                    c = b
-                                    b = temp
-                                
-                                a = b
-                                fa = fb
-                                b = c
-                                fb = fc
-                                c = cn
-                                fc = fcn
-                                              
-                                print(i,a,b,c,fcn)
-                                
-                                s1 = c - fc/(fc-fb)*(c-b) 
-                                s2 = c - fc/(fc-fa)*(c-a)
-                                
-                                
-                                # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
-                                
-                                
-                                if (abs(s1-b) < abs(s2-b)):
-                                    is1 = 0
-                                else:
-                                    is1 +=1
-                                    
-                                # we prefer s1, but only allow it a few times to not provide the opposite boundary
-                                if is1 < is1max:           
-                                    s = s1
-                                    print('s1')
+    
+                            if fc*fb > 0.:
+                                if abs(fb) < abs(fc):
+                                    c4gl = c4glb
+                                    c4gli_morning = c4gli_morningb
                                 else:
-                                    is1 = 0
-                                    s = s2
-                                    print('s2')
+                                    c4gl = c4glc
+                                    c4gli_morning = c4gli_morningc
+                                print("Warning!!! function value of the boundaries have the same sign, so I will not able to find a root")
+                            
+                            else:
+                                print('starting ITERATION!!!')
+                                cn  = c - fc/(fc-fb)*(c-b)
                                 
-                                if c > b:
-                                    l = b
-                                    r = c
-                                else:
-                                    l = c
-                                    r = b
-                                
-                                m = (b+c)/2.
-                                     
-                                if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
-                                    cn = s
-                                    print('midpoint')
-                                else:
-                                    cn = m
-                                    print('bissection')
-                                    
                                 
                                 #fcn = f(cn)
                                 c4gli_morning.pars.wg = np.asscalar(cn)
@@ -393,84 +396,153 @@
                                 c4gl.run()
                                 fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
                                 
-                            
-                                i+=1
+                                tol = 0.02
+                                ftol = 10.
+                                maxiter = 10
                                 
-                            if i == maxiter:
-                                raise StopIteration('did not converge')
-
-
-
-
-                        #c4gl = class4gl(c4gli_morning)
-                        #c4gl.run()
-
-                        c4gli_morning.pars.itersteps = i
-                        c4gli_morning.dump(file_ini)
-                        
-                        
-                        c4gl.dump(file_mod,\
-                                      include_input=False,\
-                                   #   timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    except:
-                        print('run not succesfull')
-
-                #iexp = iexp +1
-            file_ini.close()
-            file_mod.close()
-            file_morning.close()
-            file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='mod',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-    #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_mod = records_mod.loc[(STNID,index)]
-    #             c4gl_mod = get_record_yaml(file_station_mod, 
-    #                                         record_mod.index_start, 
-    #                                         record_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
-
+                                is1=0
+                                is1max=1
+                                while (( abs(cn-c) > tol) or ( abs(fcn) > ftol)) and (fcn != 0) and (i < maxiter):
+                                    if fc * fcn > 0:
+                                        temp = c
+                                        c = b
+                                        b = temp
+                                    
+                                    a = b
+                                    fa = fb
+                                    b = c
+                                    fb = fc
+                                    c = cn
+                                    fc = fcn
+                                                  
+                                    print(i,a,b,c,fcn)
+                                    
+                                    s1 = c - fc/(fc-fb)*(c-b) 
+                                    s2 = c - fc/(fc-fa)*(c-a)
+                                    
+                                    
+                                    # take the one that is closest to the border  (opposite to the previous border), making the chance that the border is eliminated is bigger
+                                    
+                                    
+                                    if (abs(s1-b) < abs(s2-b)):
+                                        is1 = 0
+                                    else:
+                                        is1 +=1
+                                        
+                                    # we prefer s1, but only allow it a few times to not provide the opposite boundary
+                                    if is1 < is1max:           
+                                        s = s1
+                                        print('s1')
+                                    else:
+                                        is1 = 0
+                                        s = s2
+                                        print('s2')
+                                    
+                                    if c > b:
+                                        l = b
+                                        r = c
+                                    else:
+                                        l = c
+                                        r = b
+                                    
+                                    m = (b+c)/2.
+                                         
+                                    if ((s > l) and (s < r)):# and (abs(m-b) < abs(s - b)):
+                                        cn = s
+                                        print('midpoint')
+                                    else:
+                                        cn = m
+                                        print('bissection')
+                                        
+                                    
+                                    #fcn = f(cn)
+                                    c4gli_morning.pars.wg = np.asscalar(cn)
+                                    c4gli_morning.pars.w2 = np.asscalar(cn)
+                                    c4gl = class4gl(c4gli_morning)
+                                    c4gl.run()
+                                    fcn = c4gl.out.LE.sum()/(c4gl.out.H.sum() + c4gl.out.LE.sum()) - EFobs
+                                    
+                                
+                                    i+=1
+                                    
+                                if i == maxiter:
+                                    raise StopIteration('did not converge')
+    
+    
+    
+    
+                            #c4gl = class4gl(c4gli_morning)
+                            #c4gl.run()
+    
+                            c4gli_morning.pars.itersteps = i
+                            c4gli_morning.dump(file_ini)
+                            
+                            
+                            c4gl.dump(file_mod,\
+                                          include_input=False,\
+                                       #   timeseries_only=timeseries_only,\
+                                     )
+                            onerun = True
+                        except:
+                            print('run not succesfull')
+    
+                    #iexp = iexp +1
+                file_ini.close()
+                file_mod.close()
+                file_morning.close()
+                file_afternoon.close()
+        
+                if onerun:
+                    records_ini = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='ini',
+                                                               refetch_records=True,
+                                                               )
+                    records_mod = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='end',\
+                                                               refetch_records=True,\
+                                                               )
+                else:
+                    # remove empty files
+                    os.system('rm '+fn_ini)
+                    os.system('rm '+fn_mod)
+        
+        # # align afternoon records with initial records, and set same index
+        # records_afternoon.index = records_afternoon.ldatetime.dt.date
+        # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+        # records_afternoon.index = records_ini.index
+        
+        # stations_for_iter = stations(path_exp)
+        # for STNID,station in stations_iterator(stations_for_iter):
+        #     records_current_station_index = \
+        #             (records_ini.index.get_level_values('STNID') == STNID)
+        #     file_current_station_mod = STNID
+        # 
+        #     with \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
+        #     open(path_soundings+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        #         for (STNID,index),record_ini in records_iterator(records_ini):
+        #             c4gli_ini = get_record_yaml(file_station_ini, 
+        #                                         record_ini.index_start, 
+        #                                         record_ini.index_end,
+        #                                         mode='ini')
+        #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+        # 
+        #             record_mod = records_mod.loc[(STNID,index)]
+        #             c4gl_mod = get_record_yaml(file_station_mod, 
+        #                                         record_mod.index_start, 
+        #                                         record_mod.index_end,
+        #                                         mode='mod')
+        #             record_afternoon = records_afternoon.loc[(STNID,index)]
+        #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+        #                                         record_afternoon.index_start, 
+        #                                         record_afternoon.index_end,
+        #                                         mode='ini')
+    
+if __name__ == '__main__':
+    #execute(**vars(args))
+    execute()

From 98a36a8fdedd9e3faca2f243a81b084ad834c840 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 2 Feb 2019 11:43:34 +0100
Subject: [PATCH 120/129] fix prev record.

---
 class4gl/interface_multi.py      | 2 +-
 class4gl/setup/setup_goamazon.py | 4 +---
 setup.py                         | 2 +-
 3 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index f2a1a2e..8643964 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -474,7 +474,7 @@ def next_record(self,jump=1):
         self.update_record()
 
     def prev_record_event(self,event=None,**kwargs):
-        self,prev_record(**kwargs)
+        self.prev_record(**kwargs)
 
     def prev_record(self,event=None):
         self.next_record(jump=-1)
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
index e0e83aa..9716f69 100644
--- a/class4gl/setup/setup_goamazon.py
+++ b/class4gl/setup/setup_goamazon.py
@@ -456,6 +456,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
     
     humpafn = pair['afternoon'][1]
     balloon_file_afternoon = xr.open_dataset(humpafn)
+    humpafn = pair['morning'][1]
     balloon_file_morning = xr.open_dataset(humpafn)
     print(ipair)
     if (\
@@ -467,9 +468,6 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
         ipair += 1
 
-        print(pair['morning'])
-        humpafn =pair['morning'][1]
-        print(humpafn)
 
         c4gli_morning = humppa_parser(balloon_file_morning,file_morning,date,pair['morning'][0])
         print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
diff --git a/setup.py b/setup.py
index 9e06a36..f3832ba 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.9.1',
+        version='0.9.3',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 1273fb5906243e6166f247557324c310b85a9db4 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Tue, 19 Feb 2019 13:58:35 +0100
Subject: [PATCH 121/129] fixes in the advection rates and profile estimates
 for igra and goamazon

---
 class4gl/class4gl.py                        |  27 +-
 class4gl/interface/interface_new_koeppen.py |   8 +-
 class4gl/interface/interface_stations.py    |  22 +-
 class4gl/interface_functions.py             | 128 ++--
 class4gl/interface_multi.py                 |  56 +-
 class4gl/model.py                           | 189 +++--
 class4gl/setup/batch_setup_igra.py          |   2 +-
 class4gl/setup/setup_bllast.py              | 152 +++--
 class4gl/setup/setup_goamazon.py            | 147 +++-
 class4gl/setup/setup_goamazon_noon.py       | 298 ++++----
 class4gl/setup/setup_humppa.py              | 148 ++--
 class4gl/setup/setup_igra.py                | 199 +++---
 class4gl/simulations/batch_simulations.py   |   3 +-
 class4gl/simulations/simulations.py         | 720 ++++++++++----------
 class4gl/simulations/simulations_iter.py    |  19 +-
 setup.py                                    |   2 +-
 16 files changed, 1193 insertions(+), 927 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index a0de860..0173222 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -183,6 +183,7 @@ def set_pars_defaults(self):
         theta      = 288.   ,  # initial mixed-layer potential temperature [K]
         dtheta     = 1.     ,  # initial temperature jump at h [K]
         gammatheta = 0.006  ,  # free atmosphere potential temperature lapse rate [K m-1]
+        gammatheta_lower_limit = 0.002,
         advtheta   = 0.     ,  # advection of heat [K s-1]
         beta       = 0.2    ,  # entrainment ratio for virtual heat [-]
         wtheta     = 0.1    ,  # surface kinematic heat flux [K m s-1]
@@ -599,16 +600,30 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         #is_valid = (air_balloon.z >= 0)
         # # this is an alternative pipe/numpy method
         # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
-        valid_indices = air_balloon.index[is_valid].values
+        valid_indices = air_balloon.index[is_valid]
         #print(valid_indices)
 
-        dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
+        dpars['Ps'] = air_balloon.p.loc[[valid_indices[0]]][0]
 
         air_balloon['t'] = air_balloon['TEMP']+273.15
         air_balloon['theta'] = (air_balloon.t) * \
                    (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
         air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
 
+
+
+        i = 1
+        t_cut_off = 2.0
+        if t_cut_off is not None:
+            
+            while (air_balloon.thetav.loc[[valid_indices[0]]][0] - \
+                   air_balloon.thetav.loc[valid_indices[i:i+1]][0] ) > t_cut_off:
+                #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
+                air_balloon.thetav.loc[valid_indices[0:i]] = \
+                        air_balloon.thetav.loc[valid_indices[i:i+1]][0] + t_cutoff
+                
+                i +=1
+
         if len(valid_indices) > 0:
             #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
             dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
@@ -635,13 +650,13 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            is_valid_below_h = (air_balloon.loc[valid_indices].z < dpars['h'])
+            valid_indices_below_h =  air_balloon.loc[valid_indices].index[is_valid_below_h].values
             if len(valid_indices) > 1:
                 if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
+                    ml_mean = air_balloon.loc[valid_indices][is_valid_below_h].mean()
                 else:
-                    ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
+                    ml_mean = air_balloon.loc[valid_indices[0:2]].mean()
             elif len(valid_indices) == 1:
                 ml_mean = (air_balloon.iloc[0:1]).mean()
             else:
diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index a950d86..dddb816 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -124,8 +124,6 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 sns.reset_orig()
 
-
-
 lookup_symbols= {
  'A':'equatorial',
  'B':'arid',
@@ -535,6 +533,7 @@ def brightness(rrggbb):
         filter_classess = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
         mod = mod.loc[filter_classes]
         obs = obs.loc[filter_classes]
+
     
         nbins=40       
         x, y = obs.values,mod.values
@@ -702,7 +701,7 @@ def brightness(rrggbb):
     fig.show()  
 
     koeppenlookuptable = koeppenlookuptable.sort_index()
-    if bool(args.show_control_parameters):
+    if args.show_control_parameters == 'True':
 
 
         pkmn_type_colors = [
@@ -797,9 +796,7 @@ def brightness(rrggbb):
             tempdataini = tempdataini.set_index(['source_index','STNID','dates'])
     
 
-            #print('hello2')
             index_intersect = tempdataini.index.intersection(tempdatamodstats.index)
-            #print('hello3')
 
             tempdataini = tempdataini.loc[index_intersect]
             #print('hello4')
@@ -905,6 +902,7 @@ def brightness(rrggbb):
                                  #gridspec_kw=dict(height_ratios=(1, 3), 
                                  gridspec_kw=dict(hspace=0.20,wspace=0.08,top=0.94,bottom=0.06,left=0.15,right=0.99))
 
+        varkey = 'q'
         data_all['d'+varkey+'dt ['+units[varkey]+'/h]'] *= 1000.  
 
         icol = 0
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index a5bbbac..e98bbee 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -146,8 +146,6 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         #axes_taylor[varkey] = fig.add_subplot(2,3,i+3)                                       
     
         #print(obs.std())
-        obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
-        STD_OBS = obs.std()
         dias[varkey] =  TaylorDiagram(1., srange=[0.0,1.7],fig=fig, rect=(230+i+3),label='Reference')
         dias[varkey].add_grid(zorder=-100.)
         dias[varkey]._ax.axis["left"].label.set_text(\
@@ -155,6 +153,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         i += 1
     i = 1                                                                           
     for varkey in ['h','theta','q']:                                                    
+        obs = c4gldata[args.experiments.strip().split()[0]].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
+        STD_OBS = obs.std()
         if i == 1:
             axes[varkey].annotate('Normalized standard deviation',\
                         xy= (0.05,0.36),
@@ -192,7 +192,16 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             RMSE = rmse(obs,mod)                                               
             BIAS = np.mean(mod) - np.mean(obs)
             STD = mod.std()
-            
+
+            # print(STD)
+            # print(PR)
+            print(varkey,STD,STD_OBS,STD/STD_OBS,PR)
+            dias[varkey].add_sample(STD/STD_OBS, PR,
+                           marker='o', ms=5, ls='',
+                           #mfc='k', mec='k', # B&W
+                           mfc=colors[ikey], mec=colors[ikey], # Colors
+                           label=key,zorder=101)
+                
             fit = np.polyfit(x,y,deg=1)
 
             if varkey == 'q':
@@ -261,13 +270,6 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
 
             
-            # print(STD)
-            # print(PR)
-            dias[varkey].add_sample(STD/STD_OBS, PR,
-                           marker='o', ms=5, ls='',
-                           #mfc='k', mec='k', # B&W
-                           mfc=colors[ikey], mec=colors[ikey], # Colors
-                           label=key,zorder=100)
     
         # put ticker position, see
         # https://matplotlib.org/examples/ticks_and_spines/tick-locators.html 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index dd9a007..547e6f8 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -14,6 +14,7 @@
 from interface_functions import *
 #from data_soundings import wyoming
 import yaml
+from yaml import CLoader
 import glob
 import pandas as pd
 import json
@@ -81,29 +82,34 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
     print('index_end',index_end)
     buf =  yaml_file.read(index_end- index_start).replace('inf','9e19').replace('nan','9e19').replace('---','')
 
-    os.system('mkdir -p '+TEMPDIR)
-    filebuffer = open(TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
-    filebuffer.write(buf)
-    filebuffer.close()
-    # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
-    
-    if which('ruby') is None:
-        raise RuntimeError ('ruby is not found. Aborting...')
-    command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
+    # # Ruby way -> fast
+    # os.system('mkdir -p '+TEMPDIR)
+    # filebuffer = open(TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start),'w')
+    # filebuffer.write(buf)
+    # filebuffer.close()
+    # # print("HHHEEELOOOO",filename+'.buffer.yaml'+str(index_start))
+    # 
+    # if which('ruby') is None:
+    #     raise RuntimeError ('ruby is not found. Aborting...')
+    # command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+shortfn+".buffer.yaml."+str(index_start)+"').to_json"+'" > '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start)+' '
+
+    # #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
+    # print(command)
+    # os.system(command)
+    # jsonstream = open(TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
+    # record_dict = json.load(jsonstream)
+    # jsonstream.close()
+    # os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start))
 
-    #command = '/apps/gent/CO7/sandybridge/software/Ruby/2.4.2-foss-2017b/bin/ruby -rjson -ryaml -e "'+"puts YAML.load(ARGF.read()).to_json"+'"'
-    print(command)
-    os.system(command)
-    jsonstream = open(TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
-    record_dict = json.load(jsonstream)
-    jsonstream.close()
-    os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.yaml.'+str(index_start))
+    record_dict = yaml.load(buf,Loader=CLoader)
 
 
     if mode =='model_output':
         modelout = class4gl()
         modelout.load_yaml_dict(record_dict)
-        os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
+
+        # # needed in case of Ruby
+        # os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
 
         return modelout
     elif mode == 'model_input':
@@ -112,11 +118,12 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
         # datetimes are incorrectly converted to strings. We need to convert them
         # again to datetimes
         for key,value in record_dict['pars'].items():
+            # # needed in case of ruby
             # we don't want the key with columns that have none values
-            if value is not None: 
-                if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
-               # elif (type(value) == str):
-                    record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+            # if value is not None: 
+            #     if key in ['lSunrise','lSunset','datetime','ldatetime','ldatetime_daylight','datetime_daylight',]:#(type(value) == str):
+            #    # elif (type(value) == str):
+            #         record_dict['pars'][key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
 
             if (value == 0.9e19) or (value == '.9e19'):
                 record_dict['pars'][key] = np.nan
@@ -127,12 +134,13 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
                 for datakey,datavalue in record_dict[key].items():
                     record_dict[key][datakey] = [ np.nan if (x =='.9e19') else x for x in record_dict[key][datakey]]
 
-        #os.system('rm '+filename+'.buffer.json.'+str(index_start))
 
         c4gli = class4gl_input()
         #print(c4gli.logger,'hello')
         c4gli.load_yaml_dict(record_dict)
-        os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
+
+        # # needed in case of ruby
+        # os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
         return c4gli
 
 
@@ -222,7 +230,7 @@ def get_stations(self,suffix):
         stations_list = []
         for stations_list_file in stations_list_files:
             thisfile = open(stations_list_file,'r')
-            yamlgen = yaml.load_all(thisfile)
+            yamlgen = yaml.load_all(thisfile,Loader=CLoader)
             try:
                 first_record  = yamlgen.__next__()
             except:
@@ -477,16 +485,15 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                         print('pkl file does not exist. I generate "'+\
                               path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                         generate_pkl = True
+                    elif refetch_records:
+                        print('refetch_records flag is True. I regenerate "'+\
+                              path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
+                        generate_pkl = True
                     elif not (os.path.getmtime(path_yaml+'/'+yamlfilename) <  \
                         os.path.getmtime(path_yaml+'/'+pklfilename)):
                         print('pkl file older than yaml file, so I regenerate "'+\
                               path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
                         generate_pkl = True
-
-                    if refetch_records:
-                        print('refetch_records flag is True. I regenerate "'+\
-                              path_yaml+'/'+pklfilename+'" from "'+path_yaml+'/'+yamlfilename+'"...')
-                        generate_pkl = True
                     if not generate_pkl:
                         records_station_chunk = pd.read_pickle(path_yaml+'/'+pklfilename)
                         records_station = pd.concat([records_station,records_station_chunk])
@@ -496,6 +503,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
 
                             dictout = {}
 
+                            #initialization (go to file position just after "---")
                             next_record_found = False
                             end_of_file = False
                             while (not next_record_found) and (not end_of_file):
@@ -504,39 +512,54 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                                 end_of_file = (linebuffer == '')
                             next_tell = yaml_file.tell()
                             
+                            # loop over different yaml profile records
                             while not end_of_file:
 
                                 print(' next record:',next_tell)
                                 current_tell = next_tell
                                 next_record_found = False
                                 yaml_file.seek(current_tell)
-                                os.system('mkdir -p '+TEMPDIR)
-                                filebuffer = open(TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+
+                                # # needed for Ruby
+                                # os.system('mkdir -p '+TEMPDIR)
+                                # filebuffer = open(TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell),'w')
+
                                 linebuffer = ''
+                                stringbuffer = ''
                                 while ( (not next_record_found) and (not end_of_file)):
-                                    filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                    # # needed for Ruby
+                                    # filebuffer.write(linebuffer.replace('inf','0').replace('nan','0'))
+                                    stringbuffer += linebuffer 
                                     linebuffer = yaml_file.readline()
                                     next_record_found = (linebuffer == '---\n')
                                     end_of_file = (linebuffer == '')
-                                filebuffer.close()
+                                # # needed for Ruby
+                                # filebuffer.close()
                                 
                                 next_tell = yaml_file.tell()
                                 index_start = current_tell
                                 index_end = next_tell
 
-                                
-                                if which('ruby') is None:
-                                    raise RuntimeError ('ruby is not found. Aborting...')
-                                #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
-                                command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
-                                print(command)
-                                
-                                os.system(command)
-                                #jsonoutput = subprocess.check_output(command,shell=True) 
-                                #print(jsonoutput)
-                                #jsonstream = io.StringIO(jsonoutput)
-                                jsonstream = open(TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
-                                record = json.load(jsonstream)
+                                # start direct yaml way -> slow
+                                record = yaml.load(stringbuffer,Loader=CLoader)
+                                # end direct way
+
+                                # # # start json ruby way -> much faster 
+                                # if which('ruby') is None:
+                                #     raise RuntimeError ('ruby is not found. Aborting...')
+                                # #if ((irecord >= start) and (np.mod(irecord - start,2) == 0.) :
+                                # command = 'ruby -rjson -ryaml -e "'+"puts YAML.load_file('"+TEMPDIR+'/'+yamlfilename+".buffer.yaml."+str(current_tell)+"').to_json"+'" > '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell)+' ' 
+                                # print(command)
+                                # 
+                                # os.system(command)
+                                # #jsonoutput = subprocess.check_output(command,shell=True) 
+                                # #print(jsonoutput)
+                                # #jsonstream = io.StringIO(jsonoutput)
+                                # jsonstream = open(TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                                # record = json.load(jsonstream)
+                                # # end json ruby way
+
+
                                 dictouttemp = {}
                                 for key,value in record['pars'].items():
                                     # we don't want the key with columns that have none values
@@ -545,22 +568,25 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                                        if (type(value) in regular_numeric_types):
                                             dictouttemp[key] = value
                                        elif key in ['lSunrise','lSunset','datetime','ldatetime','datetime_daylight','datetime_daylight','ldatetime_daylight','ldatetime_daylight']:#(type(value) == str):
-                                           #print (key,value) # dictouttemp[key] = dt.datetime.strptime(value[:-6],"%Y-%m-%d %H:%M:%S")
-                                           dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+                                           dictouttemp[key] = value
+                                           # # needed for Ruby
+                                           # dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
+
                                            # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
                                            dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
                                 recordindex = record['index']
                                 dictouttemp['chunk'] = chunk
                                 dictouttemp['index_start'] = index_start
                                 dictouttemp['index_end'] = index_end
-                                os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
+                                # os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.json.'+str(current_tell))
                                 for key,value in dictouttemp.items():
                                     if key not in dictout.keys():
                                         dictout[key] = {}
                                     dictout[key][(STNID,chunk,recordindex)] = dictouttemp[key]
                                 print(' obs record registered')
-                                jsonstream.close()
-                                os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
+                                # # needed for Ruby
+                                # jsonstream.close()
+                                #  os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
                             records_station_chunk = pd.DataFrame.from_dict(dictout)
                             records_station_chunk.index.set_names(('STNID','chunk','index'),inplace=True)
                             print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 8643964..1795592 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -59,7 +59,7 @@
 os.system('module load Ruby')
 
 class c4gl_interface_soundings(object):
-    def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=False,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False,tendencies_revised=False):
+    def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=False,end_state=True,refetch_stations=True,inputkeys = ['cveg','wg','w2','cc','sp','wwilt','Tsoil','T2','z0m','alpha','LAI',],obs_filter=False,tendencies_revised=False):
         """ creates an interactive interface for analysing class4gl experiments
 
         INPUT:
@@ -83,6 +83,7 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
         self.tendencies_revised = tendencies_revised
         self.path_exp = path_exp
         self.path_forcing = path_forcing
+        self.end_state = end_state
         self.exp_files = glob.glob(self.path_exp+'/?????.yaml')
 
         # # get the list of stations
@@ -122,7 +123,10 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                                            subset='ini',\
                                            refetch_records=refetch_records
                                            )
+        print(self.frames['stats']['records_all_stations_ini'])
         # get its records and load it into the stats frame
+
+
         self.frames['stats']['records_all_stations_end_mod'] =\
                         get_records(self.frames['stats']['stations'].table,\
                                            self.path_exp,\
@@ -138,9 +142,15 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                                                subset='end',\
                                                refetch_records=refetch_records
                                                )
+        if self.end_state:
+            if len(self.frames['stats']['records_all_stations_end_mod']) == 0:
+                raise IOError ('No end state records found. If you want to ignore end states, please use the option end_state = False')
+            self.frames['stats']['records_all_stations_end_mod'].index = \
+                self.frames['stats']['records_all_stations_ini'].index 
 
-        self.frames['stats']['records_all_stations_end_mod'].index = \
-            self.frames['stats']['records_all_stations_ini'].index 
+        else:
+                self.frames['stats']['records_all_stations_end_mod'] = \
+                self.frames['stats']['records_all_stations_ini']
 
         
         if len(self.frames['stats']['records_all_stations_ini']) ==0:
@@ -254,22 +264,21 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.250) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
+                         #(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.00) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25000) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  3.0000) & 
                       #   (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  400.) & 
-                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.00055) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.00055) & 
-                     #    (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -0.0006) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  0.0003) & 
 
                      #     # filter 'extreme' model output -> should be investigated!
-                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
-                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
-                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
                      #     (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
@@ -390,15 +399,28 @@ def update_station(self):
 
         STNID = self.frames['profiles']['STNID']
         chunk = self.frames['profiles']['current_record_chunk']
+        print(chunk)
         if 'current_station_file_ini' in self.frames['profiles'].keys():
             self.frames['profiles']['current_station_file_ini'].close()
+
+
+        fn_ini = format(STNID,"05d")+'_ini.yaml'
+        if not os.path.isfile(self.path_exp+'/'+fn_ini):
+            fn_ini = format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml'
+
         self.frames['profiles']['current_station_file_ini'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_ini.yaml','r')
+            open(self.path_exp+'/'+fn_ini,'r')
 
         if 'current_station_file_end_mod' in self.frames['profiles'].keys():
             self.frames['profiles']['current_station_file_end_mod'].close()
-        self.frames['profiles']['current_station_file_end_mod'] = \
-            open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
+
+
+        if  self.end_state:
+            self.frames['profiles']['current_station_file_end_mod'] = \
+                    open(self.path_exp+'/'+format(STNID,"05d")+'_'+str(chunk)+'_end.yaml','r')
+        else:
+            self.frames['profiles']['current_station_file_end_mod'] = \
+                    open(self.path_exp+'/'+fn_ini,'r')
         if 'current_station_file_end_obs' in self.frames['profiles'].keys():
             self.frames['profiles']['current_station_file_end_obs'].close()
         if self.path_forcing is not None:
@@ -546,8 +568,8 @@ def update_record(self):
             self.frames['profiles']['record_yaml_end_obs'] = \
                get_record_yaml(
                    self.frames['profiles']['current_station_file_end_obs'], \
-                   record_end.index_start,
-                   record_end.index_end,
+                   int(record_end.index_start),
+                   int(record_end.index_end),
                     mode='model_input')
 
 
diff --git a/class4gl/model.py b/class4gl/model.py
index 21e86c3..87dbd99 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -130,14 +130,31 @@ def __init__(self, model_input = None,debug_level=None):
                     # correct pressure of levels according to surface pressure
                     # error (so that interpolation is done in a consistent way)
 
-                    p_e = self.input.Ps - self.input.sp
-                    for irow in self.input.air_ac.index[::-1]:
-                       self.input.air_ac.p.iloc[irow] =\
-                        self.input.air_ac.p.iloc[irow] + p_e
-                       p_e = p_e -\
-                       (self.input.air_ac.p.iloc[irow]+p_e)/\
-                        self.input.air_ac.p.iloc[irow] *\
-                        self.input.air_ac.delpdgrav.iloc[irow]*grav
+                    p_prev = self.input.sp
+                    p_corr_prev = self.input.Ps
+                    #p_corr_prev = self.input.Ps - self.input.sp
+                    air_ac_index = self.input.air_ac.index
+                    for irow,indexrow in list(enumerate(air_ac_index))[::-1]:
+                        p_corr =self.input.air_ac.p.iloc[indexrow]/p_prev*p_corr_prev
+                        p_prev = self.input.air_ac.p.iloc[indexrow]
+
+                        self.input.air_ac.p.iloc[indexrow] = p_corr
+                        p_corr_prev = p_corr
+                       
+                       # p_old = self.input.air_ac.p.iloc[indexrow]
+
+                       # p_new = self.input.air_ac.p.iloc[indexrow] + p_corr
+
+                       # p_corr_next = np.log(self.input.air_ac.p.iloc[air_ac_index[irow-1]]/\
+                       #                      self.input.air_ac.p.iloc[indexrow]*p_corr)
+                       # self.input.air_ac.p.iloc[irow]=p_new
+
+                       # self.input.air_ac.p.iloc[irow] =\
+                       #  self.input.air_ac.p.iloc[irow] + p_e
+                       # p_e = p_e -\
+                       # (self.input.air_ac.p.iloc[irow]+p_e)/\
+                       #  self.input.air_ac.p.iloc[irow] *\
+                       #  self.input.air_ac.delpdgrav.iloc[irow]*grav
 
 
 
@@ -268,6 +285,8 @@ def init(self):
 
         self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
         self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
+        self.gammatheta_lower_limit = \
+                self.input.gammatheta_lower_limit # free atmosphere potential temperature lapse rate lower limit to avoid crashes [K m-1]
         self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
         self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
         self.wtheta     = self.input.wtheta     # surface kinematic heat flux [K m s-1]
@@ -454,6 +473,7 @@ def init(self):
                 self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
 
 
+                # gammatheta, gammaq, gammau, gammav are updated here.
                 self.__dict__['gamma'+var] = \
                     self.air_ap['gamma'+var][np.where(self.h >= \
                                                      self.air_ap.z)[0][-1]]
@@ -509,35 +529,54 @@ def init(self):
                                   found. We just take the bottom one.")
                     in_ml = self.air_ac.index == (len(self.air_ac) - 1)
 
-                for var in ['t','q','u','v']:
+                for var in ['theta','q','u','v']:
     
                    # calculation of the advection variables for the mixed layer
                    # we weight by the hydrostatic thickness of each layer and
                    # divide by the total thickness
-                   self.__dict__['adv'+var] = \
-                            ((self.air_ac['adv'+var+'_x'][in_ml] \
-                             + \
-                             self.air_ac['adv'+var+'_y'][in_ml])* \
-                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                            self.air_ac['delpdgrav'][in_ml].sum()
+                   if var == 'theta':
+                       self.__dict__['adv'+var] = \
+                                ((self.air_ac['advt_x'][in_ml] \
+                                 + \
+                                 self.air_ac['advt_y'][in_ml])* \
+                                self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                                self.air_ac['delpdgrav'][in_ml].sum()
+                   else:
+                       self.__dict__['adv'+var] = \
+                                ((self.air_ac['adv'+var+'_x'][in_ml] \
+                                 + \
+                                 self.air_ac['adv'+var+'_y'][in_ml])* \
+                                self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                                self.air_ac['delpdgrav'][in_ml].sum()
 
                    # calculation of the advection variables for the profile above
                    # (lowest 3 values are not used by class)
                    self.air_ap = self.air_ap.assign(**{'adv'+var : 0.})
-                   self.air_ap['adv'+var] = \
-                           np.interp(self.air_ap.p,\
-                                     self.air_ac.p,\
-                                     self.air_ac['adv'+var+'_x']) \
-                           + \
-                           np.interp(self.air_ap.p, \
-                                       self.air_ac.p, \
-                                       self.air_ac['adv'+var+'_y'])
+
+                   if var == 'theta':
+                       self.air_ap['adv'+var] = \
+                               np.interp(self.air_ap.p,\
+                                         self.air_ac.p,\
+                                         self.air_ac['advt_x']) \
+                               + \
+                               np.interp(self.air_ap.p, \
+                                           self.air_ac.p, \
+                                           self.air_ac['advt_y'])
+                   else:
+                       self.air_ap['adv'+var] = \
+                               np.interp(self.air_ap.p,\
+                                         self.air_ac.p,\
+                                         self.air_ac['adv'+var+'_x']) \
+                               + \
+                               np.interp(self.air_ap.p, \
+                                           self.air_ac.p, \
+                                           self.air_ac['adv'+var+'_y'])
 
                 # as an approximation, we consider that advection of theta in the
                 # mixed layer is equal to advection of t. This is a sufficient
                 # approximation since theta and t are very similar at the surface
                 # pressure.
-                self.__dict__['advtheta'] = self.__dict__['advt']
+                # self.__dict__['advtheta'] = self.__dict__['advt']
 
 
             # # # STRANGE, THIS DOESN'T GIVE REALISTIC VALUES, IT NEEDS TO BE
@@ -559,16 +598,22 @@ def init(self):
             #                      self.air_ach['wrho']) 
             # self.ws   = self.air_ap.w.iloc[1]
 
-            if (self.sw_ac is not None) and ('w' in self.sw_ac):
-                self.air_ap = self.air_ap.assign(wp = 0.)
-                self.air_ap['wp'] = np.interp(self.air_ap.p, \
-                                              self.air_ac.p, \
-                                              self.air_ac['wp'])
+            if (self.sw_ac is not None) and \
+               (('w' in self.sw_ac) or ('adv' in self.sw_ac)):
                 self.air_ap = self.air_ap.assign(R = 0.)
                 self.air_ap['R'] = (self.Rd*(1.-self.air_ap.q) + \
                                                      self.Rv*self.air_ap.q)
                 self.air_ap = self.air_ap.assign(rho = 0.)
+                self.air_ap['t'] = \
+                            self.air_ap.theta * \
+                            (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
                 self.air_ap['rho'] = self.air_ap.p /self.air_ap.R/  self.air_ap.t
+
+            if (self.sw_ac is not None) and ('w' in self.sw_ac):
+                self.air_ap = self.air_ap.assign(wp = 0.)
+                self.air_ap['wp'] = np.interp(self.air_ap.p, \
+                                              self.air_ac.p, \
+                                              self.air_ac['wp'])
                 
                 self.air_ap = self.air_ap.assign(w = 0.)
                 self.air_ap['w'] = -self.air_ap['wp'] /self.air_ap['rho']/self.g
@@ -625,6 +670,7 @@ def init(self):
         self.tstart     = self.input.tstart     # time of the day [-]
         self.cc         = self.input.cc         # cloud cover fraction [-]
         self.Swin       = None                  # incoming short wave radiation [W m-2]
+        self.Swin_cs    = None                  # incoming short wave radiation clearsky [W m-2]
         self.Swout      = None                  # outgoing short wave radiation [W m-2]
         self.Lwin       = None                  # incoming long wave radiation [W m-2]
         self.Lwout      = None                  # outgoing long wave radiation [W m-2]
@@ -1070,7 +1116,7 @@ def integrate_mixed_layer(self):
 
         if (self.sw_ac is not None) and ('adv' in self.sw_ac):
 
-            for var in ['t','q','u','v']:
+            for var in ['theta','q','u','v']:
                 #if ((self.z_pro is not None) and (self.__dict__['adv'+var+'_pro'] is not None)):
 
             # take into account advection for the whole profile
@@ -1101,9 +1147,9 @@ def integrate_mixed_layer(self):
                                                  self.Rv*self.air_ap.q)
 
             # air_aptheta_old = pd.Series(self.air_ap['theta'])
-            self.air_ap['theta'] = \
-                        self.air_ap.t * \
-                        (self.Ps/self.air_ap.p)**(self.air_ap['R']/self.cp)
+            self.air_ap['t'] = \
+                        self.air_ap.theta * \
+                        (self.air_ap.p/self.Ps)**(self.air_ap['R']/self.cp)
         if (self.sw_ac is not None) and ('w' in self.sw_ac):
             zidx_first = np.where(self.air_ap.z > self.h)[0][0]
             self.air_ap.z[zidx_first:] = self.air_ap.z[zidx_first:] + \
@@ -1198,14 +1244,14 @@ def integrate_mixed_layer(self):
             # quantities of moisture and temperature. The limit is set by trial
             # and error. The numerics behind the crash should be investigated
             # so that a cleaner solution can be provided.
-            gammatheta_lower_limit = 0.002
+            # self.gammatheta_lower_limit = 0.002
             while ((itop in range(0,1)) or (itop != ibottom)):
                 theta_mean = air_ap_tail_orig.theta.iloc[ibottom:(itop+1)].mean()
                 z_mean =     air_ap_tail_orig.z.iloc[ibottom:(itop+1)].mean()
                 if (
                     #(z_mean > (z_low+0.2)) and \
                     #(theta_mean > (theta_low+0.02) ) and \
-                    (((theta_mean - theta_low)/(z_mean - z_low)) > gammatheta_lower_limit)) or \
+                    (((theta_mean - theta_low)/(z_mean - z_low)) > self.gammatheta_lower_limit)) or \
                   (itop >= (len(air_ap_tail_orig)-1)) \
                    :
 
@@ -1264,29 +1310,48 @@ def integrate_mixed_layer(self):
             if in_ml.sum() == 0:
                 warnings.warn(" no circulation points in the mixed layer found. We just take the bottom one.")
                 in_ml = self.air_ac.index == (len(self.air_ac) - 1)
-            for var in ['t','q','u','v']:
+            for var in ['theta','q','u','v']:
 
                 # calculation of the advection variables for the mixed-layer
                 # these will be used for the next timestep
                 # Warning: w is excluded for now.
 
-                self.__dict__['adv'+var] = \
-                        ((self.air_ac['adv'+var+'_x'][in_ml] \
-                         + \
-                         self.air_ac['adv'+var+'_y'][in_ml])* \
-                        self.air_ac['delpdgrav'][in_ml]).sum()/ \
-                        self.air_ac['delpdgrav'][in_ml].sum()
+                if var == 'theta':
+                    self.__dict__['adv'+var] = \
+                            ((self.air_ac['advt_x'][in_ml] \
+                             + \
+                             self.air_ac['advt_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
+                else:
+                    self.__dict__['adv'+var] = \
+                            ((self.air_ac['adv'+var+'_x'][in_ml] \
+                             + \
+                             self.air_ac['adv'+var+'_y'][in_ml])* \
+                            self.air_ac['delpdgrav'][in_ml]).sum()/ \
+                            self.air_ac['delpdgrav'][in_ml].sum()
 
                 # calculation of the advection variables for the profile above
                 # the mixed layer (also for the next timestep)
-                self.air_ap['adv'+var] = \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p,\
-                                              self.air_ac['adv'+var+'_x']) \
-                                    + \
-                                    np.interp(self.air_ap.p,\
-                                              self.air_ac.p, \
-                                              self.air_ac['adv'+var+'_y'])
+
+                if var == 'theta':
+                    self.air_ap['adv'+var] = \
+                                        np.interp(self.air_ap.p,\
+                                                  self.air_ac.p,\
+                                                  self.air_ac['advt_x']) \
+                                        + \
+                                        np.interp(self.air_ap.p,\
+                                                  self.air_ac.p, \
+                                                  self.air_ac['advt_y'])
+                else:
+                    self.air_ap['adv'+var] = \
+                                        np.interp(self.air_ap.p,\
+                                                  self.air_ac.p,\
+                                                  self.air_ac['adv'+var+'_x']) \
+                                        + \
+                                        np.interp(self.air_ap.p,\
+                                                  self.air_ac.p, \
+                                                  self.air_ac['adv'+var+'_y'])
                 # if var == 't':
                 #     print(self.air_ap['adv'+var])
                 #     stop
@@ -1296,7 +1361,7 @@ def integrate_mixed_layer(self):
             # approximation since theta and t are very similar at the surface
             # pressure.
 
-            self.__dict__['advtheta'] = self.__dict__['advt']
+            # self.__dict__['advtheta'] = self.__dict__['advt']
 
         if (self.sw_ac is not None) and ('w' in self.sw_ac):
             # update the vertical wind profile
@@ -1364,6 +1429,7 @@ def integrate_mixed_layer(self):
 
                 # Based on the above, update the gamma value at the mixed-layer
                 # top
+                # gammatheta, gammaq, gammau, gammav are updated here.
                 self.__dict__['gamma'+var] = self.air_ap['gamma'+var][np.where(self.h >=
                                                                      self.air_ap.z)[0][-1]]
 
@@ -1378,6 +1444,11 @@ def run_radiation(self):
         Tr  = (0.6 + 0.2 * sinlea) * (1. - 0.4 * self.cc)
   
         self.Swin  = self.S0 * Tr * sinlea
+        self.Swin_cs = self.S0 * (0.6 + 0.2 * sinlea)  * sinlea 
+
+        # Swin/Swin_cs = (1. - 0.4*self.cc)
+        # self.cc = (1.-Swin/Swin_cs)/0.4
+
         self.Swout = self.alpha * self.S0 * Tr * sinlea
         
         
@@ -1814,6 +1885,7 @@ def store(self):
         self.out.Rib[t]        = self.Rib
   
         self.out.Swin[t]       = self.Swin
+        self.out.Swin_cs[t]       = self.Swin_cs
         self.out.Swout[t]      = self.Swout
         self.out.Lwin[t]       = self.Lwin
         self.out.Lwout[t]      = self.Lwout
@@ -1830,6 +1902,12 @@ def store(self):
         self.out.LEref[t]      = self.LEref
         self.out.G[t]          = self.G
 
+        self.out.ws[t]         = self.ws
+        self.out.advtheta[t]   = self.advtheta
+        self.out.advu[t]       = self.advu
+        self.out.advv[t]       = self.advv
+        self.out.advq[t]       = self.advq
+
         self.out.zlcl[t]       = self.lcl
         self.out.RH_h[t]       = self.RH_h
 
@@ -1934,6 +2012,7 @@ def exitmodel(self):
         del(self.tstart)
    
         del(self.Swin)
+        del(self.Swin_cs)
         del(self.Swout)
         del(self.Lwin)
         del(self.Lwout)
@@ -2068,6 +2147,7 @@ def __init__(self, tsteps):
 
         # radiation variables
         self.Swin       = np.zeros(tsteps)    # incoming short wave radiation [W m-2]
+        self.Swin_cs    = np.zeros(tsteps)    # incoming short wave radiation clearsky [W m-2]
         self.Swout      = np.zeros(tsteps)    # outgoing short wave radiation [W m-2]
         self.Lwin       = np.zeros(tsteps)    # incoming long wave radiation [W m-2]
         self.Lwout      = np.zeros(tsteps)    # outgoing long wave radiation [W m-2]
@@ -2085,6 +2165,13 @@ def __init__(self, tsteps):
         self.LEref      = np.zeros(tsteps)    # reference evaporation at rs = rsmin / LAI [W m-2]
         self.G          = np.zeros(tsteps)    # ground heat flux [W m-2]
 
+        self.ws         = np.zeros(tsteps)
+        self.advtheta   = np.zeros(tsteps)
+        self.advu       = np.zeros(tsteps)
+        self.advv       = np.zeros(tsteps)
+        self.advq       = np.zeros(tsteps)
+
+
         # Mixed-layer top variables
         self.zlcl       = np.zeros(tsteps)    # lifting condensation level [m]
         self.RH_h       = np.zeros(tsteps)    # mixed-layer top relative humidity [-]
diff --git a/class4gl/setup/batch_setup_igra.py b/class4gl/setup/batch_setup_igra.py
index 5a33bb9..4f1e432 100644
--- a/class4gl/setup/batch_setup_igra.py
+++ b/class4gl/setup/batch_setup_igra.py
@@ -26,7 +26,7 @@
 parser = argparse.ArgumentParser()
 #parser.add_argument('--timestamp')
 parser.add_argument('--exec') # chunk simulation script
-parser.add_argument('--pbs_string',default=' -l walltime=30:0:0')
+parser.add_argument('--pbs_string',default=' -l walltime=50:0:0')
 parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 # parser.add_argument('--first_YYYYMMDD',default="19810101")
diff --git a/class4gl/setup/setup_bllast.py b/class4gl/setup/setup_bllast.py
index 0de6fc4..bf779d6 100644
--- a/class4gl/setup/setup_bllast.py
+++ b/class4gl/setup/setup_bllast.py
@@ -122,6 +122,14 @@ def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
         
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
         valid_indices = air_balloon.index[is_valid].values
+        i = 1
+        while (air_balloon.thetav.iloc[valid_indices[0]] - \
+               air_balloon.thetav.iloc[valid_indices[i]] ) > 0.5:
+            #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
+            air_balloon.thetav.iloc[valid_indices[0:i]] = \
+                air_balloon.thetav.iloc[valid_indices[i]] + 0.5 
+            
+            i +=1
         
         air_ap_mode='b'
         
@@ -151,11 +159,11 @@ def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            is_valid_below_h = (air_balloon.iloc[valid_indices].z < dpars['h'])
+            valid_indices_below_h =  air_balloon.iloc[valid_indices].index[is_valid_below_h].values
             if len(valid_indices) > 1:
                 if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
+                    ml_mean = air_balloon.iloc[valid_indices][is_valid_below_h].mean()
                 else:
                     ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
             elif len(valid_indices) == 1:
@@ -423,7 +431,7 @@ def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
 path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
 
 
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_ini.yaml','w') 
 for date,pair  in HOUR_FILES.items(): 
     print(pair['morning'])
     humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['morning'][1]
@@ -435,7 +443,7 @@ def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
     print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
 file_morning.close()
 
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_end.yaml','w') 
 for date,pair  in HOUR_FILES.items(): 
     humpafn ='/user/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/BLLAST/MODEM Radiosoundings/'+pair['afternoon'][1]
     balloon_file = open(humpafn,'r',encoding='latin-1')
@@ -471,82 +479,82 @@ def bllast_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
 
 records_morning = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='morning',
+                                           subset='ini',
                                            refetch_records=True,
                                            )
 print('records_morning_ldatetime',records_morning.ldatetime)
 
 records_afternoon = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='afternoon',
+                                           subset='end',
                                            refetch_records=True,
                                            )
 
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-
-    
-    c4gli_morning.pars.sw_ac = []
-    c4gli_morning.pars.sw_ap = True
-    c4gli_morning.pars.sw_lit = False
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
+# # align afternoon records with noon records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+# records_afternoon.index = records_morning.index
+# path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
+# 
+# os.system('mkdir -p '+path_exp)
+# file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
+# file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
+# file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+# file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+# 
+# for (STNID,chunk,index),record_morning in records_morning.iterrows():
+#     record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+# 
+#     c4gli_morning = get_record_yaml(file_morning, 
+#                                     record_morning.index_start, 
+#                                     record_morning.index_end,
+#                                     mode='ini')
+#     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+#     
+#     
+#     c4gli_afternoon = get_record_yaml(file_afternoon, 
+#                                       record_afternoon.index_start, 
+#                                       record_afternoon.index_end,
+#                                     mode='ini')
+# 
+#     c4gli_morning.update(source='pairs',pars={'runtime' : \
+#                         int((c4gli_afternoon.pars.datetime_daylight - 
+#                              c4gli_morning.pars.datetime_daylight).total_seconds())})
+# 
+#     
+#     c4gli_morning.pars.sw_ac = []
+#     c4gli_morning.pars.sw_ap = True
+#     c4gli_morning.pars.sw_lit = False
+#     c4gli_morning.dump(file_ini)
+#     
+#     c4gl = class4gl(c4gli_morning)
+#     c4gl.run()
+#     
+#     c4gl.dump(file_mod,\
+#               include_input=False,\
+#               timeseries_only=timeseries_only)
+# file_ini.close()
+# file_mod.close()
+# file_morning.close()
+# file_afternoon.close()
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='ini',
+#                                            refetch_records=True,
+#                                            )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='mod',
+#                                            refetch_records=True,
+#                                            )
+# 
+# records_mod.index = records_ini.index
+# 
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+# records_afternoon.index = records_ini.index
 
 
 
diff --git a/class4gl/setup/setup_goamazon.py b/class4gl/setup/setup_goamazon.py
index 9716f69..18f5612 100644
--- a/class4gl/setup/setup_goamazon.py
+++ b/class4gl/setup/setup_goamazon.py
@@ -23,7 +23,7 @@
 Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
 epsilon = Rd/Rv # or mv/md
 
-path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/'
+path_soundings_in = '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/radio_all/'
 
 def replace_iter(iterable, search, replace):
     for value in iterable:
@@ -70,19 +70,50 @@ def replace_iter(iterable, search, replace):
 HOUR_FILES = {}
 for iDT, DT in enumerate(DTS):
     morning_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*cdf')
-    if len(possible_files)>0:
-        morning_file= possible_files[0]
+    possible_files_morning =\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.11??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.10??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.09??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.08??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.12??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.13??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.14??00.*cdf')+\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.15??00.*cdf')
+    # glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.07??00.*cdf')+\
+    # glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.06??00.*cdf')+\
+    # glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*cdf')+\
+    if len(possible_files_morning)>0:
+        ix = 0
+        while ((ix < (len(possible_files_morning))) and (morning_file is None)):
+            # print (xr.open_dataset(possible_files_morning[ix]).pres.shape)
+            # print(xr.open_dataset(possible_files_morning[ix]).pres.shape[0] >= 10)
+            if (xr.open_dataset(possible_files_morning[ix]).pres.shape[0] >= 500) and\
+                (possible_files_morning[ix].split('/')[-1] != 'maosondewnpnM1.b1.20150103.115200.custom.cdf') and \
+                (possible_files_morning[ix].split('/')[-1] != 'maosondewnpnM1.b1.20150103.115200.custom.cdf'):
+                morning_file = possible_files_morning[ix]
+            ix +=1
+
     afternoon_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*cdf')
-    if len(possible_files)>0:
-        afternoon_file= possible_files[0]
+    possible_files_afternoon =\
+        glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.20??00.*cdf')+\
+        glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.19??00.*cdf')+\
+        glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.18??00.*cdf')+\
+        glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.17??00.*cdf')+\
+        glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.16??00.*cdf')
+    if len(possible_files_afternoon)>0:
+        ix = 0
+        while ((ix < (len(possible_files_afternoon))) and (afternoon_file is None)):
+            # print (xr.open_dataset(possible_files_afternoon[ix]).pres.shape)
+            # print(xr.open_dataset(possible_files_afternoon[ix]).pres.shape[0] >= 10)
+            if (xr.open_dataset(possible_files_afternoon[ix]).pres.shape[0] >= 300) and \
+               (possible_files_afternoon[ix].split('/')[-1] != '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/maosondewnpnM1.b1.20140823.143600.cdf'):
+                afternoon_file = possible_files_afternoon[ix]
+            ix +=1
 
     if (morning_file is not None) and (afternoon_file is not None):
-        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
-                          'afternoon':[17.5,afternoon_file]}
-
-print(HOUR_FILES)
+        HOUR_FILES[DT] = {'morning':  [int(morning_file.split('/')[-1].split('.')[3])/10000.,morning_file],
+                          'afternoon':[ int(afternoon_file.split('/')[-1].split('.')[3])/10000. ,afternoon_file]}
+        print(HOUR_FILES[DT])
 
 # HOUR_FILES = \
 # {
@@ -162,12 +193,13 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         if 'alt' in xrin:
             air_balloon['z'] = xrin.alt.values
         else:
+            g          = 9.81                  # gravity acceleration [m s-2]
             air_balloon['z'] = 0.
             for irow,row in air_balloon.iloc[1:].iterrows():
                 air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
-                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow])/g * \
                         (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
-                        
+                    
              
         for varname,lfunction in rowmatches.items():
             air_balloon[varname] = lfunction(air_balloon)
@@ -189,15 +221,38 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
         air_balloon = air_balloon.iloc[:].reset_index().drop(['index'],axis=1)
         
+        # if air_balloon.z.max() > 100000.:
+        #     air_balloon.z = air_balloon.z/10.
+
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
         valid_indices = air_balloon.index[is_valid].values
         
         air_ap_mode='b'
         
+        i = 1
+        while (air_balloon.thetav.iloc[valid_indices[0]] - \
+               air_balloon.thetav.iloc[valid_indices[i]] ) > 0.5:
+            #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
+            air_balloon.thetav.iloc[valid_indices[0:i]] = \
+                air_balloon.thetav.iloc[valid_indices[i]] + 0.5 
+            
+            i +=1
+
+
+        # while ((len(valid_indices) > 10) and
+        #        ((air_balloon.theta.iloc[valid_indices[0]] -
+        #          air_balloon.theta.iloc[valid_indices[1]]) > 0.5)):
+        #     valid_indices = valid_indices[1:]
+
+        #theta_vs_first_inconsistent = True
+        # while theta_vs_first_inconsistent:
+        
         if len(valid_indices) > 0:
-            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            air_balloon_temp = air_balloon.iloc[valid_indices]
+            print(air_balloon_temp)
+            print(air_balloon_temp.z.shape,air_balloon_temp.thetav.shape,)
             dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+                blh(air_balloon_temp.z.values,air_balloon_temp.thetav.values,air_balloon_temp.WSPD.values)
             dpars['h_b'] = np.max((dpars['h'],10.))
             dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
             dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
@@ -221,11 +276,11 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            is_valid_below_h = (air_balloon.iloc[valid_indices].z < dpars['h'])
+            valid_indices_below_h =  air_balloon.iloc[valid_indices].index[is_valid_below_h].values
             if len(valid_indices) > 1:
                 if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
+                    ml_mean = air_balloon.iloc[valid_indices][is_valid_below_h].mean()
                 else:
                     ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
             elif len(valid_indices) == 1:
@@ -235,15 +290,26 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
                 temp.iloc[0] = np.nan
                 ml_mean = temp
                        
+
+
             dpars['theta']= ml_mean.theta
             dpars['q']    = ml_mean.q
             dpars['u']    = ml_mean.u
             dpars['v']    = ml_mean.v 
+            # theta_vs_first_inconsistent = \
+            #     ((air_balloon.theta.iloc[valid_indices[0]] - air_balloon.theta.iloc[valid_indices[1]]) > 0.2)
+            # theta_vs_first_inconsistent = \
+            #     ((air_balloon.theta.iloc[valid_indices[0]] - dpars['theta']) > 0.1)
+            # if theta_vs_first_inconsistent:
+            #     valid_indices = valid_indices[1:]
+            #     print("warning! too large difference between near surface value and abl value of theta. I'm taking the next one as near surface vlue")
         else:
             dpars['theta'] = np.nan
             dpars['q'] = np.nan
             dpars['u'] = np.nan
             dpars['v'] = np.nan
+            # theta_bl_inconsistent = False
+
         
         air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
         # All other  data points above the mixed-layer fit
@@ -343,8 +409,14 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         dpars['lon'] = 0.
         # this is the real longitude that will be used to extract ground data
         
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=+4)
+        # dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        # dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=+4)
+
+
+        dpars['datetime'] =  ldate+dt.timedelta(hours=lhour)
+        dpars['ldatetime'] =  dpars['datetime'] + dt.timedelta(hours=dpars['longitude']/360.*24.)
+
+
         dpars['doy'] = dpars['datetime'].timetuple().tm_yday
         
         dpars['SolarAltitude'] = \
@@ -409,6 +481,9 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
                     air_balloon[column] = air_balloon[column].round(decimal)
                     air_ap[column] = air_ap[column].round(decimal)
         
+
+
+        dpars['gammatheta_lower_limit'] = 0.0001
         updateglobal = False
         if c4gli is None:
             c4gli = class4gl_input()
@@ -427,7 +502,9 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # if profile_ini:
         #     c4gli.runtime = 10 * 3600
 
-        c4gli.dump(file_sounding)
+        # if not ((dpars['ldatetime'].hour <=12) or\
+        #    ((dpars['lSunset'].hour - dpars['ldatetime'].hour) >= (2.))):
+        #     c4gli = None
         
         # if profile_ini:
         #     c4gl = class4gl(c4gli)
@@ -447,30 +524,38 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
 
 
 # path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GOAMAZON2/'
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GOAMAZON6/'
 
+if os.path.isdir(path_soundings):
+    print("Warning, I'm removing "+path_soundings+" in 10 seconds. Press ctrl-c to cancel")
+    os.system('rm '+path_soundings)
 file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_end.yaml','w') 
 file_morning = open(path_soundings+format(current_station.name,'05d')+'_ini.yaml','w') 
 ipair = 0
 for date,pair  in HOUR_FILES.items(): 
     
+    print(date,ipair,pair)
     humpafn = pair['afternoon'][1]
     balloon_file_afternoon = xr.open_dataset(humpafn)
     humpafn = pair['morning'][1]
     balloon_file_morning = xr.open_dataset(humpafn)
-    print(ipair)
     if (\
         (balloon_file_morning.pres.shape[0] > 10) and \
         (balloon_file_afternoon.pres.shape[0] > 10)\
         ):
-
+        print('filename',pair['afternoon'][1],date,pair['afternoon'][0])
         c4gli_afternoon = humppa_parser(balloon_file_afternoon,file_afternoon,date,pair['afternoon'][0])
-        print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
         ipair += 1
+        
 
-
+        print('filename',pair['morning'][1],date,pair['afternoon'][0])
         c4gli_morning = humppa_parser(balloon_file_morning,file_morning,date,pair['morning'][0])
-        print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+        if (c4gli_morning is not None) and (c4gli_afternoon is not None):
+            print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
+            print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
+            c4gli_morning.dump(file_morning)
+            c4gli_afternoon.dump(file_afternoon)
+
 
 print(ipair)
 file_afternoon.close()
@@ -503,15 +588,15 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
 
 records_morning = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='ini',
-                                           refetch_records=True,
+                                           subset='ini',\
+                                           refetch_records=True,\
                                            )
 print('records_morning_ldatetime',records_morning.ldatetime)
 
 records_afternoon = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='end',
-                                           refetch_records=True,
+                                           subset='end',\
+                                           refetch_records=True,\
                                            )
 
 # align afternoon records with noon records, and set same index
diff --git a/class4gl/setup/setup_goamazon_noon.py b/class4gl/setup/setup_goamazon_noon.py
index c99e913..efd9333 100644
--- a/class4gl/setup/setup_goamazon_noon.py
+++ b/class4gl/setup/setup_goamazon_noon.py
@@ -62,38 +62,31 @@ def replace_iter(iterable, search, replace):
  'Range[m]',
 ]
 
-#DTSTART = dt.datetime(2014,1,1,0,0,0,0,pytz.UTC)
-#DTEND = dt.datetime(2015,5,16,0,0,0,0,pytz.UTC)
-#DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
-
-DTS = [dt.datetime(2014,11,15,0,0,0,0,pytz.UTC), 
-       dt.datetime(2014,12,29,0,0,0,0,pytz.UTC),
-       dt.datetime(2015,1,5,0,0,0,0,pytz.UTC),
-       dt.datetime(2015,5,7,0,0,0,0,pytz.UTC)
-      ]
-
-
-
+DTSTART = dt.datetime(2014,9,1,0,0,0,0,pytz.UTC)
+DTEND = dt.datetime(2014,10,1,0,0,0,0,pytz.UTC)
 
 
+DTS = [DTSTART+dt.timedelta(days=day) for day in range(0, int((DTEND-DTSTART).total_seconds()/3600./24.))]
 HOUR_FILES = {}
 for iDT, DT in enumerate(DTS):
     morning_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.05??00.*.cdf')
-    if len(possible_files)>0:
-        morning_file= possible_files[0]
-    afternoon_file = None
-    possible_files = glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.12??00.*.cdf')+\
-                     glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.11??00.*.cdf')+\
-                     glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.10??00.*.cdf')
-    if len(possible_files)>0:
-        afternoon_file= possible_files[-1]
-        hour_afternoon = int(afternoon_file[-17:-15])+float(afternoon_file[-15:-13])/60.
-    if (morning_file is not None) and (afternoon_file is not None):
-        HOUR_FILES[DT] = {'morning':[5.5,morning_file],
-                          'afternoon':[hour_afternoon,afternoon_file]}
-
-print(HOUR_FILES)
+    possible_files_morning =\
+    glob.glob(path_soundings_in+'/maosondewnpnM1.b1.'+DT.strftime("%Y%m%d")+'.23??00.*cdf')
+    if len(possible_files_morning)>0:
+        ix = 0
+        while ((ix < (len(possible_files_morning))) and (morning_file is None)):
+            # print (xr.open_dataset(possible_files_morning[ix]).pres.shape)
+            # print(xr.open_dataset(possible_files_morning[ix]).pres.shape[0] >= 10)
+            if (xr.open_dataset(possible_files_morning[ix]).pres.shape[0] >= 500) :
+            # and\
+            #   (possible_files_morning[ix] != '/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/GOAMAZON/Radiosounding_ARM/maosondewnpnM1.b1.20150103.115200.custom.cdf'):
+                morning_file = possible_files_morning[ix]
+            ix +=1
+
+
+    if (morning_file is not None):
+        HOUR_FILES[DT] = {'morning':  [int(morning_file.split('/')[-1].split('.')[3])/10000.,morning_file], }
+        print(HOUR_FILES[DT])
 
 # HOUR_FILES = \
 # {
@@ -127,8 +120,6 @@ def efrom_rh100_T(rh100,T):
 def qfrom_e_p(e,p):
     return epsilon * e/(p - (1.-epsilon)*e)
 
-
-
 def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         print(balloon_file)
         
@@ -157,38 +148,37 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         #                              header=None,
         #                              names = columns,
         #                              na_values='-----')
-    
-
         
-        rowmatches = {
+        print(air_balloon['p'][0])
+        varcalc = {
             'R' :    lambda x: (Rd*(1.-x.q) + Rv*x.q),
+            'pp': lambda x: (x['p'][0]/x['p'])**(x['R']/cp),
             'theta': lambda x: (x['t']) * (x['p'][0]/x['p'])**(x['R']/cp),
             'thetav': lambda x: x.theta  + 0.61 * x.theta * x.q,
             'rho': lambda x: x.p /x.t / x.R ,
         }
-        for varname,lfunction in rowmatches.items():
+        for varname,lfunction in varcalc.items():
             air_balloon[varname] = lfunction(air_balloon)
         
         print('alt in xrin?:','alt' in xrin)
         if 'alt' in xrin:
             air_balloon['z'] = xrin.alt.values
         else:
+            g          = 9.81                  # gravity acceleration [m s-2]
             air_balloon['z'] = 0.
             for irow,row in air_balloon.iloc[1:].iterrows():
                 air_balloon['z'].iloc[irow] = air_balloon['z'].iloc[irow-1] - \
-                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow]) * \
+                        2./(air_balloon['rho'].iloc[irow-1]+air_balloon['rho'].iloc[irow])/g * \
                         (air_balloon['p'].iloc[irow] - air_balloon['p'].iloc[irow-1])
-                        
              
-        for varname,lfunction in rowmatches.items():
-            air_balloon[varname] = lfunction(air_balloon)
+        # for varname,lfunction in varcakc.items():
+        #     air_balloon[varname] = lfunction(air_balloon)
         
         dpars = {}
         dpars['longitude']  = current_station['longitude']
         dpars['latitude']  = current_station['latitude'] 
         
         dpars['STNID'] = current_station.name
-        
 
         # # there are issues with the lower measurements in the HUMPPA campaign,
         # # for which a steady decrease of potential temperature is found, which
@@ -200,15 +190,29 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # air_balloon = air_balloon.iloc[ifirst:].reset_index().drop(['index'],axis=1)
         air_balloon = air_balloon.iloc[:].reset_index().drop(['index'],axis=1)
         
+        # if air_balloon.z.max() > 100000.:
+        #     air_balloon.z = air_balloon.z/10.
+
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
         valid_indices = air_balloon.index[is_valid].values
         
         air_ap_mode='b'
+
+
+        while ((len(valid_indices) > 10) and
+               ((air_balloon.theta.iloc[valid_indices[0]] -
+                 air_balloon.theta.iloc[valid_indices[1]]) > 0.5)):
+            valid_indices = valid_indices[1:]
+
+        #theta_vs_first_inconsistent = True
+        # while theta_vs_first_inconsistent:
         
         if len(valid_indices) > 0:
-            print(air_balloon.z.shape,air_balloon.thetav.shape,)
+            air_balloon_temp = air_balloon.iloc[valid_indices]
+            print(air_balloon_temp)
+            print(air_balloon_temp.z.shape,air_balloon_temp.thetav.shape,)
             dpars['h'],dpars['h_u'],dpars['h_l'] =\
-                blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+                blh(air_balloon_temp.z.values,air_balloon_temp.thetav.values,air_balloon_temp.WSPD.values)
             dpars['h_b'] = np.max((dpars['h'],10.))
             dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
             dpars['h_l'] = np.max((dpars['h_l'],10.)) #low limit of mixed layer height
@@ -220,8 +224,6 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
             dpars['h_e'] =np.nan
             dpars['h'] =np.nan
         
-        
-        
         if ~np.isnan(dpars['h']):
             dpars['Ps'] = air_balloon.p.iloc[valid_indices[0]]
         else:
@@ -232,11 +234,11 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            is_valid_below_h = (air_balloon.iloc[valid_indices].z < dpars['h'])
+            valid_indices_below_h =  air_balloon.iloc[valid_indices].index[is_valid_below_h].values
             if len(valid_indices) > 1:
                 if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
+                    ml_mean = air_balloon.iloc[valid_indices][is_valid_below_h].mean()
                 else:
                     ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
             elif len(valid_indices) == 1:
@@ -245,23 +247,29 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
                 temp =  pd.DataFrame(air_balloon)
                 temp.iloc[0] = np.nan
                 ml_mean = temp
-                       
+
             dpars['theta']= ml_mean.theta
             dpars['q']    = ml_mean.q
             dpars['u']    = ml_mean.u
             dpars['v']    = ml_mean.v 
+            # theta_vs_first_inconsistent = \
+            #     ((air_balloon.theta.iloc[valid_indices[0]] - air_balloon.theta.iloc[valid_indices[1]]) > 0.2)
+            # theta_vs_first_inconsistent = \
+            #     ((air_balloon.theta.iloc[valid_indices[0]] - dpars['theta']) > 0.1)
+            # if theta_vs_first_inconsistent:
+            #     valid_indices = valid_indices[1:]
+            #     print("warning! too large difference between near surface value and abl value of theta. I'm taking the next one as near surface vlue")
         else:
             dpars['theta'] = np.nan
             dpars['q'] = np.nan
             dpars['u'] = np.nan
             dpars['v'] = np.nan
-        
+            # theta_bl_inconsistent = False
+
         air_ap_head = air_balloon[0:0] #pd.DataFrame(columns = air_balloon.columns)
         # All other  data points above the mixed-layer fit
         air_ap_tail = air_balloon[air_balloon.z > dpars['h']]
 
-
-
         air_ap_head.z = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
         jump = air_ap_head.iloc[0] * np.nan
         
@@ -297,17 +305,15 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         
         air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
 
-
-
         # only select samples monotonically increasing with height
         air_ap_tail_orig = pd.DataFrame(air_ap_tail)
         air_ap_tail = pd.DataFrame()
+        print(air_ap_tail_orig)
         air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[0],ignore_index=True)
         for ibottom in range(1,len(air_ap_tail_orig)):
             if air_ap_tail_orig.iloc[ibottom].z > air_ap_tail.iloc[-1].z +10.:
                 air_ap_tail = air_ap_tail.append(air_ap_tail_orig.iloc[ibottom],ignore_index=True)
 
-
         # make theta increase strong enough to avoid numerical
         # instability
         air_ap_tail_orig = pd.DataFrame(air_ap_tail)
@@ -347,16 +353,20 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         air_ap['p'].iloc[1] =(dpars['Ps'] - rho * g * dpars['h'])
         air_ap['p'].iloc[2] =(dpars['Ps'] - rho * g * dpars['h'] -0.1)
         
-        
         dpars['lat'] = dpars['latitude']
         # this is set to zero because we use local (sun) time as input (as if we were in Greenwhich)
         dpars['lon'] = 0.
         # this is the real longitude that will be used to extract ground data
         
-        print('ldate',ldate)
-        print('lhour',lhour)
-        dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
-        dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=-4)
+        # dpars['ldatetime'] = ldate+dt.timedelta(hours=lhour)
+        # dpars['datetime'] =  dpars['ldatetime'] + dt.timedelta(hours=+4)
+
+        dpars['datetime'] =  ldate+dt.timedelta(hours=lhour)
+        dpars['ldatetime'] =  dpars['datetime'] + dt.timedelta(hours=-4)
+
+
+
+
         dpars['doy'] = dpars['datetime'].timetuple().tm_yday
         
         dpars['SolarAltitude'] = \
@@ -371,13 +381,12 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
                                     dpars['datetime']\
                                 )
         
-        
         dpars['lSunrise'], dpars['lSunset'] \
         =  Pysolar.util.GetSunriseSunset(dpars['latitude'],
                                          0.,
                                          dpars['ldatetime'],0.)
         
-        # Warning!!! Unfortunately!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
+        # Warning!!! Unfortunatly!!!! WORKAROUND!!!! Even though we actually write local solar time, we need to assign the timezone to UTC (which is WRONG!!!). Otherwise ruby cannot understand it (it always converts tolocal computer time :( ). 
         dpars['lSunrise'] = pytz.utc.localize(dpars['lSunrise'])
         dpars['lSunset'] = pytz.utc.localize(dpars['lSunset'])
         
@@ -421,6 +430,9 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
                     air_balloon[column] = air_balloon[column].round(decimal)
                     air_ap[column] = air_ap[column].round(decimal)
         
+
+
+        dpars['gammatheta_lower_limit'] = 0.0001
         updateglobal = False
         if c4gli is None:
             c4gli = class4gl_input()
@@ -428,7 +440,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         
         print('updating...')
         print(column)
-        c4gli.update(source='humppa',\
+        c4gli.update(source='goamazon',\
                     # pars=pars,
                     pars=dpars,\
                     air_balloon=air_balloon,\
@@ -439,7 +451,9 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         # if profile_ini:
         #     c4gli.runtime = 10 * 3600
 
-        c4gli.dump(file_sounding)
+        if not ((dpars['ldatetime'].hour <=12) or\
+           ((dpars['lSunset'].hour - dpars['ldatetime'].hour) >= (2.))):
+            c4gli = None
         
         # if profile_ini:
         #     c4gl = class4gl(c4gli)
@@ -458,29 +472,25 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
         return c4gli
 
 
-path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS_NOON/'
-
-
+# path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
+path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/GOAMAZON_EVENING/'
 os.system('mkdir -p '+path_soundings)
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
-for date,pair  in HOUR_FILES.items(): 
-    print(pair['morning'])
-    humpafn =pair['morning'][1]
-    print(humpafn)
-    balloon_file = xr.open_dataset(humpafn)
-
-    c4gli_morning = humppa_parser(balloon_file,file_morning,date,pair['morning'][0])
-    print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
-file_morning.close()
 
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_ini.yaml','w') 
+ipair = 0
 for date,pair  in HOUR_FILES.items(): 
-    humpafn = pair['afternoon'][1]
-    balloon_file = xr.open_dataset(humpafn)
+    
+    humpafn = pair['morning'][1]
+    balloon_file_morning = xr.open_dataset(humpafn)
+    if (balloon_file_morning.pres.shape[0] > 10):
+        c4gli_morning = humppa_parser(balloon_file_morning,file_morning,date,pair['morning'][0])
+        if (c4gli_morning is not None) :
+            c4gli_morning.dump(file_morning)
+        ipair += 1
+
+print(ipair)
+file_morning.close()
 
-    c4gli_afternoon = humppa_parser(balloon_file,file_afternoon,date,pair['afternoon'][0])
-    print('c4gli_afternoon_ldatetime 0',c4gli_afternoon.pars.ldatetime)
-file_afternoon.close()
  
 
 # file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
@@ -506,82 +516,70 @@ def humppa_parser(balloon_file,file_sounding,ldate,lhour,c4gli=None):
 # 
 # file_model    = open(fnout_model+   format(current_station.name,'05d')+'.yaml','w') 
 
-
 records_morning = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='morning',
-                                           refetch_records=True,
-                                           )
-print('records_morning_ldatetime',records_morning.ldatetime)
-
-records_afternoon = get_records(pd.DataFrame([current_station]),\
-                                           path_soundings,\
-                                           subset='afternoon',
-                                           refetch_records=True,
+                                           subset='ini',\
+                                           refetch_records=True,\
                                            )
 
 # align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS_NOON/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
 
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
+# os.system('mkdir -p '+path_exp)
+# file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_ini.yaml')
+# file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_end.yaml')
+# file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+# file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
+# 
+# for (STNID,chunk,index),record_morning in records_morning.iterrows():
+#     record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+# 
+#     c4gli_morning = get_record_yaml(file_morning, 
+#                                     record_morning.index_start, 
+#                                     record_morning.index_end,
+#                                     mode='ini')
+#     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+#     
+#     
+#     c4gli_afternoon = get_record_yaml(file_afternoon, 
+#                                       record_afternoon.index_start, 
+#                                       record_afternoon.index_end,
+#                                     mode='ini')
+# 
+#     c4gli_morning.update(source='pairs',pars={'runtime' : \
+#                         int((c4gli_afternoon.pars.datetime_daylight - 
+#                              c4gli_morning.pars.datetime_daylight).total_seconds())})
+#     c4gli_morning.update(source='manual',
+#                          pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+#     c4gli_morning.dump(file_ini)
+#     
+#     c4gl = class4gl(c4gli_morning)
+#     c4gl.run()
+#     
+#     c4gl.dump(file_mod,\
+#               include_input=False,\
+#               timeseries_only=timeseries_only)
+# file_ini.close()
+# file_mod.close()
+# file_morning.close()
+# file_afternoon.close()
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='ini',
+#                                            refetch_records=True,
+#                                            )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='mod',
+#                                            refetch_records=True,
+#                                            )
+# 
+# records_mod.index = records_ini.index
+# 
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+# records_afternoon.index = records_ini.index
 
 
 """
diff --git a/class4gl/setup/setup_humppa.py b/class4gl/setup/setup_humppa.py
index b7bcc7c..273e039 100644
--- a/class4gl/setup/setup_humppa.py
+++ b/class4gl/setup/setup_humppa.py
@@ -161,6 +161,14 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
         
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
         valid_indices = air_balloon.index[is_valid].values
+        i = 1
+        while (air_balloon.thetav.iloc[valid_indices[0]] - \
+               air_balloon.thetav.iloc[valid_indices[i]] ) > 0.5:
+            #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
+            air_balloon.thetav.iloc[valid_indices[0:i]] = \
+                air_balloon.thetav.iloc[valid_indices[i]] + 0.5 
+            
+            i +=1
         
         air_ap_mode='b'
         
@@ -190,11 +198,11 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = is_valid & (air_balloon.z < dpars['h'])
-            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            is_valid_below_h = (air_balloon.iloc[valid_indices].z < dpars['h'])
+            valid_indices_below_h =  air_balloon.iloc[valid_indices].index[is_valid_below_h].values
             if len(valid_indices) > 1:
                 if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon[is_valid_below_h].mean()
+                    ml_mean = air_balloon.iloc[valid_indices][is_valid_below_h].mean()
                 else:
                     ml_mean = air_balloon.iloc[valid_indices[0]:valid_indices[1]].mean()
             elif len(valid_indices) == 1:
@@ -203,7 +211,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
                 temp =  pd.DataFrame(air_balloon)
                 temp.iloc[0] = np.nan
                 ml_mean = temp
-                       
+                      
             dpars['theta']= ml_mean.theta
             dpars['q']    = ml_mean.q
             dpars['u']    = ml_mean.u
@@ -441,7 +449,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
 path_soundings = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS/'
 
 
-file_morning = open(path_soundings+format(current_station.name,'05d')+'_morning.yaml','w') 
+file_morning = open(path_soundings+format(current_station.name,'05d')+'_ini.yaml','w') 
 for date,pair  in HOUR_FILES.items(): 
     print(pair['morning'])
     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['morning'][1]
@@ -452,7 +460,7 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
     print('c4gli_morning_ldatetime 0',c4gli_morning.pars.ldatetime)
 file_morning.close()
 
-file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_afternoon.yaml','w') 
+file_afternoon = open(path_soundings+format(current_station.name,'05d')+'_end.yaml','w') 
 for date,pair  in HOUR_FILES.items(): 
     humpafn ='/kyukon/data/gent/gvo000/gvo00090/EXT/data/SOUNDINGS/HUMPPA/'+pair['afternoon'][1]
     balloon_file = open(humpafn,'r',encoding='latin-1')
@@ -488,79 +496,79 @@ def humppa_parser(balloon_file,file_sounding,ldate,hour,c4gli=None):
 
 records_morning = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='morning',
+                                           subset='ini',
                                            refetch_records=True,
                                            )
 print('records_morning_ldatetime',records_morning.ldatetime)
 
 records_afternoon = get_records(pd.DataFrame([current_station]),\
                                            path_soundings,\
-                                           subset='afternoon',
+                                           subset='end',
                                            refetch_records=True,
                                            )
 
-# align afternoon records with noon records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
-records_afternoon.index = records_morning.index
-path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/C4GL/IOPS/'
-
-os.system('mkdir -p '+path_exp)
-file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_morning.yaml')
-file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_afternoon.yaml')
-file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
-file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_mod.yaml','w')
-
-for (STNID,chunk,index),record_morning in records_morning.iterrows():
-    record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-
-    c4gli_morning = get_record_yaml(file_morning, 
-                                    record_morning.index_start, 
-                                    record_morning.index_end,
-                                    mode='ini')
-    #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
-    
-    
-    c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                      record_afternoon.index_start, 
-                                      record_afternoon.index_end,
-                                    mode='ini')
-
-    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon.pars.datetime_daylight - 
-                             c4gli_morning.pars.datetime_daylight).total_seconds())})
-    c4gli_morning.update(source='manual',
-                         pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
-    c4gli_morning.dump(file_ini)
-    
-    c4gl = class4gl(c4gli_morning)
-    c4gl.run()
-    
-    c4gl.dump(file_mod,\
-              include_input=False,\
-              timeseries_only=timeseries_only)
-file_ini.close()
-file_mod.close()
-file_morning.close()
-file_afternoon.close()
-
-records_ini = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='ini',
-                                           refetch_records=True,
-                                           )
-records_mod = get_records(pd.DataFrame([current_station]),\
-                                           path_exp,\
-                                           subset='mod',
-                                           refetch_records=True,
-                                           )
-
-records_mod.index = records_ini.index
-
-# align afternoon records with initial records, and set same index
-records_afternoon.index = records_afternoon.ldatetime.dt.date
-records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-records_afternoon.index = records_ini.index
+# # align afternoon records with noon records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_morning.ldatetime.dt.date]
+# records_afternoon.index = records_morning.index
+# path_exp = '/kyukon/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/IOPS_ALL/'
+# 
+# os.system('mkdir -p '+path_exp)
+# file_morning = open(path_soundings+'/'+format(current_station.name,'05d')+'_ini.yaml')
+# file_afternoon = open(path_soundings+'/'+format(current_station.name,'05d')+'_ini.yaml')
+# file_ini = open(path_exp+'/'+format(current_station.name,'05d')+'_ini.yaml','w')
+# file_mod = open(path_exp+'/'+format(current_station.name,'05d')+'_end.yaml','w')
+# 
+# for (STNID,chunk,index),record_morning in records_morning.iterrows():
+#     record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+# 
+#     c4gli_morning = get_record_yaml(file_morning, 
+#                                     record_morning.index_start, 
+#                                     record_morning.index_end,
+#                                     mode='ini')
+#     #print('c4gli_morning_ldatetime',c4gli_morning.pars.ldatetime)
+#     
+#     
+#     c4gli_afternoon = get_record_yaml(file_afternoon, 
+#                                       record_afternoon.index_start, 
+#                                       record_afternoon.index_end,
+#                                     mode='ini')
+# 
+#     c4gli_morning.update(source='pairs',pars={'runtime' : \
+#                         int((c4gli_afternoon.pars.datetime_daylight - 
+#                              c4gli_morning.pars.datetime_daylight).total_seconds())})
+#     c4gli_morning.update(source='manual',
+#                          pars={'sw_ac' : [],'sw_ap': True,'sw_lit': False})
+#     c4gli_morning.dump(file_ini)
+#     
+#     c4gl = class4gl(c4gli_morning)
+#     c4gl.run()
+#     
+#     c4gl.dump(file_mod,\
+#               include_input=False,\
+#               timeseries_only=timeseries_only)
+# file_ini.close()
+# file_mod.close()
+# file_morning.close()
+# file_afternoon.close()
+# 
+# records_ini = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='ini',
+#                                            refetch_records=True,
+#                                            )
+# records_mod = get_records(pd.DataFrame([current_station]),\
+#                                            path_exp,\
+#                                            subset='end',
+#                                            refetch_records=True,
+#                                            )
+# 
+# records_mod.index = records_ini.index
+# 
+# # align afternoon records with initial records, and set same index
+# records_afternoon.index = records_afternoon.ldatetime.dt.date
+# records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+# records_afternoon.index = records_ini.index
 
 # stations_for_iter = stations(path_exp)
 # for STNID,station in stations_iterator(stations_for_iter):
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index 3c71599..f1516c2 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -153,8 +153,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     one_run = False
 # for iSTN,STN in STNlist[5:]:  
     
-    fnout = args.path_output+"/"+format(STN.name,'05d')+"_morning.yaml"
-    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_afternoon.yaml"
+    fnout = args.path_output+"/"+format(STN.name,'05d')+"_ini.yaml"
+    fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_end.yaml"
     
 
     # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
@@ -199,7 +199,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 # we take 3000 because previous analysis (ie., HUMPPA) has
                 # focussed towards such altitude
                 le3000 = (c4gli.air_balloon.z <= 3000.)
-                logic['10measurements'] = (np.sum(le3000) >= 10) 
+                logic['10measurements'] = (np.sum(le3000) >= 7) 
 
                 leh = (c4gli.air_balloon.z <= c4gli.pars.h)
 
@@ -207,9 +207,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                         (len(np.where(leh)[0]) > 0) and \
                         # in cases where humidity is not defined, the mixed-layer
                         # values get corr
-                        (not np.isnan(c4gli.pars.theta)) and \
-                        (rmse(c4gli.air_balloon.theta[leh] , \
-                              c4gli.pars.theta,filternan_actual=True) < 1.)\
+                        (not np.isnan(c4gli.pars.theta))\
+                                     #and \
+                        #(rmse(c4gli.air_balloon.theta[leh] , \
+                        #      c4gli.pars.theta,filternan_actual=True) < 1.0)\
                               )
     
 
@@ -237,118 +238,120 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                        c4gli.pars.ldatetime.day)
                 c4gli_afternoon.clear()
                 print('AFTERNOON PROFILE CLEARED')
-                try:
+                # try:
+                c4gli_afternoon.get_profile_wyoming(wy_strm)
+                print('AFTERNOON PROFILE OK')
+
+                if wy_strm.current is not None:
+                    current_date_afternoon = \
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
+                else:
+                    # a dummy date: this will be ignored anyway
+                    current_date_afternoon = dt.date(1900,1,1)
+
+                # we will dump the latest afternoon sounding that fits the
+                # minimum criteria specified by logic_afternoon
+                print(current_date,current_date_afternoon)
+                c4gli_afternoon_for_dump = None
+                while ((current_date_afternoon == current_date) and \
+                       (wy_strm.current is not None)):
+                    logic_afternoon =dict()
+
+                    logic_afternoon['afternoon'] = \
+                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                    # the sounding should have taken place before 1 hours
+                    # before sunset. This is to minimize the chance that a
+                    # stable boundary layer (yielding very low mixed layer
+                    # heights) is formed which can not be represented by
+                    # class.
+                    logic_afternoon['daylight'] = \
+                      ((c4gli_afternoon.pars.ldatetime - \
+                        c4gli_afternoon.pars.lSunset \
+                       ).total_seconds()/3600. <= -1.)
+
+
+                    le3000_afternoon = \
+                        (c4gli_afternoon.air_balloon.z <= 3000.)
+                    logic_afternoon['5measurements'] = \
+                        (np.sum(le3000_afternoon) >= 7) 
+
+                    # we only store the last afternoon sounding that fits these
+                    # minimum criteria
+
+                    afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                    print('logic_afternoon: ',logic_afternoon)
+                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                    if afternoon_ok == 1.:
+                        # # doesn't work :(
+                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                        
+                        # so we just create a new one from the same wyoming profile
+                        c4gli_afternoon_for_dump = class4gl_input()
+                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                    wy_strm.find_next()
+                    c4gli_afternoon.clear()
                     c4gli_afternoon.get_profile_wyoming(wy_strm)
-                    print('AFTERNOON PROFILE OK')
 
                     if wy_strm.current is not None:
                         current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
+                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                       c4gli_afternoon.pars.ldatetime.month, \
+                                       c4gli_afternoon.pars.ldatetime.day)
                     else:
                         # a dummy date: this will be ignored anyway
                         current_date_afternoon = dt.date(1900,1,1)
 
-                    # we will dump the latest afternoon sounding that fits the
-                    # minimum criteria specified by logic_afternoon
-                    print(current_date,current_date_afternoon)
-                    c4gli_afternoon_for_dump = None
-                    while ((current_date_afternoon == current_date) and \
-                           (wy_strm.current is not None)):
-                        logic_afternoon =dict()
-
-                        logic_afternoon['afternoon'] = \
-                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                        # the sounding should have taken place before 1 hours
-                        # before sunset. This is to minimize the chance that a
-                        # stable boundary layer (yielding very low mixed layer
-                        # heights) is formed which can not be represented by
-                        # class.
-                        logic_afternoon['daylight'] = \
-                          ((c4gli_afternoon.pars.ldatetime - \
-                            c4gli_afternoon.pars.lSunset \
-                           ).total_seconds()/3600. <= 1.)
-
-
-                        le3000_afternoon = \
-                            (c4gli_afternoon.air_balloon.z <= 3000.)
-                        logic_afternoon['5measurements'] = \
-                            (np.sum(le3000_afternoon) >= 5) 
-
-                        # we only store the last afternoon sounding that fits these
-                        # minimum criteria
-
-                        afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                        print('logic_afternoon: ',logic_afternoon)
-                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                        if afternoon_ok == 1.:
-                            # # doesn't work :(
-                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                            
-                            # so we just create a new one from the same wyoming profile
-                            c4gli_afternoon_for_dump = class4gl_input()
-                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                        wy_strm.find_next()
-                        c4gli_afternoon.clear()
-                        c4gli_afternoon.get_profile_wyoming(wy_strm)
-
-                        if wy_strm.current is not None:
-                            current_date_afternoon = \
-                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                           c4gli_afternoon.pars.ldatetime.month, \
-                                           c4gli_afternoon.pars.ldatetime.day)
-                        else:
-                            # a dummy date: this will be ignored anyway
-                            current_date_afternoon = dt.date(1900,1,1)
-
-                        # Only in the case we have a good pair of soundings, we
-                        # dump them to disk
-                    if c4gli_afternoon_for_dump is not None:
-                        c4gli.update(source='pairs',pars={'runtime' : \
-                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                                 c4gli.pars.datetime_daylight).total_seconds())})
+                    # Only in the case we have a good pair of soundings, we
+                    # dump them to disk
+                if c4gli_afternoon_for_dump is not None:
+                    c4gli.update(source='pairs',pars={'runtime' : \
+                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                             c4gli.pars.datetime_daylight).total_seconds())})
     
     
-                        print('ALMOST...')
-                        if c4gli.pars.runtime > 18000.: # more than 5 hours simulation
-                                
+                    print('ALMOST...')
+                    if c4gli.pars.runtime > 3600*4.: # more than 4 hours simulation
+                            
         
-                            c4gli.get_global_input(globaldata)
-                            print('VERY CLOSE...')
-                            if c4gli.check_source_globaldata() and \
-                                (c4gli.check_source(source='wyoming',\
-                                                   check_only_sections='pars')):
-                                c4gli.dump(fileout)
-                                
-                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                                
-                                
-                                # for keyEXP,dictEXP in experiments.items():
-                                #     
-                                #     c4gli.update(source=keyEXP,pars = dictEXP)
-                                #     c4gl = class4gl(c4gli)
-                                #     # c4gl.run()
-                                #     
-                                #     c4gl.dump(c4glfiles[key])
-                                
-                                print('HIT!!!')
-                                one_run = True
-                except:
-                    print('get profile failed')
+                        c4gli.get_global_input(globaldata)
+                        print('VERY CLOSE...')
+                        if c4gli.check_source_globaldata() and \
+                            (c4gli.check_source(source='wyoming',\
+                                               check_only_sections='pars')):
+                            print('starting dumps')
+                            c4gli.dump(fileout)
+                            print('file morning dumped')
+                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                            print('file afternoon dumped')
+                            
+                            
+                            # for keyEXP,dictEXP in experiments.items():
+                            #     
+                            #     c4gli.update(source=keyEXP,pars = dictEXP)
+                            #     c4gl = class4gl(c4gli)
+                            #     # c4gl.run()
+                            #     
+                            #     c4gl.dump(c4glfiles[key])
+                            
+                            print('HIT!!!')
+                            one_run = True
+                # except:
+                #     print('get profile failed')
                 
     if one_run:
         #STN.name = STN.name
         all_records_morning = get_records(pd.DataFrame([STN]),\
                                       args.path_output,\
-                                      subset='morning',
+                                      subset='ini',
                                       refetch_records=True,
                                       )
         all_records_afternoon = get_records(pd.DataFrame([STN]),\
                                       args.path_output,\
-                                      subset='afternoon',
+                                      subset='end',
                                       refetch_records=True,
                                       )
     else:
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index 61c7da2..0762cf8 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -175,8 +175,7 @@
             # https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path
             import importlib.util
             print(batch_args.exec)
-            spec = importlib.util.spec_from_file_location("module.name",
-                                                          batch_args.exec)
+            spec = importlib.util.spec_from_file_location("module.name", batch_args.exec)
             task_module = importlib.util.module_from_spec(spec)
             spec.loader.exec_module(task_module)
             print('hello')
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 8d9a652..3639c2d 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -74,378 +74,384 @@ def __init__(self,**kwargs):
     print(args.__dict__)
         
 
-# def execute(**kwargs):
-# note that with args, we actually mean the same as those specified with
-# the argparse module above
-
-# overwrite the args according to the kwargs when the procedure is called
-# as module function
-# for key,value in kwargs.items():
-#     args.__dict__[key]  = value
-
-print("-- begin arguments --")
-for key,value in args.__dict__.items():
-     print(key,': ',value)
-print("-- end arguments ----")
-
-# load specified class4gl library
-if args.c4gl_path_lib is not None:
-    sys.path.insert(0, args.c4gl_path_lib)
-
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
-
-EXP_DEFS  =\
-{
-  'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-
-  'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-    
-  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
-  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
-
-# ========================
-print("getting a list of stations")
-# ========================
-
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
-
-# ====================================
-print('defining all_stations_select')
-# ====================================
-
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
-
-
-
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
-else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print(all_stations_select)
-print("getting all records of the whole batch")
-all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_forcing,\
-                                         subset=args.subset_forcing,
-                                         refetch_records=False,
-                                         )
-
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
-
-    if (args.split_by is None) or (args.split_by <= 0):
-            raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
-
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-            chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-            print('chunks_current_station',chunks_current_station)
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk = int(args.global_chunk_number) - totalchunks 
-        
-            totalchunks +=chunks_current_station
+def execute(**kwargs):
+    # note that with args, we actually mean the same as those specified with
+    # the argparse module above
+    
+    # overwrite the args according to the kwargs when the procedure is called
+    # as module function
+    for key,value in kwargs.items():
+        args.__dict__[key]  = value
+    
+    print("-- begin arguments --")
+    for key,value in args.__dict__.items():
+         print(key,': ',value)
+    print("-- end arguments ----")
+    
+    # load specified class4gl library
+    if args.c4gl_path_lib is not None:
+        sys.path.insert(0, args.c4gl_path_lib)
+    
+    from class4gl import class4gl_input, data_global,class4gl
+    from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+    from class4gl import blh,class4gl_input
+    
+    # this is a variant of global run in which the output of runs are still written
+    # out even when the run crashes.
+    
+    # #only include the following timeseries in the model output
+    # timeseries_only = \
+    # ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+    #  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+    #  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+    #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+    #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+    
+    
+    EXP_DEFS  =\
+    {
+      'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+    
+      'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
         
-
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
+      'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'IOPS_ADV_SM2':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+        'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shearwe':True},
+      'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+      'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
+      'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
+    }
+    
+    # ========================
+    print("getting a list of stations")
+    # ========================
+    
+    # these are all the stations that are found in the input dataset
+    all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+    
+    # ====================================
+    print('defining all_stations_select')
+    # ====================================
+    
+    # these are all the stations that are supposed to run by the whole batch (all
+    # chunks). We narrow it down according to the station(s) specified.
+    
+    
+    
+    if args.station_id is not None:
+        print("Selecting station by ID")
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        all_stations_select = pd.DataFrame([run_station])
     else:
-        if args.split_by is not None:
-            raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
-        
-
-#print(all_stations)
-print('Fetching initial/forcing records')
-records_morning = get_records(run_stations,\
-                              args.path_forcing,\
-                              subset=args.subset_forcing,
-                              refetch_records=False,
-                              )
-
-# note that if runtime is an integer number, we don't need to get the afternoon
-# profiles. 
-if args.runtime == 'from_profile_pair':
-    print('Fetching afternoon records for determining the simulation runtimes')
-    records_afternoon = get_records(run_stations,\
-                                    args.path_forcing,\
-                                    subset='end',
-                                    refetch_records=False,
-                                    )
-    
-    # print(records_morning.index)
-    # print(records_afternoon.index)
-    # align afternoon records with the noon records, and set same index
-    print('hello')
-    print(len(records_afternoon))
-    print(len(records_morning))
-
-    print("aligning morning and afternoon records")
-    records_morning['dates'] = records_morning['ldatetime'].dt.date
-    records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
-    records_afternoon.set_index(['STNID','dates'],inplace=True)
-    ini_index_dates = records_morning.set_index(['STNID','dates']).index
-    records_afternoon = records_afternoon.loc[ini_index_dates]
-    records_afternoon.index = records_morning.index
-
-experiments = args.experiments.strip(' ').split(' ')
-if args.experiments_names is not None:
-    experiments_names = args.experiments_names.strip(' ').split(' ')
-    if len(experiments_names) != len(experiments):
-        raise ValueError('Lenght of --experiments_names is different from --experiments')
-
-else:
-    experiments_names = experiments
-
-for iexpname,expid in enumerate(experiments):
-    expname = experiments_names[iexpname]
-    exp = EXP_DEFS[expid]
-    path_exp = args.path_experiments+'/'+expname+'/'
-
-    os.system('mkdir -p '+path_exp)
-    for istation,current_station in run_stations.iterrows():
-        print(istation,current_station)
-        records_morning_station = records_morning.query('STNID == '+str(current_station.name))
-        start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
-        end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
-        if start_record >= (len(records_morning_station)):
-            print("warning: outside of profile number range for station "+\
-                  str(current_station)+". Skipping chunk number for this station.")
+        print("Selecting stations from a row range in the table")
+        all_stations_select = pd.DataFrame(all_stations.table)
+        if args.last_station_row is not None:
+            all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            all_stations_select = all_station_select.iloc[int(args.first_station):]
+    print("station numbers included in the whole batch "+\
+          "(all chunks):",list(all_stations_select.index))
+    
+    print(all_stations_select)
+    print("getting all records of the whole batch")
+    all_records_morning_select = get_records(all_stations_select,\
+                                             args.path_forcing,\
+                                             subset=args.subset_forcing,
+                                             refetch_records=False,
+                                             )
+    
+    # only run a specific chunck from the selection
+    if args.global_chunk_number is not None:
+        if args.station_chunk_number is not None:
+            raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+    
+        if (args.split_by is None) or (args.split_by <= 0):
+                raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    
+        run_station_chunk = None
+        print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+        totalchunks = 0
+        stations_iter = all_stations_select.iterrows()
+        in_current_chunk = False
+        try:
+            while not in_current_chunk:
+                istation,current_station = stations_iter.__next__()
+                all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+                chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+                print('chunks_current_station',chunks_current_station)
+                in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+            
+                if in_current_chunk:
+                    run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                    run_station_chunk = int(args.global_chunk_number) - totalchunks 
+            
+                totalchunks +=chunks_current_station
+            
+    
+        except StopIteration:
+           raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+        print("station = ",list(run_stations.index))
+        print("station chunk number:",run_station_chunk)
+    
+    # if no global chunk is specified, then run the whole station selection in one run, or
+    # a specific chunk for each selected station according to # args.station_chunk_number
+    else:
+        run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+        if args.station_chunk_number is not None:
+            run_station_chunk = int(args.station_chunk_number)
+            print("station(s) that is processed.",list(run_stations.index))
+            print("chunk number: ",run_station_chunk)
         else:
-            fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
-            if os.path.isfile(fn_morning):
-                file_morning = open(fn_morning)
-            else:
-                fn_morning = \
-                     args.path_forcing+'/'+format(current_station.name,'05d')+\
-                     '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-                file_morning = open(fn_morning)
-
-            if args.runtime == 'from_profile_pair':
-                file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
-            fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_ini.yaml'
-            fn_end_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
-                     str(int(run_station_chunk))+'_end.yaml'
-            file_ini = open(fn_ini,'w')
-            file_end_mod = open(fn_end_mod,'w')
-
-            #iexp = 0
-            onerun = False
-            print('starting station chunk number: '\
-                  +str(run_station_chunk)+' (chunk size:',args.split_by,')')
-
-            records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-
-            isim = 0
-            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
-                    print('starting '+str(isim+1)+' out of '+\
-                      str(len(records_morning_station_chunk) )+\
-                      ' (station total: ',str(len(records_morning_station)),')')  
-                
+            if args.split_by is not None:
+                raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
+            run_station_chunk = 0
+            print("stations that are processed.",list(run_stations.index))
             
-                    c4gli_morning = get_record_yaml(file_morning, 
-                                                    record_morning.index_start, 
-                                                    record_morning.index_end,
-                                                    mode='model_input')
-                    if args.diag_tropo is not None:
-                        print('add tropospheric parameters on advection and subsidence (for diagnosis)')
-                        seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                        profile_tropo = c4gli_morning.air_ac[seltropo]
-                        for var in args.diag_tropo:#['t','q','u','v',]:
-                            if var[:3] == 'adv':
-                                mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                                c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                            else:
-                                print("warning: tropospheric variable "+var+" not recognized")
-                    
+    
+    #print(all_stations)
+    print('Fetching initial/forcing records')
+    records_morning = get_records(run_stations,\
+                                  args.path_forcing,\
+                                  subset=args.subset_forcing,
+                                  refetch_records=False,
+                                  )
+    if len(records_morning) == 0:
+        raise IOError("No initialization records records found in "+\
+                      args.path_forcing+' (subset: '+args_subset_forcing+')')
+    
+    # note that if runtime is an integer number, we don't need to get the afternoon
+    # profiles. 
+    if args.runtime == 'from_profile_pair':
+        print('Fetching afternoon records for determining the simulation runtimes')
+        records_afternoon = get_records(run_stations,\
+                                        args.path_forcing,\
+                                        subset='end',
+                                        refetch_records=False,
+                                        )
+        if len(records_afternoon) == 0:
+            raise IOError("No final state records found in "+\
+                          args.path_forcing+' (subset: '+args_subset_forcing+')')
+        
+        # print(records_morning.index)
+        # print(records_afternoon.index)
+        # align afternoon records with the noon records, and set same index
+        print('hello')
+        print(len(records_afternoon))
+        print(len(records_morning))
+    
+        print("aligning morning and afternoon records")
+        records_morning['dates'] = records_morning['ldatetime'].dt.date
+        records_afternoon['dates'] = records_afternoon['ldatetime'].dt.date
+        records_afternoon.set_index(['STNID','dates'],inplace=True)
+        ini_index_dates = records_morning.set_index(['STNID','dates']).index
+        records_afternoon = records_afternoon.loc[ini_index_dates]
+        records_afternoon.index = records_morning.index
+    
+    experiments = args.experiments.strip(' ').split(' ')
+    if args.experiments_names is not None:
+        experiments_names = args.experiments_names.strip(' ').split(' ')
+        if len(experiments_names) != len(experiments):
+            raise ValueError('Lenght of --experiments_names is different from --experiments')
+    
+    else:
+        experiments_names = experiments
+    
+    for iexpname,expid in enumerate(experiments):
+        expname = experiments_names[iexpname]
+        exp = EXP_DEFS[expid]
+        path_exp = args.path_experiments+'/'+expname+'/'
+    
+        os.system('mkdir -p '+path_exp)
+        for istation,current_station in run_stations.iterrows():
+            print(istation,current_station)
+            records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+            start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
+            end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
+            if start_record >= (len(records_morning_station)):
+                print("warning: outside of profile number range for station "+\
+                      str(current_station)+". Skipping chunk number for this station.")
+            else:
+                fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+                if os.path.isfile(fn_morning):
+                    file_morning = open(fn_morning)
+                else:
+                    fn_morning = \
+                         args.path_forcing+'/'+format(current_station.name,'05d')+\
+                         '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+                    file_morning = open(fn_morning)
+    
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
+                fn_ini = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_ini.yaml'
+                fn_end_mod = path_exp+'/'+format(current_station.name,'05d')+'_'+\
+                         str(int(run_station_chunk))+'_end.yaml'
+                file_ini = open(fn_ini,'w')
+                file_end_mod = open(fn_end_mod,'w')
+    
+                #iexp = 0
+                onerun = False
+                print('starting station chunk number: '\
+                      +str(run_station_chunk)+' (chunk size:',args.split_by,')')
+    
+                records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+    
+                isim = 0
+                for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                        print('starting '+str(isim+1)+' out of '+\
+                          str(len(records_morning_station_chunk) )+\
+                          ' (station total: ',str(len(records_morning_station)),')')  
                     
-                    if args.runtime == 'from_profile_pair':
-                        record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
-                        c4gli_afternoon = get_record_yaml(file_afternoon, 
-                                                          int(record_afternoon.index_start),
-                                                          int(record_afternoon.index_end),
+                
+                        c4gli_morning = get_record_yaml(file_morning, 
+                                                        record_morning.index_start, 
+                                                        record_morning.index_end,
                                                         mode='model_input')
-                        runtime = int((c4gli_afternoon.pars.datetime_daylight - 
-                                             c4gli_morning.pars.datetime_daylight).total_seconds())
-                    elif args.runtime == 'from_input':
-                        runtime = c4gli_morning.pars.runtime
-                    else:
-                        runtime = int(args.runtime)
-
-            
-                    c4gli_morning.update(source='pairs',pars={'runtime' : \
-                                        runtime})
-                    c4gli_morning.update(source=expname, pars=exp)
-
-                    c4gl = class4gl(c4gli_morning)
-
-                    if args.error_handling == 'dump_always':
-                        try:
-                            print('checking data sources')
-                            if not c4gli_morning.check_source_globaldata():
-                                print('Warning: some input sources appear invalid')
-                            c4gl.run()
-                            print('run succesful')
-                        except:
-                            print('run not succesful')
-                        onerun = True
-
-                        print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
-                        c4gli_morning.dump(file_ini)
+                        if args.diag_tropo is not None:
+                            print('add tropospheric parameters on advection and subsidence (for diagnosis)')
+                            seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
+                            profile_tropo = c4gli_morning.air_ac[seltropo]
+                            for var in args.diag_tropo:#['t','q','u','v',]:
+                                if var[:3] == 'adv':
+                                    mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
+                                    c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
+                                else:
+                                    print("warning: tropospheric variable "+var+" not recognized")
                         
                         
-                        c4gl.dump(file_end_mod,\
-                                  include_input=False,\
-                                  #timeseries_only=timeseries_only,\
-                                 )
-                        onerun = True
-                    # in this case, only the file will dumped if the runs were
-                    # successful
-                    elif args.error_handling == 'dump_on_success':
-                       try:
-                            print('checking data sources')
-                            if not c4gli_morning.check_source_globaldata():
-                                print('Warning: some input sources appear invalid')
-                            c4gl.run()
-                            print('run succesful')
+                        if args.runtime == 'from_profile_pair':
+                            record_afternoon = records_afternoon.loc[(STNID,chunk,index)]
+                            c4gli_afternoon = get_record_yaml(file_afternoon, 
+                                                              int(record_afternoon.index_start),
+                                                              int(record_afternoon.index_end),
+                                                            mode='model_input')
+                            runtime = int((c4gli_afternoon.pars.datetime_daylight - 
+                                                 c4gli_morning.pars.datetime_daylight).total_seconds())
+                        elif args.runtime == 'from_input':
+                            runtime = c4gli_morning.pars.runtime
+                        else:
+                            runtime = int(args.runtime)
+    
+                
+                        c4gli_morning.update(source='pairs',pars={'runtime' : \
+                                            runtime})
+                        c4gli_morning.update(source=expname, pars=exp)
+    
+                        c4gl = class4gl(c4gli_morning)
+    
+                        if args.error_handling == 'dump_always':
+                            try:
+                                print('checking data sources')
+                                if not c4gli_morning.check_source_globaldata():
+                                    print('Warning: some input sources appear invalid')
+                                c4gl.run()
+                                print('run succesful')
+                            except:
+                                print('run not succesful')
+                            onerun = True
+    
+                            print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
                             c4gli_morning.dump(file_ini)
                             
                             
-                            print("dumping to "+str(file_ini)) 
                             c4gl.dump(file_end_mod,\
                                       include_input=False,\
                                       #timeseries_only=timeseries_only,\
                                      )
                             onerun = True
-                       except:
-                           print('run not succesful')
-                    isim += 1
-
-
-            file_ini.close()
-            file_end_mod.close()
-            file_morning.close()
-            if args.runtime == 'from_profile_pair':
-                file_afternoon.close()
-    
-            if onerun:
-                records_ini = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='ini',
-                                                           refetch_records=True,
-                                                           )
-                records_end_mod = get_records(pd.DataFrame([current_station]),\
-                                                           path_exp,\
-                                                           getchunk = int(run_station_chunk),\
-                                                           subset='end',\
-                                                           refetch_records=True,\
-                                                           )
-            else:
-                # remove empty files
-                os.system('rm '+fn_ini)
-                os.system('rm '+fn_end_mod)
-    
-    # # align afternoon records with initial records, and set same index
-    # records_afternoon.index = records_afternoon.ldatetime.dt.date
-    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
-    # records_afternoon.index = records_ini.index
-    
-    # stations_for_iter = stations(path_exp)
-    # for STNID,station in stations_iterator(stations_for_iter):
-    #     records_current_station_index = \
-    #             (records_ini.index.get_level_values('STNID') == STNID)
-    #     file_current_station_end_mod = STNID
-    # 
-    #     with \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
-    #     open(path_exp+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
-    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-    #         for (STNID,index),record_ini in records_iterator(records_ini):
-    #             c4gli_ini = get_record_yaml(file_station_ini, 
-    #                                         record_ini.index_start, 
-    #                                         record_ini.index_end,
-    #                                         mode='ini')
-    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
-    # 
-    #             record_end_mod = records_end_mod.loc[(STNID,index)]
-    #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
-    #                                         record_end_mod.index_start, 
-    #                                         record_end_mod.index_end,
-    #                                         mode='mod')
-    #             record_afternoon = records_afternoon.loc[(STNID,index)]
-    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-    #                                         record_afternoon.index_start, 
-    #                                         record_afternoon.index_end,
-    #                                         mode='ini')
+                        # in this case, only the file will dumped if the runs were
+                        # successful
+                        elif args.error_handling == 'dump_on_success':
+                           try:
+                                print('checking data sources')
+                                if not c4gli_morning.check_source_globaldata():
+                                    print('Warning: some input sources appear invalid')
+                                c4gl.run()
+                                print('run succesful')
+                                c4gli_morning.dump(file_ini)
+                                
+                                
+                                print("dumping to "+str(file_ini)) 
+                                c4gl.dump(file_end_mod,\
+                                          include_input=False,\
+                                          #timeseries_only=timeseries_only,\
+                                         )
+                                onerun = True
+                           except:
+                               print('run not succesful')
+                        isim += 1
+    
+    
+                file_ini.close()
+                file_end_mod.close()
+                file_morning.close()
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon.close()
+        
+                if onerun:
+                    records_ini = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='ini',
+                                                               refetch_records=True,
+                                                               )
+                    records_end_mod = get_records(pd.DataFrame([current_station]),\
+                                                               path_exp,\
+                                                               getchunk = int(run_station_chunk),\
+                                                               subset='end',\
+                                                               refetch_records=True,\
+                                                               )
+                else:
+                    # remove empty files
+                    os.system('rm '+fn_ini)
+                    os.system('rm '+fn_end_mod)
+        
+        # # align afternoon records with initial records, and set same index
+        # records_afternoon.index = records_afternoon.ldatetime.dt.date
+        # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+        # records_afternoon.index = records_ini.index
+        
+        # stations_for_iter = stations(path_exp)
+        # for STNID,station in stations_iterator(stations_for_iter):
+        #     records_current_station_index = \
+        #             (records_ini.index.get_level_values('STNID') == STNID)
+        #     file_current_station_end_mod = STNID
+        # 
+        #     with \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+        #     open(path_exp+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
+        #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+        #         for (STNID,index),record_ini in records_iterator(records_ini):
+        #             c4gli_ini = get_record_yaml(file_station_ini, 
+        #                                         record_ini.index_start, 
+        #                                         record_ini.index_end,
+        #                                         mode='ini')
+        #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+        # 
+        #             record_end_mod = records_end_mod.loc[(STNID,index)]
+        #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
+        #                                         record_end_mod.index_start, 
+        #                                         record_end_mod.index_end,
+        #                                         mode='mod')
+        #             record_afternoon = records_afternoon.loc[(STNID,index)]
+        #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+        #                                         record_afternoon.index_start, 
+        #                                         record_afternoon.index_end,
+        #                                         mode='ini')
 
 
-# if __name__ == '__main__':
-#     #execute(**vars(args))
-#     execute()
+if __name__ == '__main__':
+    #execute(**vars(args))
+    execute()
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 8b60b86..bf0da9d 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -110,9 +110,11 @@ def execute(**kwargs):
     
     EXP_DEFS  =\
     {
-      'BASE_ITER':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'BASE_ITER':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
+      'BASE_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
+      'BASE_ITER_W_ADV':{'sw_ac' : ['adv',"w"],'sw_ap': True,'sw_lit': False},
+      'BASE_ITER_W':{'sw_ac' : ["w"],'sw_ap': True,'sw_lit': False},
     
-      'NOADV_ITER':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
         
       'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
       'NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
@@ -222,6 +224,11 @@ def execute(**kwargs):
                                   subset=args.subset_forcing,
                                   refetch_records=False,
                                   )
+    if len(records_morning) == 0:
+        raise IOError("No initialization records records found in "+\
+                      args.path_forcing+' (subset: '+args_subset_forcing+')')
+
+
     
     # note that if runtime is an integer number, we don't need to get the afternoon
     # profiles. 
@@ -232,6 +239,9 @@ def execute(**kwargs):
                                         subset='end',
                                         refetch_records=False,
                                         )
+        if len(records_afternoon) == 0:
+            raise IOError("No final state records found in "+\
+                          args.path_forcing+' (subset: '+args_subset_forcing+')')
         
         # print(records_morning.index)
         # print(records_afternoon.index)
@@ -348,7 +358,6 @@ def execute(**kwargs):
                         b = c4gli_morning.pars.wwilt
                         c = c4gli_morning.pars.wfc #max(c4gli_morning.pars.wfc,c4gli_morning.pars.wsat-0.01)
                         
-                        
                         try:
                             #fb = f(b)
                             c4gli_morning.pars.wg = b
@@ -374,7 +383,6 @@ def execute(**kwargs):
                             c4gli_morningc = c4gli_morning
                             i=0
                             
-    
                             if fc*fb > 0.:
                                 if abs(fb) < abs(fc):
                                     c4gl = c4glb
@@ -490,7 +498,8 @@ def execute(**kwargs):
                 file_ini.close()
                 file_mod.close()
                 file_morning.close()
-                file_afternoon.close()
+                if args.runtime == 'from_profile_pair':
+                    file_afternoon.close()
         
                 if onerun:
                     records_ini = get_records(pd.DataFrame([current_station]),\
diff --git a/setup.py b/setup.py
index f3832ba..3ea0222 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.9.3',
+        version='0.9.4',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From f57ed7e0237e02aff03be5c25f9459260e7c5efa Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 4 Mar 2019 16:36:51 +0100
Subject: [PATCH 122/129] multiple updates

---
 class4gl/class4gl.py                |  60 +++---
 class4gl/interface_functions.py     |  13 +-
 class4gl/interface_multi.py         |  27 +--
 class4gl/model.py                   |  10 +-
 class4gl/setup/batch_setup_igra.py  |   2 +-
 class4gl/setup/setup_igra.py        | 272 ++++++++++++++--------------
 class4gl/simulations/simulations.py |  16 +-
 7 files changed, 204 insertions(+), 196 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index 0173222..bebf496 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -601,30 +601,32 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         # # this is an alternative pipe/numpy method
         # (~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)).pipe(np.where)[0]
         valid_indices = air_balloon.index[is_valid]
+        air_balloon = air_balloon[is_valid].reset_index()
         #print(valid_indices)
 
-        dpars['Ps'] = air_balloon.p.loc[[valid_indices[0]]][0]
 
-        air_balloon['t'] = air_balloon['TEMP']+273.15
-        air_balloon['theta'] = (air_balloon.t) * \
-                   (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
-        air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+        if len(air_balloon) > 2:
 
+            dpars['Ps'] = air_balloon.p.values[0]
+            air_balloon['t'] = air_balloon['TEMP']+273.15
+            air_balloon['theta'] = (air_balloon.t) * \
+                       (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+            air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
+
+            # t_cut_off = 1.5
+            # i = 1
+            # if t_cut_off is not None:
+            #     
+            #     while ((i < len(air_balloon)) and \
+            #           ((air_balloon.thetav[0] - air_balloon.thetav[i] ) > t_cut_off)):
+            #         #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
+            #         air_balloon.thetav[0:i] = \
+            #                 air_balloon.thetav[i] + t_cut_off
+            #         
+            #         i +=1
 
 
-        i = 1
-        t_cut_off = 2.0
-        if t_cut_off is not None:
-            
-            while (air_balloon.thetav.loc[[valid_indices[0]]][0] - \
-                   air_balloon.thetav.loc[valid_indices[i:i+1]][0] ) > t_cut_off:
-                #diff = (air_balloon.theta.iloc[valid_indices[i]] -air_balloon.theta.iloc[valid_indices[i+1]])- 0.5
-                air_balloon.thetav.loc[valid_indices[0:i]] = \
-                        air_balloon.thetav.loc[valid_indices[i:i+1]][0] + t_cutoff
-                
-                i +=1
 
-        if len(valid_indices) > 0:
             #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
             dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
             
@@ -641,28 +643,22 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
             dpars['h_l'] =np.nan
             dpars['h_e'] =np.nan
             dpars['h'] =np.nan
-
-
-        if np.isnan(dpars['h']):
             dpars['Ps'] = np.nan
+            air_balloon['t'] = np.nan
+            air_balloon['theta'] = np.nan
+            air_balloon['thetav'] = np.nan
 
         if ~np.isnan(dpars['h']):
             # determine mixed-layer properties (moisture, potential temperature...) from profile
             
             # ... and those of the mixed layer
-            is_valid_below_h = (air_balloon.loc[valid_indices].z < dpars['h'])
-            valid_indices_below_h =  air_balloon.loc[valid_indices].index[is_valid_below_h].values
-            if len(valid_indices) > 1:
-                if len(valid_indices_below_h) >= 3.:
-                    ml_mean = air_balloon.loc[valid_indices][is_valid_below_h].mean()
-                else:
-                    ml_mean = air_balloon.loc[valid_indices[0:2]].mean()
-            elif len(valid_indices) == 1:
-                ml_mean = (air_balloon.iloc[0:1]).mean()
+            is_valid_below_h = (air_balloon.z < dpars['h'])
+            valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
+            if len(valid_indices_below_h) >= 3.:
+                ml_mean = air_balloon[is_valid_below_h].mean()
             else:
-                temp =  pd.DataFrame(air_balloon)
-                temp.iloc[0] = np.nan
-                ml_mean = temp
+                ml_mean = air_balloon[0:2].mean()
+                    
                        
             dpars['theta']= ml_mean.theta
             dpars['q']    = ml_mean.q
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 547e6f8..d3e8829 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -588,11 +588,14 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                                 # jsonstream.close()
                                 #  os.system('rm '+TEMPDIR+'/'+yamlfilename+'.buffer.yaml.'+str(current_tell))
                             records_station_chunk = pd.DataFrame.from_dict(dictout)
-                            records_station_chunk.index.set_names(('STNID','chunk','index'),inplace=True)
-                            print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
-                                  +str(STNID)+', chunk number '+str(chunk))
-                            records_station_chunk.to_pickle(path_yaml+'/'+pklfilename)
-                            records_station = pd.concat([records_station,records_station_chunk])
+                            if len(records_station_chunk) > 0:
+                                records_station_chunk.index.set_names(('STNID','chunk','index'),inplace=True)
+                                print('writing table file ('+path_yaml+'/'+pklfilename+') for station '\
+                                      +str(STNID)+', chunk number '+str(chunk))
+                                records_station_chunk.to_pickle(path_yaml+'/'+pklfilename)
+                                records_station = pd.concat([records_station,records_station_chunk])
+                            else:
+                                print('Warning. No records found in ',yaml_file)
                         # else:
                         #     os.system('rm '+pklfilename)
                 if (getchunk == 'all') and (pklfilename_unified not in pklchunks):
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 1795592..e4fd4e9 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -264,22 +264,25 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                         #(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.00) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25000) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  3.0000) & 
-                      #   (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
+                    #   (self.frames['stats']['records_all_stations_ini'].lat >-2.  ) & 
+                     #    (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.00) & 
+                        (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25000) & 
+                      #  (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  3.0000) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  400.) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -0.0006) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  0.0003) & 
-
-                     #     # filter 'extreme' model output -> should be investigated!
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
-                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
-                     #     (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
+                       (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -0.0005) & 
+                       (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  0.0003) & 
+                     #   ((self.frames['stats']['records_all_stations_ini'].ldatetime-
+                     #     self.frames['stats']['records_all_stations_ini'].lSunset).total_seconds()
+                     #    <= -2.*3600.) & 
+                         # filter 'extreme' model output -> should be investigated!
+                       (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0005) & 
+                       (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                        (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
diff --git a/class4gl/model.py b/class4gl/model.py
index 87dbd99..8a85c23 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -285,7 +285,11 @@ def init(self):
 
         self.dtheta     = self.input.dtheta     # initial temperature jump at h [K]
         self.gammatheta = self.input.gammatheta # free atmosphere potential temperature lapse rate [K m-1]
-        self.gammatheta_lower_limit = \
+
+        if 'gammatheta_lower_limit' not in self.input.__dict__.keys():
+            self.gammatheta_lower_limit = 0.002
+        else:
+            self.gammatheta_lower_limit = \
                 self.input.gammatheta_lower_limit # free atmosphere potential temperature lapse rate lower limit to avoid crashes [K m-1]
         self.advtheta   = self.input.advtheta   # advection of heat [K s-1]
         self.beta       = self.input.beta       # entrainment ratio for virtual heat [-]
@@ -418,6 +422,10 @@ def init(self):
             # we will update the original variables afterwards
             #self.air_ap['q'] = self.air_ap.QABS/1000.
 
+            #work around for corrupt input
+            if 'level_0' in self.air_ap.columns:
+                self.air_ap = self.air_ap.drop(columns=['level_0'])
+
             self.air_ap = \
                     self.air_ap.assign(R= lambda x: self.Rd*(1.-x.q) + self.Rv*x.q)
             # we require the temperature fields, since we need to consider
diff --git a/class4gl/setup/batch_setup_igra.py b/class4gl/setup/batch_setup_igra.py
index 4f1e432..4950dcf 100644
--- a/class4gl/setup/batch_setup_igra.py
+++ b/class4gl/setup/batch_setup_igra.py
@@ -26,7 +26,7 @@
 parser = argparse.ArgumentParser()
 #parser.add_argument('--timestamp')
 parser.add_argument('--exec') # chunk simulation script
-parser.add_argument('--pbs_string',default=' -l walltime=50:0:0')
+parser.add_argument('--pbs_string',default=' -l walltime=70:00:00')
 parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
 parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 # parser.add_argument('--first_YYYYMMDD',default="19810101")
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index f1516c2..ad0fa43 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -176,54 +176,51 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         while wy_strm.current is not None:
             
             c4gli.clear()
-            try: 
-                c4gli.get_profile_wyoming(wy_strm)
-                #print(STN['ID'],c4gli.pars.datetime)
-                #c4gli.get_global_input(globaldata)
-
-                print(c4gli.pars.STNID, c4gli.pars.ldatetime)
-
-                logic = dict()
-                logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
-
-                # Sounding should have taken place after 3 hours before sunrise.
-                # Note that the actual simulation only start at sunrise
-                # (specified by ldatetime_daylight), so the ABL cooling af the time
-                # before sunrise is ignored by the simulation.
-                logic['daylight'] = \
-                    ((c4gli.pars.ldatetime - 
-                      c4gli.pars.lSunrise).total_seconds()/3600. >= -3.)
-                
-                logic['springsummer'] = (c4gli.pars.theta > 278.)
-                
-                # we take 3000 because previous analysis (ie., HUMPPA) has
-                # focussed towards such altitude
-                le3000 = (c4gli.air_balloon.z <= 3000.)
-                logic['10measurements'] = (np.sum(le3000) >= 7) 
-
-                leh = (c4gli.air_balloon.z <= c4gli.pars.h)
-
-                logic['mlerrlow'] = (\
-                        (len(np.where(leh)[0]) > 0) and \
-                        # in cases where humidity is not defined, the mixed-layer
-                        # values get corr
-                        (not np.isnan(c4gli.pars.theta))\
-                                     #and \
-                        #(rmse(c4gli.air_balloon.theta[leh] , \
-                        #      c4gli.pars.theta,filternan_actual=True) < 1.0)\
-                              )
+            c4gli.get_profile_wyoming(wy_strm)
+            #print(STN['ID'],c4gli.pars.datetime)
+            #c4gli.get_global_input(globaldata)
+
+            print(c4gli.pars.STNID, c4gli.pars.ldatetime)
+
+            logic = dict()
+            logic['morning'] =  (c4gli.pars.ldatetime.hour <= 12.)
+
+            # Sounding should have taken place after 3 hours before sunrise.
+            # Note that the actual simulation only start at sunrise
+            # (specified by ldatetime_daylight), so the ABL cooling af the time
+            # before sunrise is ignored by the simulation.
+            logic['daylight'] = \
+                ((c4gli.pars.ldatetime - 
+                  c4gli.pars.lSunrise).total_seconds()/3600. >= -4.)
+            
+            logic['springsummer'] = (c4gli.pars.theta > 278.)
+            
+            # we take 3000 because previous analysis (ie., HUMPPA) has
+            # focussed towards such altitude
+            le3000 = (c4gli.air_balloon.z <= 3000.)
+            logic['10measurements'] = (np.sum(le3000) >= 7) 
+
+            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+
+            logic['mlerrlow'] = (\
+                    (len(np.where(leh)[0]) > 0) and \
+                    # in cases where humidity is not defined, the mixed-layer
+                    # values get corr
+                    (not np.isnan(c4gli.pars.theta)) and \
+                    (rmse(c4gli.air_balloon.theta[leh] , c4gli.pars.theta,filternan_actual=True) < 1.8)\
+                          )
     
 
-                logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
-                
-                print('logic:', logic)
-                # the result
-                morning_ok = np.mean(list(logic.values()))
-                print(morning_ok,c4gli.pars.ldatetime)
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+            
+            print('logic:', logic)
+            # the result
+            morning_ok = np.mean(list(logic.values()))
+            print(morning_ok,c4gli.pars.ldatetime)
 
-            except:
-                morning_ok =False
-                print('obtain morning not good')
+            # except:
+            #     morning_ok =False
+            #     print('obtain morning not good')
 
             # the next sounding will be used either for an afternoon sounding
             # or for the morning sounding of the next day.
@@ -238,109 +235,109 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                        c4gli.pars.ldatetime.day)
                 c4gli_afternoon.clear()
                 print('AFTERNOON PROFILE CLEARED')
-                # try:
-                c4gli_afternoon.get_profile_wyoming(wy_strm)
-                print('AFTERNOON PROFILE OK')
-
-                if wy_strm.current is not None:
-                    current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
-                else:
-                    # a dummy date: this will be ignored anyway
-                    current_date_afternoon = dt.date(1900,1,1)
-
-                # we will dump the latest afternoon sounding that fits the
-                # minimum criteria specified by logic_afternoon
-                print(current_date,current_date_afternoon)
-                c4gli_afternoon_for_dump = None
-                while ((current_date_afternoon == current_date) and \
-                       (wy_strm.current is not None)):
-                    logic_afternoon =dict()
-
-                    logic_afternoon['afternoon'] = \
-                        (c4gli_afternoon.pars.ldatetime.hour >= 12.)
-                    # the sounding should have taken place before 1 hours
-                    # before sunset. This is to minimize the chance that a
-                    # stable boundary layer (yielding very low mixed layer
-                    # heights) is formed which can not be represented by
-                    # class.
-                    logic_afternoon['daylight'] = \
-                      ((c4gli_afternoon.pars.ldatetime - \
-                        c4gli_afternoon.pars.lSunset \
-                       ).total_seconds()/3600. <= -1.)
-
-
-                    le3000_afternoon = \
-                        (c4gli_afternoon.air_balloon.z <= 3000.)
-                    logic_afternoon['5measurements'] = \
-                        (np.sum(le3000_afternoon) >= 7) 
-
-                    # we only store the last afternoon sounding that fits these
-                    # minimum criteria
-
-                    afternoon_ok = np.mean(list(logic_afternoon.values()))
-
-                    print('logic_afternoon: ',logic_afternoon)
-                    print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
-                    if afternoon_ok == 1.:
-                        # # doesn't work :(
-                        # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
-                        
-                        # so we just create a new one from the same wyoming profile
-                        c4gli_afternoon_for_dump = class4gl_input()
-                        c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
-
-                    wy_strm.find_next()
-                    c4gli_afternoon.clear()
+                try:
                     c4gli_afternoon.get_profile_wyoming(wy_strm)
+                    print('AFTERNOON PROFILE OK')
 
                     if wy_strm.current is not None:
                         current_date_afternoon = \
-                               dt.date(c4gli_afternoon.pars.ldatetime.year, \
-                                       c4gli_afternoon.pars.ldatetime.month, \
-                                       c4gli_afternoon.pars.ldatetime.day)
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
                     else:
                         # a dummy date: this will be ignored anyway
                         current_date_afternoon = dt.date(1900,1,1)
 
-                    # Only in the case we have a good pair of soundings, we
-                    # dump them to disk
-                if c4gli_afternoon_for_dump is not None:
-                    c4gli.update(source='pairs',pars={'runtime' : \
-                        int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
-                             c4gli.pars.datetime_daylight).total_seconds())})
+                    # we will dump the latest afternoon sounding that fits the
+                    # minimum criteria specified by logic_afternoon
+                    print(current_date,current_date_afternoon)
+                    c4gli_afternoon_for_dump = None
+                    while ((current_date_afternoon == current_date) and \
+                           (wy_strm.current is not None)):
+                        logic_afternoon =dict()
+
+                        logic_afternoon['afternoon'] = \
+                            (c4gli_afternoon.pars.ldatetime.hour >= 12.)
+                        # the sounding should have taken place before 1 hours
+                        # before sunset. This is to minimize the chance that a
+                        # stable boundary layer (yielding very low mixed layer
+                        # heights) is formed which can not be represented by
+                        # class.
+                        logic_afternoon['daylight'] = \
+                          ((c4gli_afternoon.pars.ldatetime - \
+                            c4gli_afternoon.pars.lSunset \
+                           ).total_seconds()/3600. <= -2.)
+
+
+                        le3000_afternoon = \
+                            (c4gli_afternoon.air_balloon.z <= 3000.)
+                        logic_afternoon['5measurements'] = \
+                            (np.sum(le3000_afternoon) >= 7) 
+
+                        # we only store the last afternoon sounding that fits these
+                        # minimum criteria
+
+                        afternoon_ok = np.mean(list(logic_afternoon.values()))
+
+                        print('logic_afternoon: ',logic_afternoon)
+                        print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
+                        if afternoon_ok == 1.:
+                            # # doesn't work :(
+                            # c4gli_afternoon_for_dump = cp.deepcopy(c4gli_afternoon)
+                            
+                            # so we just create a new one from the same wyoming profile
+                            c4gli_afternoon_for_dump = class4gl_input()
+                            c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+
+                        wy_strm.find_next()
+                        c4gli_afternoon.clear()
+                        c4gli_afternoon.get_profile_wyoming(wy_strm)
+
+                        if wy_strm.current is not None:
+                            current_date_afternoon = \
+                                   dt.date(c4gli_afternoon.pars.ldatetime.year, \
+                                           c4gli_afternoon.pars.ldatetime.month, \
+                                           c4gli_afternoon.pars.ldatetime.day)
+                        else:
+                            # a dummy date: this will be ignored anyway
+                            current_date_afternoon = dt.date(1900,1,1)
+
+                        # Only in the case we have a good pair of soundings, we
+                        # dump them to disk
+                    if c4gli_afternoon_for_dump is not None:
+                        c4gli.update(source='pairs',pars={'runtime' : \
+                            int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
+                                 c4gli.pars.datetime_daylight).total_seconds())})
     
     
-                    print('ALMOST...')
-                    if c4gli.pars.runtime > 3600*4.: # more than 4 hours simulation
-                            
+                        print('ALMOST...')
+                        if c4gli.pars.runtime > 3600*4.: # more than 4 hours simulation
+                                
         
-                        c4gli.get_global_input(globaldata)
-                        print('VERY CLOSE...')
-                        if c4gli.check_source_globaldata() and \
-                            (c4gli.check_source(source='wyoming',\
-                                               check_only_sections='pars')):
-                            print('starting dumps')
-                            c4gli.dump(fileout)
-                            print('file morning dumped')
-                            c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                            print('file afternoon dumped')
-                            
-                            
-                            # for keyEXP,dictEXP in experiments.items():
-                            #     
-                            #     c4gli.update(source=keyEXP,pars = dictEXP)
-                            #     c4gl = class4gl(c4gli)
-                            #     # c4gl.run()
-                            #     
-                            #     c4gl.dump(c4glfiles[key])
-                            
-                            print('HIT!!!')
-                            one_run = True
-                # except:
-                #     print('get profile failed')
+                            c4gli.get_global_input(globaldata)
+                            print('VERY CLOSE...')
+                            if c4gli.check_source_globaldata() and \
+                                (c4gli.check_source(source='wyoming',\
+                                                   check_only_sections='pars')):
+                                print('starting dumps')
+                                c4gli.dump(fileout)
+                                print('file morning dumped')
+                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                print('file afternoon dumped')
+                                
+                                
+                                # for keyEXP,dictEXP in experiments.items():
+                                #     
+                                #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                #     c4gl = class4gl(c4gli)
+                                #     # c4gl.run()
+                                #     
+                                #     c4gl.dump(c4glfiles[key])
+                                
+                                print('HIT!!!')
+                                one_run = True
+                except:
+                    print('get profile failed')
                 
     if one_run:
         #STN.name = STN.name
@@ -355,6 +352,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                       refetch_records=True,
                                       )
     else:
+        print('no valid record found. Removing files', fnout, fnout_afternoon)
         os.system('rm '+fnout)
         os.system('rm '+fnout_afternoon)
 
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 3639c2d..d759e28 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -352,14 +352,14 @@ def execute(**kwargs):
                         c4gl = class4gl(c4gli_morning)
     
                         if args.error_handling == 'dump_always':
-                            try:
-                                print('checking data sources')
-                                if not c4gli_morning.check_source_globaldata():
-                                    print('Warning: some input sources appear invalid')
-                                c4gl.run()
-                                print('run succesful')
-                            except:
-                                print('run not succesful')
+                            # try:
+                            print('checking data sources')
+                            if not c4gli_morning.check_source_globaldata():
+                                print('Warning: some input sources appear invalid')
+                            c4gl.run()
+                            print('run succesful')
+                            # except:
+                            #     print('run not succesful')
                             onerun = True
     
                             print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 

From 4b48c0c109c68e404e8598907d5fc1280634764b Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Mon, 4 Mar 2019 16:37:35 +0100
Subject: [PATCH 123/129] multiple updates

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 3ea0222..b82c7c4 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.9.4',
+        version='0.9.5',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 98b1a223ee8db1c243035cc5669222db3ecb9b2c Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 29 May 2019 14:31:12 +0200
Subject: [PATCH 124/129] multiple updates

---
 class4gl/class4gl.py                       |  65 +--
 class4gl/data_global.py                    |  11 +-
 class4gl/interface/interface_stations.py   |  21 +-
 class4gl/interface/taylorDiagram.py        |   7 +-
 class4gl/interface_functions.py            |   8 +-
 class4gl/interface_multi.py                |  62 ++-
 class4gl/model.py                          |  10 +-
 class4gl/setup/setup_igra.py               | 115 ++--
 class4gl/setup/update_input.py             |   8 +-
 class4gl/setup/update_setup.py             | 327 ------------
 class4gl/simulations/batch_simulations.pbs |   6 +-
 class4gl/simulations/copy_update.py        | 584 +++++++++++----------
 class4gl/simulations/simulations.py        |   7 +
 class4gl/simulations/simulations_iter.py   |   1 +
 setup.py                                   |   2 +-
 15 files changed, 511 insertions(+), 723 deletions(-)
 delete mode 100644 class4gl/setup/update_setup.py

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index bebf496..cdb85c8 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -554,7 +554,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         string =  '\n'.join(string)
         
         columns = [ 'PRES', 'HGHT', 'TEMP', 'DWPT', 'RELH', 'MIXR', 'DRCT','SKNT' , 'THTA','THTE', 'THTV']             
-        air_balloon = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
+        air_balloon_in = pd.read_fwf(io.StringIO(str(string)),widths=[7]*11,names=columns,skiprows=5,dtype=np.float,skipfooter=0)#.iloc[5:-1]
         #ONE_COLUMN = pd.read_table(io.StringIO(str(string)),sep=r"\s*",skiprows=[0,1,3,4])
         
         #string =  soup.pre.next_sibling.next_sibling
@@ -573,17 +573,18 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         dpars['STNID'] = dpars['Station number']
 
         # altitude above ground level
-        air_balloon['z'] = air_balloon.HGHT -dpars['Station elevation']
+        air_balloon = pd.DataFrame()
+        air_balloon['z'] = air_balloon_in.HGHT -dpars['Station elevation']
         # absolute humidity in g/kg
-        air_balloon['q']= (air_balloon.MIXR/1000.) \
+        air_balloon['q']= (air_balloon_in.MIXR/1000.) \
                               / \
-                             (air_balloon.MIXR/1000.+1.)
+                             (air_balloon_in.MIXR/1000.+1.)
         # convert wind speed from knots to m/s
-        air_balloon['WSPD'] = 0.51444 * air_balloon.SKNT
-        angle_x = (90.-air_balloon.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
+        air_balloon['V'] = 0.51444 * air_balloon_in.SKNT
+        angle_x = (90.-air_balloon_in.DRCT)/180.*np.pi # assuming that wind in direction of the south is 0 degrees.
         
-        air_balloon['u'] = air_balloon.WSPD * np.sin(angle_x)
-        air_balloon['v'] = air_balloon.WSPD * np.cos(angle_x)
+        air_balloon['u'] = air_balloon.V * np.sin(angle_x)
+        air_balloon['v'] = air_balloon.V * np.cos(angle_x)
 
         
 
@@ -592,8 +593,9 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
 
         air_balloon['R'] = (Rd*(1.-air_balloon.q) + Rv*air_balloon.q)
-        air_balloon['p'] = air_balloon.PRES*100.
+        air_balloon['p'] = air_balloon_in.PRES*100.
 
+        air_balloon['t'] = air_balloon_in['TEMP']+273.15
 
         # Therefore, determine the sounding that are valid for 'any' column 
         is_valid = ~np.isnan(air_balloon).any(axis=1) & (air_balloon.z >= 0)
@@ -608,9 +610,8 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         if len(air_balloon) > 2:
 
             dpars['Ps'] = air_balloon.p.values[0]
-            air_balloon['t'] = air_balloon['TEMP']+273.15
             air_balloon['theta'] = (air_balloon.t) * \
-                       (dpars['Ps']/(air_balloon.PRES*100.))**(air_balloon['R']/cp)
+                       (dpars['Ps']/(air_balloon.p))**(air_balloon['R']/cp)
             air_balloon['thetav']   = air_balloon['theta']*(1. + 0.61 * air_balloon['q'])
 
             # t_cut_off = 1.5
@@ -628,7 +629,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
 
 
             #calculated mixed-layer height considering the critical Richardson number of the virtual temperature profile
-            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.WSPD)
+            dpars['h'],dpars['h_u'],dpars['h_l'] = blh(air_balloon.z,air_balloon.thetav,air_balloon.V)
             
             dpars['h_b'] = np.max((dpars['h'],10.))
             dpars['h_u'] = np.max((dpars['h_u'],10.)) #upper limit of mixed layer height
@@ -654,10 +655,10 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
             # ... and those of the mixed layer
             is_valid_below_h = (air_balloon.z < dpars['h'])
             valid_indices_below_h =  air_balloon.index[is_valid_below_h].values
-            if len(valid_indices_below_h) >= 3.:
+            if len(valid_indices_below_h) >= 2.:
                 ml_mean = air_balloon[is_valid_below_h].mean()
             else:
-                ml_mean = air_balloon[0:2].mean()
+                ml_mean = air_balloon.iloc[0:1].mean()
                     
                        
             dpars['theta']= ml_mean.theta
@@ -679,9 +680,9 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         #calculate mixed-layer jump ( this should be larger than 0.1)
         
         air_ap_head['z'] = pd.Series(np.array([2.,dpars['h'],dpars['h']]))
-        air_ap_head['HGHT'] = air_ap_head['z'] \
-                                + \
-                                np.round(dpars[ 'Station elevation'],1)
+        # air_ap_head['HGHT'] = air_ap_head['z'] \
+        #                         + \
+        #                         np.round(dpars[ 'Station elevation'],1)
         
         # make a row object for defining the jump
         jump = air_ap_head.iloc[0] * np.nan
@@ -716,7 +717,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         
                air_ap_head[column][2] += jump[column]
         
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
+        air_ap_head.V = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
 
 
 
@@ -838,9 +839,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
 
         # we round the columns to a specified decimal, so that we get a clean
         # output format for yaml
-        decimals = {'p':0,'HGHT':1,'t':2,'DWPT':2,'RELH':2,'MIXR':2,\
-                   'DRCT':2 ,'SKNT':2,   'theta':4,   'THTE':2,  'THTV':2,\
-                   'z':2, 'q':5, 'WSPD':2, 'u':4,       'v':4}
+        decimals = {'p':0, 't':2, 'theta':4, 'z':2, 'q':5, 'V':2, 'u':4, 'v':4}
 # 
         for column,decimal in decimals.items():
             air_balloon[column] = air_balloon[column].round(decimal)
@@ -1078,9 +1077,9 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                         if globaldata.datasets[key].page.variables['lon'].values[ilonmin] > globaldata.datasets[key].page.variables['lon'].values[ilon]:
                             ilonmin = ilon        
                         
-                        # for the koeppen climate classification we just take nearest
+                        # for the koeppen climate classification and midsummermonth, we just take nearest
                         print(key)
-                        if key == 'KGC':
+                        if key in ['KGC','midsummermonth','AI']:
                             ilatrange = range(ilat,ilat+1)
                             ilonrange = range(ilon,ilon+1)
                         else:
@@ -1099,7 +1098,8 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                             
                             idatetime = np.where((DIST) == np.min(DIST))[0][0]
                             #print('idatetime',idatetime,globaldata.datasets[key].variables['time'].values[idatetime],classdatetime)
-                            if key not in ['Tsoil','T2']:
+
+                            if key not in ['Tsoil','T2','blptb_daymax','t2m_daymax','t2m','blpt','blpt_afternoon','blh','t2m_afternoon','blh_afternoon','blq','blq_afternoon','blptb','blptb_afternoon','blptw','blptw_afternoon','HI','HI_afternoon','rh100','rh100_afternoon']:
                                 if ((globaldata.datasets[key].page.variables['time'].values[idatetime] < classdatetime) ):
                                     idatetime += 1
                             
@@ -1118,9 +1118,9 @@ def get_global_input(self, globaldata,only_keys=None,exclude_keys=None):
                                 idatetime = idatetime - 1
                                 idatetimeend = idatetimeend - 1
 
-                            # in case of soil temperature, we take the exact
+                            # in case of soil temperature or maximum daytime temperature, we take the exact
                             # timing (which is the morning)
-                            if key in ['Tsoil','T2']:
+                            if key in ['Tsoil','T2','t2m_daymax','t2m','blpt','blpt_afternoon','blh','t2m_afternoon','blh_afternoon','blq','blq_afternoon','blptb','blptb_afternoon','blptw','blptw_afternoon','HI','HI_afternoon','rh100','rh100_afternoon']:
                                 idatetimeend = idatetime
                             
                             idts = range(idatetime,idatetimeend+1)
@@ -1469,13 +1469,6 @@ def check_source_globaldata(self):
         # and now we can get the surface values
         #class_settings = class4gl_input()
         #class_settings.set_air_input(input_atm)
-        
-        # we only allow non-polar stations
-        if not (self.pars.lat <= 60.):
-            source_globaldata_ok = False
-            self.logger.warning('cveg  is invalid: ('+str(self.pars.cveg)+')')
-        
-        # check lat and lon
         if (pd.isnull(self.pars.lat)) or (pd.isnull(self.pars.lon)):
             source_globaldata_ok = False
             self.logger.warning('lat  is invalid: ('+str(self.pars.lat)+')')
@@ -1596,7 +1589,6 @@ def mixed_layer_fit(self,air_ap,source,mode):
             mlvalues['v']  = np.nan
             
 
-        self.update(source='fit_from_'+source,pars=mlvalues)
 
 
         # First 3 data points of the mixed-layer fit. We create a empty head
@@ -1644,10 +1636,11 @@ def mixed_layer_fit(self,air_ap,source,mode):
                   jump.theta = np.max((0.1,jump.theta))
         
                air_ap_head[column][2] += jump[column]
+               mlvalues['d'+column] = jump[column]
         
-        air_ap_head.WSPD = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
-
+        self.update(source='fit_from_'+source,pars=mlvalues)
 
+        air_ap_head.V = np.sqrt(air_ap_head.u**2 +air_ap_head.v**2)
 
         # make theta increase strong enough to avoid numerical
         # instability
diff --git a/class4gl/data_global.py b/class4gl/data_global.py
index 73f0dda..c46b65e 100644
--- a/class4gl/data_global.py
+++ b/class4gl/data_global.py
@@ -151,6 +151,11 @@ def set_page(self,ipage,page=None):
                if 'level' in self.page.dims:
                    self.page = self.page.rename({'level':'lev'})
 
+               lon = self.page.lon.values
+               lon[lon > 180.] -= 360.
+               self.page.lon.values = lon[:]
+
+
                self.page = self.page.rename(self.renames)
                self.page = self.page.squeeze(drop=True)
 
@@ -213,7 +218,6 @@ def browse_page(self,rewind=2,**args):
         else:
             self.logger.debug("I'm now at page "+ str(self.ipage))
 
-
 class data_global(object):
     def __init__(self,sources= {
         'KOEPPEN:KGC'   : '/user/data/gent/gvo000/gvo00090/EXT/data/KOEPPEN/Koeppen-Geiger.nc',
@@ -259,6 +263,11 @@ def __init__(self,sources= {
         #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
         "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
         "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
+        "MSWEPGLEAM:AI"      : '/data/gent/vo/000/gvo00090/D2D/data/Aridity//Ep_1981_2017_MO_meanhottestmonth.nc',
+        "ERA5:t2m_daymax" : '/data/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/t2m_1hourly_for_t2m_daymax.nc:t2m',
+        "ERA5:blptb_daymax" : '/scratch/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/blptb_1hourly_for_blptb_daymax.nc:blptb',
+        # "ERA5:slhf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/slhf_1hourly/slhf_*_1hourly.nc',
+        # "ERA5:sshf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/sshf_1hourly/sshf_*_1hourly.nc',
         #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
         },debug_level=None):
         self.library = {} #unique references to data sources being used. They can be files that are original on the disks or some unambiguous xarray virtual sources. These references are used in other variables. This way, a file or source cannot be loaded twice (a warning is made if one would try it).
diff --git a/class4gl/interface/interface_stations.py b/class4gl/interface/interface_stations.py
index e98bbee..9fc2162 100644
--- a/class4gl/interface/interface_stations.py
+++ b/class4gl/interface/interface_stations.py
@@ -176,8 +176,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
         # Q95 = obs.quantile(0.95)
         # Q95 = obs.quantile(0.90)
         # Add RMS contours, and label them
-        contours = dias[varkey].add_contours(levels=5, colors='0.5') # 5 levels
-        dias[varkey].ax.clabel(contours, inline=1, fontsize=10, fmt='%.1f')
+        contours = dias[varkey].add_contours(levels=5, colors='0.7') # 5 levels
+        dias[varkey].ax.clabel(contours, inline=1, fontsize=10,fmt='%.1f')
         #dia._ax.set_title(season.capitalize())
         i += 1
     
@@ -197,7 +197,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # print(PR)
             print(varkey,STD,STD_OBS,STD/STD_OBS,PR)
             dias[varkey].add_sample(STD/STD_OBS, PR,
-                           marker='o', ms=5, ls='',
+                           marker='o', ms=7, ls='',
                            #mfc='k', mec='k', # B&W
                            mfc=colors[ikey], mec=colors[ikey], # Colors
                            label=key,zorder=101)
@@ -234,6 +234,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                'Bias = '+format((BIAS*1000.),'0.2f')+r'$\,  \mathrm{g\,  kg^{-1}\,  h^{-1}}$'+' \n'+\
                                r'$R$ = '+format(PR,'0.2f')
                 ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+                # ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
        horizontalalignment='right', verticalalignment='bottom' ,
         bbox={'edgecolor':'black',
                           'fc':'white',  
@@ -245,8 +246,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                 'RMSE = '+format(RMSE,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
                                 'Bias = '+format(BIAS,'0.1f')+r'$\,  \mathrm{m\, h^{-1}}$'+'\n'+\
                                 r'$R$ = '+format(PR,'0.2f')
-                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
-       horizontalalignment='left', verticalalignment='top' ,
+                ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='right', verticalalignment='bottom' ,
         bbox={'edgecolor':'black',
                           'fc':'white',  
                               'boxstyle':'square',
@@ -258,8 +259,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                 'Bias = '+format(BIAS,'0.3f')+r'$\, \mathrm{K\, h^{-1}}$'+'\n'+\
                                 r'$R$ = '+format(PR,'0.2f')
 
-                ann = axes[varkey].annotate(annotate_text, xy=(0.05, .97 ), xycoords='axes fraction',fontsize=9,
-       horizontalalignment='left', verticalalignment='top' ,
+                ann = axes[varkey].annotate(annotate_text, xy=(0.95, .05 ), xycoords='axes fraction',fontsize=9,
+       horizontalalignment='right', verticalalignment='bottom' ,
         bbox={'edgecolor':'black',
                           'fc':'white',  
                               'boxstyle':'square',
@@ -303,7 +304,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                 
                 dias[varkey].add_sample(station_end_mod.std()/station_obs.std(),
                                pearsonr(station_end_mod,station_obs)[0],#annotate=symbols[istation],
-                               marker=symbols[istation], ms=5, ls='',
+                               marker=symbols[istation], ms=7, ls='',
                                mfc='k', mec='k', # B&W
                                #mfc=colors[ikey], mec=colors[ikey], # Colors
                                label=key,zorder=100)
@@ -367,12 +368,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     leg = []
     isymbol = 0
     for icurrent_station,current_station in c4gldata[key].frames['worldmap']['stations'].table.iterrows():
-        leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=10)
+        leg1, = ax.plot([],'k'+symbols[isymbol] ,markersize=14)
         leg.append(leg1)
         isymbol += 1
     
     # symbol for all stations
-    leg1, = ax.plot([],'ko',markersize=10)
+    leg1, = ax.plot([],'ko',markersize=14)
     leg.append(leg1)
     
     
diff --git a/class4gl/interface/taylorDiagram.py b/class4gl/interface/taylorDiagram.py
index 5ece652..8d3e72d 100644
--- a/class4gl/interface/taylorDiagram.py
+++ b/class4gl/interface/taylorDiagram.py
@@ -192,7 +192,8 @@ def test1():
     dia.add_grid()
 
     # Add RMS contours, and label them
-    contours = dia.add_contours(colors='0.5')
+    print('BLABLA')
+    # contours = dia.add_contours(colors='lightgrey')
     PLT.clabel(contours, inline=1, fontsize=10, fmt='%.2f')
 
     # Add a figure legend
@@ -235,7 +236,7 @@ def test2():
                        label=name)
 
     # Add RMS contours, and label them
-    contours = dia.add_contours(levels=5, colors='0.5')  # 5 levels in grey
+    # contours = dia.add_contours(levels=5, colors='lightgrey')  # 5 levels in grey
     PLT.clabel(contours, inline=1, fontsize=10, fmt='%.0f')
 
     dia.add_grid()                                  # Add grid
@@ -245,7 +246,7 @@ def test2():
     fig.legend(dia.samplePoints,
                [ p.get_label() for p in dia.samplePoints ],
                numpoints=1, prop=dict(size='small'), loc='upper right')
-    fig.suptitle("Taylor diagram", size='x-large')  # Figure title
+    # fig.suptitle("Taylor diagram", size='x-large')  # Figure title
 
     return dia
 
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index d3e8829..240f246 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -496,7 +496,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                         generate_pkl = True
                     if not generate_pkl:
                         records_station_chunk = pd.read_pickle(path_yaml+'/'+pklfilename)
-                        records_station = pd.concat([records_station,records_station_chunk])
+                        records_station = pd.concat([records_station,records_station_chunk],sort=True)
                        # irecord = 0
                     else:
                         with open(path_yaml+'/'+yamlfilename) as yaml_file:
@@ -572,8 +572,8 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                                            # # needed for Ruby
                                            # dictouttemp[key] = dt.datetime.strptime(value,"%Y-%m-%d %H:%M:%S %z")
 
-                                           # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
-                                           dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
+                                           # # Workaround. Unfortunately, Ruby puts it in local time of the computer. Turn it back to UTC (note that UTC means actually local time)!!!
+                                           # dictouttemp[key] = dictouttemp[key].astimezone(pytz.UTC)
                                 recordindex = record['index']
                                 dictouttemp['chunk'] = chunk
                                 dictouttemp['index_start'] = index_start
@@ -604,7 +604,7 @@ def get_records(stations,path_yaml,getchunk='all',subset='morning',refetch_recor
                                   +str(STNID))
                     records_station.to_pickle(path_yaml+'/'+pklfilename_unified)
 
-            records = pd.concat([records,records_station])
+            records = pd.concat([records,records_station],sort=True)
     return records
 
 def stdrel(mod,obs,columns):
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index e4fd4e9..8931172 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -264,30 +264,58 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                    #   (self.frames['stats']['records_all_stations_ini'].lat >-2.  ) & 
-                     #    (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.00) & 
-                        (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25000) & 
-                      #  (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  3.0000) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25) & 
+                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
+                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
+                          (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  2.0000) & 
                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  400.) & 
-                       (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -0.0005) & 
-                       (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  0.0003) & 
-                     #   ((self.frames['stats']['records_all_stations_ini'].ldatetime-
-                     #     self.frames['stats']['records_all_stations_ini'].lSunset).total_seconds()
-                     #    <= -2.*3600.) & 
-                         # filter 'extreme' model output -> should be investigated!
-                       (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0005) & 
-                       (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
-                        (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
+                         (self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  400.) & 
+                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  350.) & 
+                           (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+                           (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.0006) & 
+                  #         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+
+                           # filter 'extreme' model output -> should be investigated!
+                           (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
+                           (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                           (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+                           (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.3) & 
                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
                          ~np.isnan(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt) & 
                          ~np.isnan(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt))
+# 
+# 
+# 
+# 
+#                     #   (self.frames['stats']['records_all_stations_ini'].lat >-2.  ) & 
+#                          (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.00) & 
+#                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25000) & 
+#                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  3.0000) & 
+#                          (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  1.8000) & 
+#                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
+#                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
+#                          #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  350.) & 
+#                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  450.) & 
+#                        (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -0.0006) & 
+#                        (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  0.0003) & 
+#                      #   ((self.frames['stats']['records_all_stations_ini'].ldatetime-
+#                      #     self.frames['stats']['records_all_stations_ini'].lSunset).total_seconds()
+#                      #    <= -2.*3600.) & 
+#                          # filter 'extreme' model output -> should be investigated!
+#                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0005) & 
+#                         (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+#                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
+#                           (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.) & 
+#                         # (self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  400.) & 
+#                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
+#                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
+#                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
+#                          ~np.isnan(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt) & 
+#                          ~np.isnan(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt))
 
                 for key in self.frames['stats'].keys():
                     if (type(self.frames['stats'][key]) == pd.DataFrame) and \
diff --git a/class4gl/model.py b/class4gl/model.py
index 8a85c23..30387f0 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -208,6 +208,7 @@ def run(self):
         self.exitmodel()
     
     def init(self):
+        self.dtmax = +np.inf
         # assign variables from input data
         # initialize constants
         self.Lv         = 2.5e6                 # heat of vaporization [J kg-1]
@@ -460,7 +461,7 @@ def init(self):
                 value_new = self.air_ap[var][indexh[0][0]]
                 
                 if ((value_old is not None) & (value_old != value_new)):
-                    warnings.warn("Warning:  input was provided ("+str(value_old)+ "kg kg-1), but it is now overwritten by the first level (index 0) of air_ap.var which is different (" +str(value_new)+"kg kg-1).") 
+                    warnings.warn("Warning:  input was provided ("+str(value_old)+ "), but it is now overwritten by the first level (index 0) of air_ap.var which is different (" +str(value_new)+").") 
                 self.__dict__[var] = value_new
 
                 # make a profile of the stratification 
@@ -480,6 +481,11 @@ def init(self):
                 gammavar = np.array(gammavar)
                 self.air_ap = self.air_ap.assign(**{'gamma'+var : gammavar})
 
+                value_old = self.__dict__['d'+var]
+                value_new = self.air_ap[var][2] - self.air_ap[var][1]
+                if ((value_old is not None) & (value_old != value_new)):
+                    warnings.warn("Warning:  input was provided ("+str(value_old)+ "), but it is now overwritten by the first level (index 0) of air_ap.dvar which is different (" +str(value_new)+").") 
+                self.__dict__['d'+var] = value_new
 
                 # gammatheta, gammaq, gammau, gammav are updated here.
                 self.__dict__['gamma'+var] = \
@@ -782,8 +788,8 @@ def init(self):
             self.run_mixed_layer()
 
     def timestep(self):
-
         self.dtmax = +np.inf
+
         self.logger.debug('before stats') 
         self.statistics()
 
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index ad0fa43..b37225a 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -156,10 +156,12 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     fnout = args.path_output+"/"+format(STN.name,'05d')+"_ini.yaml"
     fnout_afternoon = args.path_output+"/"+format(STN.name,'05d')+"_end.yaml"
     
+    fnout_diag = args.path_output+"/"+format(STN.name,'05d')+"_diag.pkl"
 
     # c4glfiles = dict([(EXP,odirexperiments[EXP]+'/'+format(STN['ID'],'05d')+'.yaml') \
     #                   for EXP in experiments.keys()])
         
+    dict_diag_station = {}
     with open(fnout,'w') as fileout, \
          open(fnout_afternoon,'w') as fileout_afternoon:
         wy_strm = wyoming(PATH=args.path_input, STNM=STN.name)
@@ -191,23 +193,25 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # before sunrise is ignored by the simulation.
             logic['daylight'] = \
                 ((c4gli.pars.ldatetime - 
-                  c4gli.pars.lSunrise).total_seconds()/3600. >= -4.)
+                  c4gli.pars.lSunrise).total_seconds()/3600. >= -3)
             
             logic['springsummer'] = (c4gli.pars.theta > 278.)
+            print(c4gli.pars.theta)
             
             # we take 3000 because previous analysis (ie., HUMPPA) has
             # focussed towards such altitude
             le3000 = (c4gli.air_balloon.z <= 3000.)
-            logic['10measurements'] = (np.sum(le3000) >= 7) 
+            logic['7measurements'] = (np.sum(le3000) >= 7) 
 
-            leh = (c4gli.air_balloon.z <= c4gli.pars.h)
+            leh = (c4gli.air_balloon.z < c4gli.pars.h)
 
             logic['mlerrlow'] = (\
                     (len(np.where(leh)[0]) > 0) and \
                     # in cases where humidity is not defined, the mixed-layer
                     # values get corr
-                    (not np.isnan(c4gli.pars.theta)) and \
-                    (rmse(c4gli.air_balloon.theta[leh] , c4gli.pars.theta,filternan_actual=True) < 1.8)\
+                    (not np.isnan(c4gli.pars.theta)) and (
+                    # in cases where humidity is not defined, the mixed-layer
+                    (rmse(c4gli.air_balloon.theta[leh] , c4gli.pars.theta,filternan_actual=True) < 1.5))\
                           )
     
 
@@ -216,8 +220,11 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             print('logic:', logic)
             # the result
             morning_ok = np.mean(list(logic.values()))
+            logic['morning_ok'] = morning_ok
             print(morning_ok,c4gli.pars.ldatetime)
 
+
+
             # except:
             #     morning_ok =False
             #     print('obtain morning not good')
@@ -227,6 +234,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             wy_strm.find_next()
             # If the morning is ok, then we try to find a decent afternoon
             # sounding
+            logic_afternoon_def =dict()
+
             if morning_ok == 1.:
                 print('MORNING OK!')
                 # we get the current date
@@ -252,6 +261,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                     # minimum criteria specified by logic_afternoon
                     print(current_date,current_date_afternoon)
                     c4gli_afternoon_for_dump = None
+                    afternoon_first = True
                     while ((current_date_afternoon == current_date) and \
                            (wy_strm.current is not None)):
                         logic_afternoon =dict()
@@ -263,22 +273,36 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                         # stable boundary layer (yielding very low mixed layer
                         # heights) is formed which can not be represented by
                         # class.
-                        logic_afternoon['daylight'] = \
+                        logic_afternoon['afternoon_daylight'] = \
                           ((c4gli_afternoon.pars.ldatetime - \
                             c4gli_afternoon.pars.lSunset \
-                           ).total_seconds()/3600. <= -2.)
-
+                           ).total_seconds()/3600. <= -1.)
 
                         le3000_afternoon = \
                             (c4gli_afternoon.air_balloon.z <= 3000.)
-                        logic_afternoon['5measurements'] = \
+                        logic_afternoon['afternoon_7measurements'] = \
                             (np.sum(le3000_afternoon) >= 7) 
 
+                        leh = (c4gli_afternoon.air_balloon.z < c4gli_afternoon.pars.h)
+                        logic_afternoon['afternoon_mlerrlow'] = (\
+                                (len(np.where(leh)[0]) > 0) and \
+                                # in cases where humidity is not defined, the mixed-layer
+                                # values get corr
+                                (not np.isnan(c4gli_afternoon.pars.theta)) and (
+                                # in cases where humidity is not defined, the mixed-layer
+                                (rmse(c4gli_afternoon.air_balloon.theta[leh] , c4gli_afternoon.pars.theta,filternan_actual=True) < 1.5))\
+                                      )
+
                         # we only store the last afternoon sounding that fits these
                         # minimum criteria
 
                         afternoon_ok = np.mean(list(logic_afternoon.values()))
 
+                        #we set the definitive first afternoon logic only the first time. We only set a new one later if we find a good one.
+                        if afternoon_first:
+                            logic_afternoon_def = {**logic_afternoon,**dict()}
+                            afternoon_first = False
+
                         print('logic_afternoon: ',logic_afternoon)
                         print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
                         if afternoon_ok == 1.:
@@ -288,7 +312,10 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                             # so we just create a new one from the same wyoming profile
                             c4gli_afternoon_for_dump = class4gl_input()
                             c4gli_afternoon_for_dump.get_profile_wyoming(wy_strm)
+                            logic_afternoon_def = {**logic_afternoon,**dict()}
+                            afternoon_set = True
 
+                        
                         wy_strm.find_next()
                         c4gli_afternoon.clear()
                         c4gli_afternoon.get_profile_wyoming(wy_strm)
@@ -301,43 +328,65 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                         else:
                             # a dummy date: this will be ignored anyway
                             current_date_afternoon = dt.date(1900,1,1)
+                    if not afternoon_set:
+                        logic_afternoon_def = {**logic_afternoon,**dict()}
 
                         # Only in the case we have a good pair of soundings, we
                         # dump them to disk
+                    logic_afternoon_def['afternoon_ok'] = False
                     if c4gli_afternoon_for_dump is not None:
+                        logic_afternoon_def['afternoon_ok'] = True
                         c4gli.update(source='pairs',pars={'runtime' : \
                             int((c4gli_afternoon_for_dump.pars.datetime_daylight - 
                                  c4gli.pars.datetime_daylight).total_seconds())})
     
     
+                        logic_afternoon_def['runtime_ok'] = False
                         print('ALMOST...')
-                        if c4gli.pars.runtime > 3600*4.: # more than 4 hours simulation
-                                
+                        if c4gli.pars.runtime >= 3600*4.: # more than 4 hours simulation
+                             
+                            logic_afternoon_def['runtime_ok'] = True
+                            logic_afternoon_def['not_polar'] = False
+                            if abs(c4gli.pars.lat) < 70.:
+                                logic_afternoon_def['not_polar'] = True
+                                logic_afternoon_def['global_parameters_ok'] = False
         
-                            c4gli.get_global_input(globaldata)
-                            print('VERY CLOSE...')
-                            if c4gli.check_source_globaldata() and \
-                                (c4gli.check_source(source='wyoming',\
-                                                   check_only_sections='pars')):
-                                print('starting dumps')
-                                c4gli.dump(fileout)
-                                print('file morning dumped')
-                                c4gli_afternoon_for_dump.dump(fileout_afternoon)
-                                print('file afternoon dumped')
-                                
-                                
-                                # for keyEXP,dictEXP in experiments.items():
-                                #     
-                                #     c4gli.update(source=keyEXP,pars = dictEXP)
-                                #     c4gl = class4gl(c4gli)
-                                #     # c4gl.run()
-                                #     
-                                #     c4gl.dump(c4glfiles[key])
-                                
-                                print('HIT!!!')
-                                one_run = True
+                                c4gli.get_global_input(globaldata)
+                                print('VERY CLOSE...')
+                                if c4gli.check_source_globaldata() and \
+                                    (c4gli.check_source(source='wyoming',\
+                                                       check_only_sections='pars')):
+
+                                    logic_afternoon_def['global_parameters_ok'] = True
+                                    print('starting dumps')
+                                    c4gli.dump(fileout)
+                                    print('file morning dumped')
+                                    c4gli_afternoon_for_dump.dump(fileout_afternoon)
+                                    print('file afternoon dumped')
+                                    
+                                    
+                                    # for keyEXP,dictEXP in experiments.items():
+                                    #     
+                                    #     c4gli.update(source=keyEXP,pars = dictEXP)
+                                    #     c4gl = class4gl(c4gli)
+                                    #     # c4gl.run()
+                                    #     
+                                    #     c4gl.dump(c4glfiles[key])
+                                    
+                                    print('HIT!!!')
+                                    one_run = True
                 except:
                     print('get profile failed')
+            dict_diag_day = {}
+
+            dict_diag_day = {**dict_diag_day , **c4gli.pars.__dict__}
+            dict_diag_day = {**dict_diag_day , **logic}
+            dict_diag_day = {**dict_diag_day , **logic_afternoon_def}
+            for key,value in dict_diag_day.items():
+                if key not in dict_diag_station.keys():
+                    dict_diag_station[key] = {}
+                dict_diag_station[key][(c4gli.pars.STNID,c4gli.pars.ldatetime)] = dict_diag_day[key]
+    pd.DataFrame.from_dict(dict_diag_station).to_pickle(fnout_diag)
                 
     if one_run:
         #STN.name = STN.name
diff --git a/class4gl/setup/update_input.py b/class4gl/setup/update_input.py
index 635e1c0..9f1a894 100644
--- a/class4gl/setup/update_input.py
+++ b/class4gl/setup/update_input.py
@@ -23,11 +23,11 @@
 parser.add_argument('--station_id') # run a specific station id
 parser.add_argument('--error_handling',default='dump_on_success')
 parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
-parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
+parser.add_argument('--subset_input',default='ini') # this tells which yaml subset
                                                       # to initialize with.
                                                       # Most common options are
                                                       # 'morning' and 'ini'.
-parser.add_argument('--subset_output',default='morning')
+parser.add_argument('--subset_output',default='ini')
 
 
 # Tuntime is usually specified from the afternoon profile. You can also just
@@ -58,6 +58,10 @@
             "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
             "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
             "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
+            "ERA5:sshf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/slhf_1hourly/slhf_*_1hourly.nc',
+            "ERA5:slhf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/sshf_1hourly/sshf_*_1hourly.nc',
             }}
 
 # ...  and load initial data pages
diff --git a/class4gl/setup/update_setup.py b/class4gl/setup/update_setup.py
deleted file mode 100644
index a36b60a..0000000
--- a/class4gl/setup/update_setup.py
+++ /dev/null
@@ -1,327 +0,0 @@
-# -*- coding: utf-8 -*-
-
-""" 
-Purpose:
-    update variables in class4gl yaml files, eg., when you need new categorical
-    values in the table.
-
-
-"""
-import pandas as pd
-import io
-import os
-import numpy as np
-import datetime as dt
-import sys
-import pytz
-import math
-import dateutil.parser
-
-import argparse
-
-
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--first_station_row')
-parser.add_argument('--last_station_row')
-parser.add_argument('--path_output')
-parser.add_argument('--diag_tropo',default=None)#['advt','advq','advu','advv'])
-parser.add_argument('--station_id') # run a specific station id
-parser.add_argument('--mode',default='ini') # run a specific station id
-# this is the type of the yaml that needs to be updated. Can be 'ini' or 'mod'
-parser.add_argument('--updates')
-parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
-parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
-                                                      # to update in the yaml
-                                                      # dataset.
-                                                      # Most common options are
-                                                      # 'morning' and 'ini'.
-
-parser.add_argument('--split_by',default=-1)# station soundings are split
-
-#parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--global_keys') 
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# iniitialize global data
-globaldata = data_global()
-if 'era_profiles' in args.updates.strip().split(" "):
-    globaldata.sources = {**globaldata.sources,**{
-            "ERAINT:t"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/t_6hourly/t_*_6hourly.nc",
-            "ERAINT:q"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/q_6hourly/q_*_6hourly.nc",
-            "ERAINT:u"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/u_6hourly/u_*_6hourly.nc",
-            "ERAINT:v"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/v_6hourly/v_*_6hourly.nc",
-            }}
-
-# ...  and load initial data pages
-globaldata.load_datasets(recalc=0)
-
-
-print("getting stations")
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
-
-print('defining all_stations_select')
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
-else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print("getting all records of the whole batch")
-all_records_morning_select = get_records(all_stations_select,\
-                                         args.path_input,\
-                                         subset=args.subset_input,
-                                         refetch_records=False,
-                                         )
-
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
-
-
-    # if not (int(args.split_by) > 0) :
-    #         raise ValueError("global_chunk_number is specified, but --split-by is not a strict positive number, so I don't know how to split the batch into chunks.")
-
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
-            #chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
-
-            chunks_current_station = len(all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique())
-            print('chunks_current_station',chunks_current_station)
-
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk =all_records_morning_station_select.query('STNID == '+str(current_station.name)).chunk.unique()[int(args.global_chunk_number) - totalchunks ]
-        
-            totalchunks +=chunks_current_station
-        
-
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
-    else:
-        if args.split_by != -1:
-            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
-        
-
-#print(all_stations)
-print('Fetching current records')
-records_input = get_records(run_stations,\
-                              args.path_input,\
-                              subset=args.subset_input,
-                              refetch_records=False,
-                              )
-
-# if args.timestamp is None:
-#     backupdir = args.path_input+'/'+dt.datetime.now().isoformat()+'/'
-# else: 
-#     backupdir = args.path_input+'/'+args.timestamp+'/'
-# print('creating backup dir: '+backupdir)
-# os.system('mkdir -p "'+backupdir+'"')
-
-
-os.system('mkdir -p '+args.path_output)
-
-for istation,current_station in run_stations.iterrows():
-    records_input_station = records_input.query('STNID == ' +\
-                                                    str(current_station.name))
-
-    records_input_station_chunk = records_input_station.query('STNID == ' +\
-                                                    str(current_station.name)+\
-                                                   '& chunk == '+str(run_station_chunk))
-    print('lenrecords_input_station_chunk: ',len(records_input_station_chunk))
-    print('split_by*run_station_chunk',int(args.split_by) * int(run_station_chunk))
-    print('split_by*run_station_chunk+1',int(args.split_by) * int(run_station_chunk+1))
-    
-    # if (int(args.split_by) * int(run_station_chunk)) >= (len(records_forcing_station)):
-    #     print("warning: outside of profile number range for station "+\
-    #           str(current_station)+". Skipping chunk number for this station.")
-    if len(records_input_station_chunk) == 0:
-        print("warning: outside of profile number range for station "+\
-              str(current_station)+". Skipping chunk number for this station.")
-    else:
-        # normal case
-        if ((int(args.split_by) > 0) or \
-            (os.path.isfile(args.path_input+'/'+format(current_station.name,'05d')+'_'+\
-                 str(run_station_chunk)+'_'+args.subset_input+'.yaml'))):
-            fn_input = \
-                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
-                    str(run_station_chunk)+'_'+args.subset_input+'.yaml'
-            file_input = \
-                open(fn_input,'r')
-            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
-            file_output = \
-                open(fn_output,'w')
-            # fn_forcing_pkl = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-
-            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-        else:
-            print("\
-Warning. We are choosing chunk 0 without specifying it in filename.    \
- No-chunk naming will be removed in the future."\
-                 )
-
-            fn_input = \
-                    args.path_input+'/'+format(current_station.name,'05d')+'_'+\
-                    args.subset_input+'.yaml'
-            file_input = \
-                open(fn_input,'r')
-            fn_output = args.path_output+'/'+'/'+format(current_station.name,'05d')+'_'+\
-                     str(run_station_chunk)+'_'+args.subset_output+'.yaml'
-            file_output = \
-                open(fn_output,'w')
-            # fn_forcing_pkl = args.path_forcing+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.pkl'
-
-            # fn_backup = backupdir+format(current_station.name,'05d')+'_'+\
-            #          str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
-            # fn_backup_pkl = backupdir+format(current_station.name,'05d')+'_'+\
-            #          args.subset_forcing+'.pkl'
-
-        onerun = False
-        print('starting station chunk number: '\
-              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-
-        #records_forcing_station_chunk = records_forcing_station[(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-
-        # records_forcing_station_chunk = records_forcing.query('STNID == ' +\
-        #                                                 str(current_station.name)+\
-        #                                                '& chunk == '+str(run_station_chunk))
-        isim = 0
-        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
-                print('starting '+str(isim+1)+' out of '+\
-                  str(len(records_input_station_chunk) )+\
-                  ' (station total: ',str(len(records_input_station)),')')  
-            
-                c4gli_output = get_record_yaml(file_input, 
-                                                record_input.index_start, 
-                                                record_input.index_end,
-                                                mode=args.mode)
-                if args.diag_tropo is not None:
-                    seltropo = (c4gli_input.air_ac.p > c4gli_input.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                    profile_tropo = c4gli_input.air_ac[seltropo]
-                    for var in args.diag_tropo:
-                        if var[:3] == 'adv':
-                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                        else:
-                            print("warning: tropospheric variable "+var+" not recognized")
-                if 'era_profiles' in args.updates.strip().split(" "):
-                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
-
-                c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
-
-                cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-                Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-                Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-                R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
-                rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
-                dz = c4gli_output.air_ac.delpdgrav/rho
-                z = [dz.iloc[-1]/2.]
-                for idz in list(reversed(range(0,len(dz)-1,1))):
-                    z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
-                z = list(reversed(z))
-
-                theta = c4gli_output.air_ac.t * \
-                           (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
-                thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
-
-                
-                c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
-                                                                       'theta':list(theta),
-                                                                       'thetav':list(thetav),
-                                                                      }))
-                air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
-                air_ap_mode = 'b'
-                air_ap_input_source = c4gli_output.query_source('air_ac:theta')
-
-
-                c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
-                                     source=air_ap_input_source,
-                                     mode=air_ap_mode)
-
-                if not c4gli_output.check_source_globaldata():
-                    print('Warning: some input sources appear invalid')
-
-
-                
-                #print('c4gli_forcing_ldatetime',c4gli_forcing.pars.ldatetime)
-                
-                # if args.global_keys is not None:
-                #     print(args.global_keys.strip(' ').split(' '))
-                #     c4gli_forcing.get_global_input(
-                #         globaldata, 
-                #         only_keys=args.global_keys.strip(' ').split(' ')
-                #     )
-
-                c4gli_output.dump(file_output)
-                    
-                    
-                onerun = True
-                isim += 1
-
-
-        file_input.close()
-        file_output.close()
-
-        if onerun:
-            # os.system('mv "'+fn_forcing+'" "'+fn_backup+'"')
-            # if os.path.isfile(fn_forcing_pkl):
-            #     os.system('mv "'+fn_forcing_pkl+'" "'+fn_backup_pkl+'"')
-            # os.system('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-            # print('mv "'+fn_experiment+'" "'+fn_forcing+'"')
-            records_forcing_current_cache = get_records(pd.DataFrame([current_station]),\
-                                                       args.path_output+'/'+'/',\
-                                                       getchunk = int(run_station_chunk),\
-                                                       subset=args.subset_output,
-                                                       refetch_records=True,
-                                                       )
-
diff --git a/class4gl/simulations/batch_simulations.pbs b/class4gl/simulations/batch_simulations.pbs
index 4f12ccc..2426063 100644
--- a/class4gl/simulations/batch_simulations.pbs
+++ b/class4gl/simulations/batch_simulations.pbs
@@ -8,10 +8,10 @@
 #PBS -N c4gl_sim
 
 module purge
-source ~/.bashrc
 
-echo loading modules: $LOADDEPSCLASS4GL 
-$LOADDEPSCLASS4GL 
+# echo loading modules: $LOADDEPSCLASS4GL 
+# $LOADDEPSCLASS4GL 
+source ~/load_anaconda.sh
 
 EXEC_ALL="python $C4GLJOB_exec --global_chunk_number $PBS_ARRAYID"
 
diff --git a/class4gl/simulations/copy_update.py b/class4gl/simulations/copy_update.py
index 2410d2c..8dcea02 100644
--- a/class4gl/simulations/copy_update.py
+++ b/class4gl/simulations/copy_update.py
@@ -9,308 +9,324 @@
 import pytz
 import math
 
-import argparse
-
-#if __name__ == '__main__':
-parser = argparse.ArgumentParser()
-#parser.add_argument('--timestamp')
-parser.add_argument('--path_input')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/SOUNDINGS/')
-parser.add_argument('--path_output')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
-parser.add_argument('--first_station_row')
-parser.add_argument('--last_station_row')
-parser.add_argument('--updates')
-parser.add_argument('--station_id') # run a specific station id
-parser.add_argument('--error_handling',default='dump_on_success')
-parser.add_argument('--diag_tropo',default=['advt','advq','advu','advv'])
-parser.add_argument('--subset_input',default='morning') # this tells which yaml subset
-                                                      # to initialize with.
-                                                      # Most common options are
-                                                      # 'morning' and 'ini'.
-parser.add_argument('--subset_output',default='morning')
 
+arguments = []
 
+#parser.add_argument('--timestamp')
+arguments.append(dict(arg='--path_forcing',\
+                    help='directory of forcing data to initialize and constrain the ABL model simulations'))
+arguments.append(dict(arg='--path_output',
+                    help='output directory in which the output as subdirectories are stored'))#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
+arguments.append(dict(arg='--first_station_row',\
+                    help='starting row number of stations table'))
+arguments.append(dict(arg='--last_station_row',\
+                    help='ending row number of stations table'))
+arguments.append(dict(arg='--global_vars',\
+                    help="global vars to update"))
+arguments.append(dict(arg='--station_id',\
+                    help="process a specific station id"))
+arguments.append(dict(arg='--error_handling',\
+                    default='dump_on_success',\
+                    help="type of error handling: either\n - 'dump_on_success' (default)\n - 'dump_always'"))
+arguments.append(dict(arg='--diag_tropo',\
+                    default=['advt','advq','advu','advv'],\
+                    help="field to diagnose the mean in the troposphere (<= 3000m)"))
+arguments.append(dict(arg='--subset_forcing',
+                    default='ini', 
+                    help="This indicates which yaml subset to initialize with.  Most common options are 'ini' (default) and 'morning'."))
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
 
-parser.add_argument('--experiments')
-parser.add_argument('--split_by',default=-1)# station soundings are split
-                                            # up in chunks
-
-#parser.add_argument('--station-chunk',default=0)
-parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
-parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
-parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
-args = parser.parse_args()
-
-sys.path.insert(0, args.c4gl_path_lib)
-from class4gl import class4gl_input, data_global,class4gl
-from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
-from class4gl import blh,class4gl_input
-
-# this is a variant of global run in which the output of runs are still written
-# out even when the run crashes.
-
-# #only include the following timeseries in the model output
-# timeseries_only = \
-# ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
-#  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
-#  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
-#  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
-#  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
-
+arguments.append(dict(arg='--split_by',\
+                    type=int,
+                    help="the maxmimum number of soundings that are contained in each output file of a station. -1 means unlimited (default). In case of arrays experiments, this is usually overwritten by 50."))
 
-EXP_DEFS  =\
-{
-  'ERA_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_ADV_ERA_NEW':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-    'GLOBAL_ADV_SHR':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'sw_shr':True},
-  'GLOBAL_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'GLOBAL_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-  'IOPS_NOAC':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
-  'IOPS_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
-  'IOPS_W':  {'sw_ac' : ['w',],'sw_ap': True,'sw_lit': False},
-  'IOPS_AC': {'sw_ac' : ['adv','w'],'sw_ap': True,'sw_lit': False},
-}
+#arguments.append(dict(arg='--station-chunk',default=0)
+arguments.append(dict(arg='--c4gl_path_lib',help="the path of the CLASS4GL program"))#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
+arguments.append(dict(arg='--global_chunk_number',help="this is the batch number of the expected series of experiments according to split_by"))
+arguments.append(dict(arg='--station_chunk_number',help="this is the batch number according to split_by in case of considering one station"))
 
-# ========================
-print("getting a list of stations")
-# ========================
 
-# these are all the stations that are found in the input dataset
-all_stations = stations(args.path_input,suffix=args.subset_input,refetch_stations=False)
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    #parser.add_argument('--timestamp')
+    for argument in arguments:
+        name = argument.pop('arg')
+        parser.add_argument(name,**argument)
 
-# ====================================
-print('defining all_stations_select')
-# ====================================
-
-# these are all the stations that are supposed to run by the whole batch (all
-# chunks). We narrow it down according to the station(s) specified.
-
-
-
-if args.station_id is not None:
-    print("Selecting station by ID")
-    stations_iter = stations_iterator(all_stations)
-    STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
-    all_stations_select = pd.DataFrame([run_station])
+    args = parser.parse_args()
 else:
-    print("Selecting stations from a row range in the table")
-    all_stations_select = pd.DataFrame(all_stations.table)
-    if args.last_station_row is not None:
-        all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
-    if args.first_station_row is not None:
-        all_stations_select = all_station_select.iloc[int(args.first_station):]
-print("station numbers included in the whole batch "+\
-      "(all chunks):",list(all_stations_select.index))
-
-print(all_stations_select)
-print("getting all records of the whole batch")
-all_records_input_select = get_records(all_stations_select,\
-                                         args.path_input,\
-                                         subset=args.subset_input,
-                                         refetch_records=False,
-                                         )
-
-# only run a specific chunck from the selection
-if args.global_chunk_number is not None:
-    if args.station_chunk_number is not None:
-        raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
-
-
-    if not (int(args.split_by) > 0) :
-            raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
-
-    run_station_chunk = None
-    print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
-    totalchunks = 0
-    stations_iter = all_stations_select.iterrows()
-    in_current_chunk = False
-    try:
-        while not in_current_chunk:
-            istation,current_station = stations_iter.__next__()
-            all_records_input_station_select = all_records_input_select.query('STNID == '+str(current_station.name))
-            chunks_current_station = math.ceil(float(len(all_records_input_station_select))/float(args.split_by))
-            print('chunks_current_station',chunks_current_station)
-            in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
-        
-            if in_current_chunk:
-                run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
-                run_station_chunk = int(args.global_chunk_number) - totalchunks 
-        
-            totalchunks +=chunks_current_station
+    class Namespace:
+        def __init__(self,**kwargs):
+            self.__dict__.update(kwargs)
+
+    args = Namespace()
+    for argument in arguments:
+        if 'default' in argument.keys():
+            args.__dict__[argument['arg'].strip('-')] = argument['default']
+        else:
+            args.__dict__[argument['arg'].strip('-')] = None
+    print(args.__dict__)
         
 
-    except StopIteration:
-       raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
-    print("station = ",list(run_stations.index))
-    print("station chunk number:",run_station_chunk)
-
-# if no global chunk is specified, then run the whole station selection in one run, or
-# a specific chunk for each selected station according to # args.station_chunk_number
-else:
-    run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
-    if args.station_chunk_number is not None:
-        run_station_chunk = int(args.station_chunk_number)
-        print("station(s) that is processed.",list(run_stations.index))
-        print("chunk number: ",run_station_chunk)
+def execute(**kwargs):
+    # note that with args, we actually mean the same as those specified with
+    # the argparse module above
+    
+    # overwrite the args according to the kwargs when the procedure is called
+    # as module function
+    for key,value in kwargs.items():
+        args.__dict__[key]  = value
+    
+    print("-- begin arguments --")
+    for key,value in args.__dict__.items():
+         print(key,': ',value)
+    print("-- end arguments ----")
+    
+    # load specified class4gl library
+    if args.c4gl_path_lib is not None:
+        sys.path.insert(0, args.c4gl_path_lib)
+    
+    from class4gl import class4gl_input, data_global,class4gl
+    from interface_multi import stations,stations_iterator, records_iterator,get_record_yaml,get_records
+    from class4gl import blh,class4gl_input
+    
+    # this is a variant of global run in which the output of runs are still written
+    # out even when the run crashes.
+    
+    # #only include the following timeseries in the model output
+    # timeseries_only = \
+    # ['Cm', 'Cs', 'G', 'H', 'L', 'LE', 'LEpot', 'LEref', 'LEsoil', 'LEveg', 'Lwin',
+    #  'Lwout', 'Q', 'RH_h', 'Rib', 'Swin', 'Swout', 'T2m', 'dq', 'dtheta',
+    #  'dthetav', 'du', 'dv', 'esat', 'gammaq', 'gammatheta', 'h', 'q', 'qsat',
+    #  'qsurf', 'ra', 'rs', 'theta', 'thetav', 'time', 'u', 'u2m', 'ustar', 'uw',
+    #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
+    
+    if (args.global_vars is not None):
+        globaldata = data_global()
+        globaldata.load_datasets(recalc=0)
+    
+    
+    # ========================
+    print("getting a list of stations")
+    # ========================
+    
+    # these are all the stations that are found in the input dataset
+    all_stations = stations(args.path_forcing,suffix=args.subset_forcing,refetch_stations=False)
+    
+    # ====================================
+    print('defining all_stations_select')
+    # ====================================
+    
+    # these are all the stations that are supposed to run by the whole batch (all
+    # chunks). We narrow it down according to the station(s) specified.
+    
+    if args.station_id is not None:
+        print("Selecting station by ID")
+        stations_iter = stations_iterator(all_stations)
+        STNID,run_station = stations_iter.set_STNID(STNID=int(args.station_id))
+        all_stations_select = pd.DataFrame([run_station])
     else:
-        if args.split_by != -1:
-            raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
-        run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
-        
-
-#print(all_stations)
-print('Fetching initial/forcing records')
-records_input = get_records(run_stations,\
-                              args.path_input,\
-                              subset=args.subset_input,
-                              refetch_records=False,
-                              )
-
-
-
-os.system('mkdir -p '+args.path_output)
-for istation,current_station in run_stations.iterrows():
-    print(istation,current_station)
-    records_input_station = records_input.query('STNID == '+str(current_station.name))
-    if (int(args.split_by) * int(run_station_chunk)) >= (len(records_input_station)):
-        print("warning: outside of profile number range for station "+\
-              str(current_station)+". Skipping chunk number for this station.")
+        print("Selecting stations from a row range in the table")
+        all_stations_select = pd.DataFrame(all_stations.table)
+        if args.last_station_row is not None:
+            all_stations_select = all_station_select.iloc[:(int(args.last_station)+1)]
+        if args.first_station_row is not None:
+            all_stations_select = all_station_select.iloc[int(args.first_station):]
+    print("station numbers included in the whole batch "+\
+          "(all chunks):",list(all_stations_select.index))
+    
+    print(all_stations_select)
+    print("getting all records of the whole batch")
+    all_records_morning_select = get_records(all_stations_select,\
+                                             args.path_forcing,\
+                                             subset=args.subset_forcing,
+                                             refetch_records=False,
+                                             )
+    
+    # only run a specific chunck from the selection
+    if args.global_chunk_number is not None:
+        if args.station_chunk_number is not None:
+            raise ValueError('You need to specify either global-chunk-number or station-chunk-number, not both.')
+    
+        if (args.split_by is None) or (args.split_by <= 0):
+                raise ValueError("global_chunk_number is specified, but --split_by is not a strict positive number, so I don't know how to split the batch into chunks.")
+    
+        run_station_chunk = None
+        print('determining the station and its chunk number according global_chunk_number ('+args.global_chunk_number+')')
+        totalchunks = 0
+        stations_iter = all_stations_select.iterrows()
+        in_current_chunk = False
+        try:
+            while not in_current_chunk:
+                istation,current_station = stations_iter.__next__()
+                all_records_morning_station_select = all_records_morning_select.query('STNID == '+str(current_station.name))
+                chunks_current_station = math.ceil(float(len(all_records_morning_station_select))/float(args.split_by))
+                print('chunks_current_station',chunks_current_station)
+                in_current_chunk = (int(args.global_chunk_number) < (totalchunks+chunks_current_station))
+            
+                if in_current_chunk:
+                    run_stations = pd.DataFrame([current_station])# run_stations.loc[(int(args.__dict__['last_station'])]
+                    run_station_chunk = int(args.global_chunk_number) - totalchunks 
+            
+                totalchunks +=chunks_current_station
+            
+    
+        except StopIteration:
+           raise ValueError("Could not determine station chunk number.  --global_chunk_number ("+args.global_chunk_number+") outside of range [0,"+ str(totalchunks)+'[')
+        print("station = ",list(run_stations.index))
+        print("station chunk number:",run_station_chunk)
+    
+    # if no global chunk is specified, then run the whole station selection in one run, or
+    # a specific chunk for each selected station according to # args.station_chunk_number
     else:
-        fn_input = args.path_input+'/'+format(current_station.name,'05d')+'_'+args.subset_input+'.yaml'
-        if os.path.isfile(fn_input):
-            file_input = open(fn_input)
+        run_stations = pd.DataFrame(all_stations_select)# run_stations.loc[(int(args.__dict__['last_station'])]
+        if args.station_chunk_number is not None:
+            run_station_chunk = int(args.station_chunk_number)
+            print("station(s) that is processed.",list(run_stations.index))
+            print("chunk number: ",run_station_chunk)
         else:
-            fn_input = \
-                 args.path_input+'/'+format(current_station.name,'05d')+\
-                 '_'+str(run_station_chunk)+'_'+args.subset_input+'.yaml'
-            file_input = open(fn_input)
-
-        fn_output = args.path_output+'/'+format(current_station.name,'05d')+'_'+\
-                 str(int(run_station_chunk))+'_'+args.subset_output+'.yaml'
-        file_output = open(fn_output,'w')
-
+            if args.split_by is not None:
+                raise ValueError("Chunks are defined by --split_by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split_by.")
+            run_station_chunk = 0
+            print("stations that are processed.",list(run_stations.index))
+            
+    
+    #print(all_stations)
+    print('Fetching initial/forcing records')
+    records_morning = get_records(run_stations,\
+                                  args.path_forcing,\
+                                  subset=args.subset_forcing,
+                                  refetch_records=False,
+                                  )
+    if len(records_morning) == 0:
+        raise IOError("No initialization records records found in "+\
+                      args.path_forcing+' (subset: '+args_subset_forcing+')')
+    
+    # note that if runtime is an integer number, we don't need to get the afternoon
+    # profiles. 
+    
+
+    
+    path_output = args.path_output
+    
+    os.system('mkdir -p '+path_output)
+    for istation,current_station in run_stations.iterrows():
+        # records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        records_morning_station = records_morning.loc[(current_station.name):(current_station.name)]
+
+        fn_morning = args.path_forcing+'/'+format(current_station.name,'05d')+'_'+args.subset_forcing+'.yaml'
+        if os.path.isfile(fn_morning):
+            file_morning = open(fn_morning)
+        else:
+            fn_morning = \
+                 args.path_forcing+'/'+format(current_station.name,'05d')+\
+                 '_'+str(run_station_chunk)+'_'+args.subset_forcing+'.yaml'
+            file_morning = open(fn_morning)
+    
+        # if args.runtime == 'from_profile_pair':
+        #     file_afternoon = open(args.path_forcing+'/'+format(current_station.name,'05d')+'_end.yaml')
+        fn_ini = path_output+'/'+format(current_station.name,'05d')+'_'+\
+                 str(int(run_station_chunk))+'_ini.yaml'
+        file_ini = open(fn_ini,'w')
+    
         #iexp = 0
         onerun = False
         print('starting station chunk number: '\
-              +str(run_station_chunk)+'(size: '+str(args.split_by)+' soundings)')
-
-        records_input_station_chunk = records_input_station.iloc[((run_station_chunk)*int(args.split_by)):((run_station_chunk+1)*int(args.split_by))] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
-
-        isim = 0
-        for (STNID,chunk,index),record_input in records_input_station_chunk.iterrows():
-                print('starting '+str(isim+1)+' out of '+\
-                  str(len(records_input_station_chunk) )+\
-                  ' (station total: ',str(len(records_input_station)),')')  
-            
-        
-                c4gli_output = get_record_yaml(file_input, 
-                                                record_input.index_start, 
-                                                record_input.index_end,
-                                                mode='ini')
-                if args.diag_tropo is not None:
-                    print('add tropospheric parameters on advection and subsidence (for diagnosis)')
-                    seltropo = (c4gli_input.air_ac.p > c4gli_input.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
-                    profile_tropo = c4gli_input.air_ac[seltropo]
-                    for var in args.diag_tropo:#['t','q','u','v',]:
-                        if var[:3] == 'adv':
-                            mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
-                            c4gli_output.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
-                        else:
-                            print("warning: tropospheric variable "+var+" not recognized")
-
-
-                if 'era_profiles' in args.updates.strip().split(" "):
-                    c4gli_output.get_global_input(globaldata,only_keys=['t','u','v','q','sp'])
-
-                    c4gli_output.update(source='era-interim',pars={'Ps' : c4gli_output.pars.sp})
-
-                    cp         = 1005.                 # specific heat of dry air [J kg-1 K-1]
-                    Rd         = 287.                  # gas constant for dry air [J kg-1 K-1]
-                    Rv         = 461.5                 # gas constant for moist air [J kg-1 K-1]
-                    R = (Rd*(1.-c4gli_output.air_ac.q) + Rv*c4gli_output.air_ac.q)
-                    rho = c4gli_output.air_ac.p/R/c4gli_output.air_ac.t
-                    dz = c4gli_output.air_ac.delpdgrav/rho
-                    z = [dz.iloc[-1]/2.]
-                    for idz in list(reversed(range(0,len(dz)-1,1))):
-                        z.append(z[-1]+ (dz[idz+1]+dz[idz])/2.)
-                    z = list(reversed(z))
-
-                    theta = c4gli_output.air_ac.t * \
-                               (c4gli_output.pars.sp/(c4gli_output.air_ac.p))**(R/cp)
-                    thetav   = theta*(1. + 0.61 * c4gli_output.air_ac.q)
-
-                    
-                    c4gli_output.update(source='era-interim',air_ac=pd.DataFrame({'z':list(z),
-                                                                           'theta':list(theta),
-                                                                           'thetav':list(thetav),
-                                                                          }))
-                    air_ap_input = c4gli_output.air_ac[::-1].reset_index().drop('index',axis=1)
-                    air_ap_mode = 'b'
-                    air_ap_input_source = c4gli_output.query_source('air_ac:theta')
-
-
-                    c4gli_output.mixed_layer_fit(air_ap=air_ap_input,
-                                         source=air_ap_input_source,
-                                         mode=air_ap_mode)
-
-
-
+              +str(run_station_chunk)+' (chunk size:',args.split_by,')')
+    
+        skip_chunk = False
+        if 'chunk' in records_morning.index.names:
+            records_morning_station_chunk = records_morning_station.loc[(current_station.name,run_station_chunk):(current_station.name,run_station_chunk)]
+        else:
+            start_record = run_station_chunk*args.split_by if run_station_chunk is not 0 else 0
+            end_record = (run_station_chunk+1)*args.split_by if args.split_by is not None else None
+            if start_record >= (len(records_morning_station)):
+                print("warning: outside of profile number range for station "+\
+                      str(current_station)+". Skipping chunk number for this station.")
+                skip_chunk = True
+                records_morning_station_chunk = None
+            else:
+                records_morning_station_chunk = records_morning_station.iloc[start_record:end_record] #  [(int(args.split_by)*run_station_chunk):(int(args.split_by)*(run_station_chunk+1))]
+
+        if not skip_chunk:
+    
+            isim = 0
+            for (STNID,chunk,index),record_morning in records_morning_station_chunk.iterrows():
+                    print('starting '+str(isim+1)+' out of '+\
+                      str(len(records_morning_station_chunk) )+\
+                      ' (station total: ',str(len(records_morning_station)),')')  
                 
-                c4gli_output.dump(file_output)
+            
+                    c4gli_morning = get_record_yaml(file_morning, 
+                                                    record_morning.index_start, 
+                                                    record_morning.index_end,
+                                                    mode='model_input')
                     
                     
-        file_output.close()
-        file_input.close()
-
-        if onerun:
-            records_output = get_records(pd.DataFrame([current_station]),\
-                                                       args.path_output,\
-                                                       getchunk = int(run_station_chunk),\
-                                                       subset=args.subset_output,
-                                                       refetch_records=True,
-                                                       )
-        else:
-            # remove empty files
-            os.system('rm '+fn_output)
-
-# # align afternoon records with initial records, and set same index
-# records_afternoon.index = records_afternoon.ldatetime.dt.date
-# records_afternoon = records_afternoon.loc[records_output.ldatetime.dt.date]
-# records_afternoon.index = records_output.index
-
-# stations_for_iter = stations(path_exp)
-# for STNID,station in stations_iterator(stations_for_iter):
-#     records_current_station_index = \
-#             (records_output.index.get_level_values('STNID') == STNID)
-#     file_current_station_mod = STNID
-# 
-#     with \
-#     open(path_exp+'/'+format(STNID,"05d")+'_output.yaml','r') as file_station_output, \
-#     open(path_exp+'/'+format(STNID,"05d")+'_mod.yaml','r') as file_station_mod, \
-#     open(path_input+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
-#         for (STNID,index),record_output in records_iterator(records_output):
-#             c4gli_output = get_record_yaml(file_station_output, 
-#                                         record_output.index_start, 
-#                                         record_output.index_end,
-#                                         mode='ini')
-#             #print('c4gli_in_ldatetime 3',c4gli_output.pars.ldatetime)
-# 
-#             record_mod = records_mod.loc[(STNID,index)]
-#             c4gl_mod = get_record_yaml(file_station_mod, 
-#                                         record_mod.index_start, 
-#                                         record_mod.index_end,
-#                                         mode='mod')
-#             record_afternoon = records_afternoon.loc[(STNID,index)]
-#             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
-#                                         record_afternoon.index_start, 
-#                                         record_afternoon.index_end,
-#                                         mode='ini')
-
+    
+            
+                    if args.global_vars is not None:
+                        c4gli_morning.get_global_input(globaldata,only_keys=args.global_vars.strip().split(','))
+    
+                    onerun = True
+    
+                    print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
+                    c4gli_morning.dump(file_ini)
+                        
+                        
+                    isim += 1
+    
+    
+            file_ini.close()
+            file_morning.close()
+    
+            if onerun:
+                records_ini = get_records(pd.DataFrame([current_station]),\
+                                                           path_output,\
+                                                           getchunk = int(run_station_chunk),\
+                                                           subset='ini',
+                                                           refetch_records=True,
+                                                           )
+            else:
+                # remove empty files
+                os.system('rm '+fn_ini)
+    
+    # # align afternoon records with initial records, and set same index
+    # records_afternoon.index = records_afternoon.ldatetime.dt.date
+    # records_afternoon = records_afternoon.loc[records_ini.ldatetime.dt.date]
+    # records_afternoon.index = records_ini.index
+    
+    # stations_for_iter = stations(path_output)
+    # for STNID,station in stations_iterator(stations_for_iter):
+    #     records_current_station_index = \
+    #             (records_ini.index.get_level_values('STNID') == STNID)
+    #     file_current_station_end_mod = STNID
+    # 
+    #     with \
+    #     open(path_output+'/'+format(STNID,"05d")+'_ini.yaml','r') as file_station_ini, \
+    #     open(path_output+'/'+format(STNID,"05d")+'_end_mod.yaml','r') as file_station_end_mod, \
+    #     open(path_forcing+'/'+format(STNID,"05d")+'_afternoon.yaml','r') as file_station_afternoon:
+    #         for (STNID,index),record_ini in records_iterator(records_ini):
+    #             c4gli_ini = get_record_yaml(file_station_ini, 
+    #                                         record_ini.index_start, 
+    #                                         record_ini.index_end,
+    #                                         mode='ini')
+    #             #print('c4gli_in_ldatetime 3',c4gli_ini.pars.ldatetime)
+    # 
+    #             record_end_mod = records_end_mod.loc[(STNID,index)]
+    #             c4gl_end_mod = get_record_yaml(file_station_end_mod, 
+    #                                         record_end_mod.index_start, 
+    #                                         record_end_mod.index_end,
+    #                                         mode='mod')
+    #             record_afternoon = records_afternoon.loc[(STNID,index)]
+    #             c4gl_afternoon = get_record_yaml(file_station_afternoon, 
+    #                                         record_afternoon.index_start, 
+    #                                         record_afternoon.index_end,
+    #                                         mode='ini')
+
+
+if __name__ == '__main__':
+    #execute(**vars(args))
+    execute()
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index d759e28..9cefd61 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -108,8 +108,15 @@ def execute(**kwargs):
     #  'v', 'v2m', 'vw', 'wq', 'wtheta', 'wthetae', 'wthetav', 'wthetae', 'zlcl']
     
     
+    # for iEXP in range(4):
+    #     EXPKEY = 'LCZ'+str(iEXP)
+    #     EXP_DEFS[EXPKEY] = {'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'urban':'LCZ'+str(iEXP)}
+
+
     EXP_DEFS  =\
     {
+      'LCZ':{'sw_ac' : [],'sw_ap': True,'sw_lit': False, 'urban':'lcw1'},
+
       'BASE':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
     
       'NOADV':{'sw_ac' : [],'sw_ap': True,'sw_lit': False},
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index bf0da9d..3ebe456 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -114,6 +114,7 @@ def execute(**kwargs):
       'BASE_ITER_ADV':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False},
       'BASE_ITER_W_ADV':{'sw_ac' : ['adv',"w"],'sw_ap': True,'sw_lit': False},
       'BASE_ITER_W':{'sw_ac' : ["w"],'sw_ap': True,'sw_lit': False},
+        'BASE_ITER_ADV_B05':{'sw_ac' : ['adv',],'sw_ap': True,'sw_lit': False,'beta':0.2},
     
         
       'ERA_NOAC_ITER':    {'sw_ac' : [],'sw_ap': True,'sw_lit': False},
diff --git a/setup.py b/setup.py
index b82c7c4..a536320 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='0.9.5',
+        version='1.0.0',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From e07b40c3198cfe1d43ee463f573c3e9a841b096d Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Wed, 29 May 2019 14:35:04 +0200
Subject: [PATCH 125/129] assign next version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index a536320..1506d2d 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='1.0.0',
+        version='1.0.1',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 05fecf5340f033493e8450177c6093f207f5b4ed Mon Sep 17 00:00:00 2001
From: woutersh 
Date: Fri, 12 Feb 2021 22:33:48 +0100
Subject: [PATCH 126/129] Compatibility fixes

---
 class4gl/class4gl.py                        | 37 +++++++-----
 class4gl/data_global.py                     | 30 +++++----
 class4gl/interface/interface_new_koeppen.py | 45 ++++++++------
 class4gl/interface_functions.py             |  5 ++
 class4gl/interface_multi.py                 | 40 ++++++------
 class4gl/model.py                           | 67 +++++++++++++++------
 class4gl/setup/setup_igra.py                |  2 +
 class4gl/simulations/batch_simulations.py   | 11 ++--
 class4gl/simulations/copy_update.py         |  4 +-
 class4gl/simulations/simulations.py         | 16 ++---
 class4gl/simulations/simulations_iter.py    |  9 +++
 11 files changed, 167 insertions(+), 99 deletions(-)

diff --git a/class4gl/class4gl.py b/class4gl/class4gl.py
index cdb85c8..f54db96 100644
--- a/class4gl/class4gl.py
+++ b/class4gl/class4gl.py
@@ -563,7 +563,7 @@ def get_profile_wyoming(self,wy_strm,air_ap_mode = 'b'):
         
         # this crazy long line just loads the sounding parameter table into parameters object (using amongst others the pandas internal engine to detect the right value types (int, float, np.Datetime64 etc.)).
         dpars = {**dpars,
-                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.convert_objects(convert_numeric=True).iloc[0].to_dict()
+                **pd.read_fwf(io.StringIO(str(string)),widths=[43,1,20],names=['descr','dummy','value']).iloc[1:-1].drop("dummy",1).set_index("descr").T.apply(pd.to_numeric,errors='ignore').iloc[0].to_dict()
                }
         
         # we get weird output when it's a numpy Timestamp, so we convert it to
@@ -1972,22 +1972,27 @@ def dump(self,file,include_input=False,timeseries_only=None):
 
         dictout = {}
         dictoutlast = {}
-        if timeseries_only == None:
-            outvars = self.__dict__['out'].__dict__.keys()
+        if not 'out' in self.__dict__.keys():
+            print('Warning: no timeseries section found in output.')
         else:
-            outvars = timeseries_only
-        for key in outvars:
-            dictout[key] = self.__dict__['out'].__dict__[key]
-            dictoutlast[key] = dictout[key][-1]
-
-            if type(dictoutlast[key]).__module__ == 'numpy':
-                dictoutlast[key] = dictoutlast[key].item() 
-            # convert numpy types to native python data types. This
-            # provides cleaner data IO with yaml:
-            if type(dictout[key]).__module__ == 'numpy':
-                dictout[key] = [ a.item() for a in \
-                                 self.__dict__['out'].__dict__[key]]
-            #dictout[key] = list(dictout[key] )
+            outvars = []
+            if timeseries_only == None:
+                    outvars = self.__dict__['out'].__dict__.keys()
+            else:
+                outvars = timeseries_only
+
+            for key in outvars:
+                dictout[key] = self.__dict__['out'].__dict__[key]
+                dictoutlast[key] = dictout[key][-1]
+
+                if type(dictoutlast[key]).__module__ == 'numpy':
+                    dictoutlast[key] = dictoutlast[key].item() 
+                # convert numpy types to native python data types. This
+                # provides cleaner data IO with yaml:
+                if type(dictout[key]).__module__ == 'numpy':
+                    dictout[key] = [ a.item() for a in \
+                                     self.__dict__['out'].__dict__[key]]
+                #dictout[key] = list(dictout[key] )
 
         yaml.dump({'pars' : {**dictoutlast,**dictpars}},file)
 
diff --git a/class4gl/data_global.py b/class4gl/data_global.py
index c46b65e..564f0f1 100644
--- a/class4gl/data_global.py
+++ b/class4gl/data_global.py
@@ -153,7 +153,7 @@ def set_page(self,ipage,page=None):
 
                lon = self.page.lon.values
                lon[lon > 180.] -= 360.
-               self.page.lon.values = lon[:]
+               self.page.lon.values[:] = lon[:]
 
 
                self.page = self.page.rename(self.renames)
@@ -226,10 +226,10 @@ def __init__(self,sources= {
         # 'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/SMroot_*_GLEAM_v3.1a.nc:SMroot',
         # 'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/BR_*_GLEAM_v3.1a.nc:BR',
         # 'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/v3.1a/????/EF_*_GLEAM_v3.1a.nc:EF',
-        'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
-        'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
+  #       'GLEAM:wg'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMsurf_*_GLEAM_v3.2a.nc:SMsurf',
+  #       'GLEAM:w2'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/SMroot_*_GLEAM_v3.2a.nc:SMroot',
         #'GLEAM:BR'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a/????/BR_*_GLEAM_v3.2a.nc:BR',
-        'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc:EF',
+  #       'GLEAM:EF'      : '/user/data/gent/gvo000/gvo00090/GLEAM/data/GLEAM_v3.2/v3.2a_OUTPUT/????/EF_*_GLEAM_v3.2a.nc:EF',
         "IGBPDIS:alpha" : "/user/data/gent/gvo000/gvo00090/EXT/data/IGBP-DIS/FRACTIONS_GLEAMv31a.nc",
         "GLAS:z0m"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
         "GLAS:z0h"      : "/user/data/gent/gvo000/gvo00090/EXT/data/GLAS/global_canopy_height_0.25.nc:Band1",
@@ -251,21 +251,25 @@ def __init__(self,sources= {
         "GIMMS:LAIpixel": "/user/data/gent/gvo000/gvo00090/EXT/data/GIMMS/v2/LAI/gimms-3g.v2.lai.1981-2015_monmean_remapcon_0.25.nc:LAI",
         #'CERES.low': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_*.nc%cldarea_low_1h',
         #'CERES.cc%20000301%20100101': '/user/data/gent/gvo000/gvo00090/vsc42247/EXT/data/CERES/CERES_SYN1deg-1H_Terra-Aqua-MODIS_Ed4A_Subset_$YYYYMMDD_CERES_START-$YYYYMMDD_CERES_END.nc.cldarea_total_1h%cldarea_total_1h'
-        "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
-        "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
-        "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
-        "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
-        "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
-        "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
-        "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
-        "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
+#         "ERAINT:advt_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_x_6hourly/advt_x*_6hourly.nc:advt_x",
+#         "ERAINT:advt_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advt_y_6hourly/advt_y*_6hourly.nc:advt_y",
+#         "ERAINT:advq_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_x_6hourly/advq_x*_6hourly.nc",
+#         "ERAINT:advq_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advq_y_6hourly/advq_y*_6hourly.nc",
+#         "ERAINT:advu_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_x_6hourly/advu_x*_6hourly.nc",
+#         "ERAINT:advu_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advu_y_6hourly/advu_y*_6hourly.nc",
+#         "ERAINT:advv_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_x_6hourly/advv_x*_6hourly.nc",
+#         "ERAINT:advv_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/advv_y_6hourly/advv_y*_6hourly.nc",
         #"ERAINT:divU_x"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_x_6hourly/divU_x*_6hourly.nc:__xarray_dataarray_variable__",
         #"ERAINT:divU_y"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/divU_y_6hourly/divU_y*_6hourly.nc:__xarray_dataarray_variable__",
         "ERAINT:sp"     : "/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/sp_6hourly/sp_*_6hourly.nc",
         "ERAINT:wp"  : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA-INTERIM/by_var_nc/w_6hourly_xarray/w*_6hourly.nc:w',
         "MSWEPGLEAM:AI"      : '/data/gent/vo/000/gvo00090/D2D/data/Aridity//Ep_1981_2017_MO_meanhottestmonth.nc',
-        "ERA5:t2m_daymax" : '/data/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/t2m_1hourly_for_t2m_daymax.nc:t2m',
+#        "ERA5:t2m_daymax" : '/data/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/t2m_1hourly_for_t2m_daymax.nc:t2m',
+        "ERA5:blpt_daymax" : '/scratch/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/blpt_1hourly_for_blpt_daymax.nc:blpt',
         "ERA5:blptb_daymax" : '/scratch/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/blptb_1hourly_for_blptb_daymax.nc:blptb',
+        "ERA5:blptb_daymean" : '/scratch/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/blptb_1hourly_daymean.nc:blptb',
+        "ERA5:blptb_daymean_ge90" : '/scratch/gent/vo/000/gvo00090/D2D/data/ERA5/by_var_nc/blptb_1hourly_daymean_for_blptb_daymean_mean3month_is_max_ge90.nc:blptb',
+
         # "ERA5:slhf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/slhf_1hourly/slhf_*_1hourly.nc',
         # "ERA5:sshf" : '/user/data/gent/gvo000/gvo00090/EXT/data/ERA5/by_var_nc/sshf_1hourly/sshf_*_1hourly.nc',
         #"MSWEP:pr"    :"/user/data/gent/gvo000/gvo00090/EXT/data/MSWEP/MSWEP_v1.2_precip_1979-2015/3hr/raw_data/globe/*.nc:precipitation"
diff --git a/class4gl/interface/interface_new_koeppen.py b/class4gl/interface/interface_new_koeppen.py
index dddb816..3dbe77f 100644
--- a/class4gl/interface/interface_new_koeppen.py
+++ b/class4gl/interface/interface_new_koeppen.py
@@ -246,6 +246,9 @@ def brightness(rrggbb):
 koeppenlookuptable = koeppenlookuptable.sort_values('amount',ascending=False)
 include_koeppen = list(koeppenlookuptable.KGCID)
 
+for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:2]):
+    c4gldata[key].frames['stats']['records_all_stations_ini']['KGCname'] =  \
+        c4gldata[key].frames['stats']['records_all_stations_ini']['KGC'].map(koeppenlookuptable['KGCID'])
 
 if args.make_figures:
     fig = plt.figure(figsize=(11,8.0))   #width,height
@@ -303,10 +306,11 @@ def brightness(rrggbb):
         #dia.ax.plot(x99,y99,color='k')
         i += 1
     
+
     i = 1
     for varkey in ['h','theta','q']:                                                    
         #for ikey,key in enumerate(args.experiments.strip(' ').split(' ')):
-        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:2]):
             # cc = c4gldata[key].frames['stats']['records_all_stations_ini']['cc']
             # clearsky = (cc < 0.05)
             # mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats'].loc[clearsky]['d'+varkey+'dt']
@@ -327,6 +331,7 @@ def brightness(rrggbb):
             RMSE = rmse(obs,mod)                                               
             BIAS = np.mean(mod) - np.mean(obs)
             STD = mod.std()
+            markers=['o','^']
             
             # fit = np.polyfit(x,y,deg=1)
             # axes[varkey].plot(x, fit[0] * x + fit[1],\
@@ -340,23 +345,23 @@ def brightness(rrggbb):
             # print(STD)
             # print(PR)
             dias[varkey].add_sample(STD/STD_OBS, PR,\
-                           marker='o',ls='', mfc='white',mec='black',
+                           marker=markers[ikey],ls='', mfc='white',mec='black',
                            zorder=-100,
-                           ms=3.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                           ms=2.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
                                 np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                            # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                            # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
                            )
             dias[varkey].add_sample(STD/STD_OBS, PR,\
-                           marker='o',ls='', mfc='none',mec='black',
+                           marker=markers[ikey],ls='', mfc='none',mec='black',
                            zorder=700,
-                           ms=3.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
+                           ms=2.5*np.sqrt(np.sum(np.array(koeppenlookuptable.amount.values,dtype=np.float)))/\
                                 np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                            # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                            # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
                            )
             dias[varkey].add_sample(STD/STD_OBS, PR,\
-                           marker='o',ls='', mfc='none',mec='black',
+                           marker=markers[ikey],ls='', mfc='none',mec='black',
                            zorder=700,
                            ms=1.
                            # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
@@ -379,7 +384,7 @@ def brightness(rrggbb):
     i = 1
     for varkey in ['h','theta','q']:                                                    
 
-        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:1]):
+        for ikey,key in enumerate(args.experiments.strip(' ').split(' ')[:2]):
             icolor = 0
             for ikoeppen,koeppen in koeppenlookuptable.iterrows():
                 if koeppen.amount >= 200:
@@ -403,26 +408,26 @@ def brightness(rrggbb):
                     
                     dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
                                    pearsonr(koeppen_end_mod,koeppen_obs)[0],
-                                   marker='o',linewidth=0.5,
+                                   marker=markers[ikey],linewidth=0.5,alpha=0.7,
                                             mfc=koeppen.color,mec='black',#koeppen.color,
                                             zorder=300+icolor,
-                                   ms=3.5*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
-                                   # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
-                                   # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
-                                   )
-                    dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
-                                   pearsonr(koeppen_end_mod,koeppen_obs)[0],
-                                   marker='o',linewidth=0.5,
-                                            mfc=koeppen.color,mec='black',#koeppen.color,
-                                            zorder=301+icolor, ms=1
+                                   ms=2.5*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                                    # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
                                    # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
                                    )
+                    # dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
+                    #                pearsonr(koeppen_end_mod,koeppen_obs)[0],
+                    #                marker=markers[ikey],linewidth=0.5,
+                    #                         mfc=koeppen.color,mec='black',#koeppen.color,
+                    #                         zorder=301+icolor, ms=1
+                    #                # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
+                    #                # bbox={'edgecolor':'black','boxstyle':'circle','fc':koeppen.color,'alpha':0.7}
+                    #                )
 
 
                     # dias[varkey].add_sample(koeppen_end_mod.std()/koeppen_obs.std(),
                     #                pearsonr(koeppen_end_mod,koeppen_obs)[0],
-                    #                         marker='o',linewidth=0.5, mfc='none',mec=str(koeppen.color),
+                    #                         marker=markers[ikey],linewidth=0.5, mfc='none',mec=str(koeppen.color),
                     #                         zorder=600+icolor,
                     #                ms=10.*np.sqrt(koeppen.amount)/np.mean(np.sqrt(np.array(koeppenlookuptable.amount.values,dtype=np.float)))
                     #                # annotate=koeppen.KGCID, color=koeppen.textcolor,weight='bold',fontsize=5.,\
@@ -530,7 +535,7 @@ def brightness(rrggbb):
         mod = c4gldata[key].frames['stats']['records_all_stations_end_mod_stats']['d'+varkey+'dt']
         obs = c4gldata[key].frames['stats']['records_all_stations_end_obs_stats']['d'+varkey+'dt']
         print ('filtering classes that have sufficient samples: ', include_koeppen)
-        filter_classess = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
+        filter_classes = (c4gldata[key].frames['stats']['records_all_stations_ini'].KGCname.isin(include_koeppen))
         mod = mod.loc[filter_classes]
         obs = obs.loc[filter_classes]
 
@@ -664,7 +669,7 @@ def brightness(rrggbb):
     # ax = fig.add_axes([0.05,0.00,0.15,0.15]) #[*left*, *bottom*, *width*,    *height*]
     # leg = []
     # for ikey,key in enumerate(args.experiments.strip().split(' ')):
-    #     leg1, = ax.plot([],colors[ikey]+'o' ,markersize=10)
+    #     leg1, = ax.plot([],colors[ikey]+markers[ikey] ,markersize=10)
     #     leg.append(leg1)
     # ax.axis('off')
     # #leg1 =
diff --git a/class4gl/interface_functions.py b/class4gl/interface_functions.py
index 240f246..fbc335a 100644
--- a/class4gl/interface_functions.py
+++ b/class4gl/interface_functions.py
@@ -110,6 +110,7 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
 
         # # needed in case of Ruby
         # os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
+        print('hello')
 
         return modelout
     elif mode == 'model_input':
@@ -142,6 +143,9 @@ def get_record_yaml(yaml_file,index_start,index_end,mode='model_output'):
         # # needed in case of ruby
         # os.system('rm '+TEMPDIR+'/'+shortfn+'.buffer.json.'+str(index_start))
         return c4gli
+    else:
+        print('Warning. Mode '+mode+' not recorgnized. Returning None')
+        return None
 
 
 
@@ -205,6 +209,7 @@ def __init__(self,path,suffix='ini',refetch_stations=True):
         self.table = self.table.set_index('STNID')
 
     def get_stations(self,suffix):
+        print(suffix)
         stations_list_files = glob.glob(self.path+'/?????_*_'+suffix+'.yaml')
         if len(stations_list_files) == 0:
             stations_list_files = glob.glob(self.path+'/?????_'+suffix+'.yaml')
diff --git a/class4gl/interface_multi.py b/class4gl/interface_multi.py
index 8931172..0c5a0dd 100644
--- a/class4gl/interface_multi.py
+++ b/class4gl/interface_multi.py
@@ -264,24 +264,28 @@ def __init__(self,path_exp,path_forcing=None,globaldata=None,refetch_records=Fal
                 print('exclude exceptional observations')
                 print('exclude unrealistic model output -> should be investigated!')
                 valid = (\
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
-                          (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  2.0000) & 
-                         #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
-                         (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  40.0000) & 
-                         (self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  400.) & 
-                          (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  350.) & 
-                           (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
-                           (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.0006) & 
-                  #         (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
-
-                           # filter 'extreme' model output -> should be investigated!
-                           (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
-                           (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
-                           (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .2) & 
-                           (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.3) & 
+                        (self.frames['stats']['records_all_stations_end_mod'].theta >  273.) & 
+                        (self.frames['stats']['records_all_stations_end_obs'].theta >  273.) & 
+                       #(self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.25) & 
+                        (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt >  0.) & 
+                       #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  0.25000) & 
+                       #(self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  1.8000) & 
+                       # (self.frames['stats']['records_all_stations_end_obs_stats'].dthetadt <  2.0000) & 
+                       #(self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  50.0000) & 
+                        (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt >  0.0000) & 
+                        # (self.frames['stats']['records_all_stations_end_mod_stats'].dhdt <  400.) & 
+                        #  (self.frames['stats']['records_all_stations_end_obs_stats'].dhdt <  350.) & 
+                        #   (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+                        #   (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt >  -.0006) & 
+                        #   (self.frames['stats']['records_all_stations_end_obs_stats'].dqdt <  .0003) & 
+
+                         # filter 'extreme' model output -> should be investigated!
+                       #    (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0006) & 
+                       #    (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt >  -.0006) & 
+                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .0) & 
+                       #  (self.frames['stats']['records_all_stations_end_mod_stats'].dhdt >  40.) & 
+                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt >  .0) & 
+                          (self.frames['stats']['records_all_stations_end_mod_stats'].dthetadt <  2.3) & 
                          # (self.frames['stats']['records_all_stations_end_mod_stats'].dqdt <  .0003) & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Cwb') & 
                          # (self.frames['stats']['records_all_stations_ini'].KGC != 'Dfc') & 
diff --git a/class4gl/model.py b/class4gl/model.py
index 30387f0..b2359d5 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -156,6 +156,10 @@ def __init__(self, model_input = None,debug_level=None):
                        #  self.input.air_ac.p.iloc[irow] *\
                        #  self.input.air_ac.delpdgrav.iloc[irow]*grav
 
+            #     if 'ts' in model_input.__dict__.keys():
+            #         self.input.__dict__['ts'] = model_input.__dict__['ts']
+                if 'ts' in model_input.__dict__.keys():
+                    self.input.__dict__['ts'] = model_input.__dict__['ts']
 
 
                 # 2. Air circulation data 
@@ -250,6 +254,7 @@ def init(self):
         self.R10        =  0.23;                # respiration at 10 C [mg CO2 m-2 s-1]
         self.E0         =  53.3e3;              # activation energy [53.3 kJ kmol-1]
 
+
         # Read switches
         self.sw_ml      = self.input.sw_ml      # mixed-layer model switch
         self.sw_shearwe = self.input.sw_shearwe # shear growth ABL switch
@@ -982,8 +987,20 @@ def run_mixed_layer(self):
 
         # Calculate entrainment fluxes
         self.wthetae     = -self.we * self.dtheta
+
+        #ent2
+        #self.wthetae         = self.input.ts.wthetae[self.t] #-self.we * self.dq
+
         self.wqe         = -self.we * self.dq
+        #ent and ent2  
+        #self.wqe         = self.input.ts.wqe[self.t] #-self.we * self.dq
         self.wCO2e       = -self.we * self.dCO2
+
+        # none(bot entrianment on) - ent2 (only dry air entrainment off): dry air entrainment effect
+        # ent2(only dry air entrainment off )  - ent (both theta and q entrainment off): dry air entrainment effect
+        # none - ent: full entrainment
+
+
         
         htend_pre       = self.we + self.ws + self.wf - self.M
         
@@ -1000,15 +1017,15 @@ def run_mixed_layer(self):
                           thetatend_pre + w_th_ft
         
         dtheta_pre = float(self.dtheta + dthetatend_pre *self.dt)
-        l_entrainment = True
+        self.l_entrainment = True
 
         if (self.dtheta <= 0.1) and (dthetatend_pre < 0.):
-            l_entrainment = False
+            self.l_entrainment = False
             warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
                           " Warning! temperature jump is at the lower limit and is not growing: entrainment is disabled for this (sub)timestep.") 
         elif dtheta_pre < 0.1:
             dtmax_new = float((0.1 - self.dtheta)/dthetatend_pre)
-            l_entrainment = True
+            self.l_entrainment = True
             warnings.warn(str(self.t)+"/"+str(self.tsteps)+\
                           " Warning! Potential temperature jump at mixed-layer height would become too low. So I'm limiting the timestep from "+ str(self.dtmax)+' to '+str(dtmax_new))
             self.dtmax = min(self.dtmax,dtmax_new)
@@ -1021,18 +1038,22 @@ def run_mixed_layer(self):
         # when entrainment is disabled, we just use the simplified formulation
         # as in Wouters et al., 2013 (section 2.2.1)
 
-        self.dthetatend = l_entrainment*dthetatend_pre + \
-                        (1.-l_entrainment)*0.
-        self.thetatend = l_entrainment*thetatend_pre + \
-                        (1.-l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
-        self.htend = l_entrainment*htend_pre + \
-                     (1.-l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
+        self.dthetatend = self.l_entrainment*dthetatend_pre + \
+                        (1.-self.l_entrainment)*0.
+        self.thetatend = self.l_entrainment*thetatend_pre + \
+                        (1.-self.l_entrainment)*((self.wtheta  ) / self.h + self.advtheta)
+        self.htend = self.l_entrainment*htend_pre + \
+                     (1.-self.l_entrainment)*((self.ws - self.M)+ self.thetatend/self.gammatheta)
         #print(l_entrainment,htend_pre,self.ws,self.M,self.thetatend,self.gammatheta)
         #stop
 
+        # qtend_pre = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        # q_pre = float(self.q + dtend_pre *self.dt)
+        # if q_pre < 0:
+        #     self.qtend = 
 
-        self.qtend       = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
-        self.CO2tend     = (self.wCO2   - l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
+        self.qtend       = (self.wq     - self.l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        self.CO2tend     = (self.wCO2   - self.l_entrainment*self.wCO2e   - self.wCO2M) / self.h + self.advCO2
 
 
         # self.qtend = l_entrainment*qtend_pre + \
@@ -1064,16 +1085,16 @@ def run_mixed_layer(self):
         #     self.dthetatend = dthetatend_pre
         #     self.thetatend = thetatend_pre
         
-        self.dqtend      = self.gammaq     * (self.we*l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
-        self.dCO2tend    = self.gammaCO2   * (self.we*l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
+        self.dqtend      = self.gammaq     * (self.we*self.l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+        self.dCO2tend    = self.gammaCO2   * (self.we*self.l_entrainment + self.wf - self.M) - self.CO2tend   + w_CO2_ft
      
         # assume u + du = ug, so ug - u = du
         if(self.sw_wind):
-            self.utend       = -self.fc * self.dv + (self.uw + l_entrainment*self.we * self.du)  / self.h + self.advu
-            self.vtend       =  self.fc * self.du + (self.vw + l_entrainment*self.we * self.dv)  / self.h + self.advv
+            self.utend       = -self.fc * self.dv + (self.uw + self.l_entrainment*self.we * self.du)  / self.h + self.advu
+            self.vtend       =  self.fc * self.du + (self.vw + self.l_entrainment*self.we * self.dv)  / self.h + self.advv
   
-            self.dutend      = self.gammau * (l_entrainment*self.we + self.wf - self.M) - self.utend
-            self.dvtend      = self.gammav * (l_entrainment*self.we + self.wf - self.M) - self.vtend
+            self.dutend      = self.gammau * (self.l_entrainment*self.we + self.wf - self.M) - self.utend
+            self.dvtend      = self.gammav * (self.l_entrainment*self.we + self.wf - self.M) - self.vtend
         
         # tendency of the transition layer thickness
         if(self.ac > 0 or self.lcl - self.h < 300):
@@ -1111,6 +1132,18 @@ def integrate_mixed_layer(self):
         self.theta    = theta0  + self.dtcur * self.thetatend
         #print(dtheta0,self.dtcur,self.dthetatend)
         self.dtheta   = dtheta0 + self.dtcur * self.dthetatend
+
+
+        # qtend_pre = (self.wq     - l_entrainment*self.wqe     - self.wqM  ) / self.h + self.advq
+        # q_pre = float(self.q + dtend_pre *self.dt)
+        # if q_pre < 0:
+        #     self.qtend = 
+
+        q_pre        = q0      + self.dtcur * self.qtend
+        if q_pre < 0.001:
+            self.qtend = (0.001-q0)/self.dtcur
+            self.dqtend      = self.gammaq     * (self.we*self.l_entrainment + self.wf - self.M) - self.qtend     + w_q_ft
+
         self.q        = q0      + self.dtcur * self.qtend
         self.dq       = dq0     + self.dtcur * self.dqtend
         self.CO2      = CO20    + self.dtcur * self.CO2tend
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index b37225a..9414220 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -179,6 +179,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             
             c4gli.clear()
             c4gli.get_profile_wyoming(wy_strm)
+            #import pdb;pdb.set_trace()
             #print(STN['ID'],c4gli.pars.datetime)
             #c4gli.get_global_input(globaldata)
 
@@ -343,6 +344,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     
                         logic_afternoon_def['runtime_ok'] = False
                         print('ALMOST...')
+
                         if c4gli.pars.runtime >= 3600*4.: # more than 4 hours simulation
                              
                             logic_afternoon_def['runtime_ok'] = True
diff --git a/class4gl/simulations/batch_simulations.py b/class4gl/simulations/batch_simulations.py
index 0762cf8..fec9032 100644
--- a/class4gl/simulations/batch_simulations.py
+++ b/class4gl/simulations/batch_simulations.py
@@ -26,10 +26,12 @@
 parser.add_argument('--multi_processing_mode',default='pythonpool')
 parser.add_argument('--cpu_count',type=int,default=2)
 parser.add_argument('--subset_forcing',default='ini') 
-                                        # this tells which yaml subset
-                                        # to initialize with.
-                                        # Most common options are
-                                        # 'morning' and 'ini'.
+# parser.add_argument('--path_timeseries_forcing',default=False) 
+# parser.add_argument('--timeseries_forcing',default=False) 
+#                                         # this tells which yaml subset
+#                                         # to initialize with.
+#                                         # Most common options are
+#                                         # 'morning' and 'ini'.
 
 # Tuntime is usually specified from the afternoon profile. You can also just
 # specify the simulation length in seconds
@@ -56,7 +58,6 @@
                     help='output directory in which the experiments as subdirectories are stored')#,default='/user/data/gent/gvo000/gvo00090/D2D/data/C4GL/')
 
 
-
 #arguments only used for update_yaml.py
 #parser.add_argument('--path_dataset') 
 #parser.add_argument('--global_keys') 
diff --git a/class4gl/simulations/copy_update.py b/class4gl/simulations/copy_update.py
index 8dcea02..defa8c3 100644
--- a/class4gl/simulations/copy_update.py
+++ b/class4gl/simulations/copy_update.py
@@ -22,7 +22,7 @@
 arguments.append(dict(arg='--last_station_row',\
                     help='ending row number of stations table'))
 arguments.append(dict(arg='--global_vars',\
-                    help="global vars to update"))
+                    help="global vars to update ( ':'-seperated list) "))
 arguments.append(dict(arg='--station_id',\
                     help="process a specific station id"))
 arguments.append(dict(arg='--error_handling',\
@@ -268,7 +268,7 @@ def execute(**kwargs):
     
             
                     if args.global_vars is not None:
-                        c4gli_morning.get_global_input(globaldata,only_keys=args.global_vars.strip().split(','))
+                        c4gli_morning.get_global_input(globaldata,only_keys=args.global_vars.strip().split(':'))
     
                     onerun = True
     
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 9cefd61..93fbb20 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -359,14 +359,14 @@ def execute(**kwargs):
                         c4gl = class4gl(c4gli_morning)
     
                         if args.error_handling == 'dump_always':
-                            # try:
-                            print('checking data sources')
-                            if not c4gli_morning.check_source_globaldata():
-                                print('Warning: some input sources appear invalid')
-                            c4gl.run()
-                            print('run succesful')
-                            # except:
-                            #     print('run not succesful')
+                            try:
+                                print('checking data sources')
+                                if not c4gli_morning.check_source_globaldata():
+                                    print('Warning: some input sources appear invalid')
+                                c4gl.run()
+                                print('run succesful')
+                            except:
+                                print('run not succesful')
                             onerun = True
     
                             print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
diff --git a/class4gl/simulations/simulations_iter.py b/class4gl/simulations/simulations_iter.py
index 3ebe456..1270145 100644
--- a/class4gl/simulations/simulations_iter.py
+++ b/class4gl/simulations/simulations_iter.py
@@ -276,6 +276,15 @@ def execute(**kwargs):
     
         os.system('mkdir -p '+path_exp)
         records_morning_station = records_morning.query('STNID == '+str(current_station.name))
+        # records_afternoon_station = records_afternoon.query('STNID == '+str(current_station.name))
+        # for varkey in ['h','theta']:
+        #     records_morning_station['d'+varkey+'dt'] = \
+        #             (records_afternoon_station[sourcekey][varkey] - records_morning_station[sourcekey][varkey])/\
+        #             (records_afternoon_station[sourcekey].ldatetime - records_morning_station[sourcekey].ldatetime).dt.seconds*3600.
+        # select_loc = records_morning_station.query( '(dthetadt > 0) & (dhdt > 0.)').index
+        # records_morning_station = records_morning_station.loc[select_loc]
+        # records_afternoon_station = records_afternoon_station.loc[select_loc]
+
         for istation,current_station in run_stations.iterrows():
             if (int(args.split_by) * int(run_station_chunk)) >= (len(records_morning_station)):
                 print("warning: outside of profile number range for station "+\

From 43e374b76511eb522e81f100c77383c66f50a289 Mon Sep 17 00:00:00 2001
From: woutersh 
Date: Mon, 22 Feb 2021 14:20:28 +0100
Subject: [PATCH 127/129] features (manual input parameters) bugfixes in the
 era-based data generation allow user to specify logging level --debug_level

---
 class4gl/model.py                   | 12 +++++++++++-
 class4gl/setup/setup_era.py         | 14 ++++++++++----
 class4gl/setup/setup_igra.py        | 26 ++++++++++++++++++++++----
 class4gl/simulations/simulations.py | 19 ++++++++++++++-----
 4 files changed, 57 insertions(+), 14 deletions(-)

diff --git a/class4gl/model.py b/class4gl/model.py
index b2359d5..155b619 100644
--- a/class4gl/model.py
+++ b/class4gl/model.py
@@ -97,7 +97,7 @@ def __init__(self, model_input = None,debug_level=None):
 
         self.logger = logging.getLogger('model')
         if debug_level is not None:
-            self.logger.setLevel(debug_level)
+            self.logger.setLevel(logging._nameToLevel[debug_level])
 
         """ initialize the different components of the model """ 
 
@@ -204,6 +204,7 @@ def run(self):
         # time integrate model 
         #for self.t in range(self.tsteps):
         while self.t < self.tsteps:
+            print(self.t, '/', self.tsteps)
           
             # time integrate components
             self.timestep()
@@ -1139,6 +1140,15 @@ def integrate_mixed_layer(self):
         # if q_pre < 0:
         #     self.qtend = 
 
+        # calculate compensation to fix the free troposphere in case of subsidence 
+        if(self.sw_fixft):
+            w_th_ft  = self.gammatheta * self.ws
+            w_q_ft   = self.gammaq     * self.ws
+            w_CO2_ft = self.gammaCO2   * self.ws 
+        else:
+            w_th_ft  = 0.
+            w_q_ft   = 0.
+            w_CO2_ft = 0. 
         q_pre        = q0      + self.dtcur * self.qtend
         if q_pre < 0.001:
             self.qtend = (0.001-q0)/self.dtcur
diff --git a/class4gl/setup/setup_era.py b/class4gl/setup/setup_era.py
index 1674d80..6fa6b90 100644
--- a/class4gl/setup/setup_era.py
+++ b/class4gl/setup/setup_era.py
@@ -23,6 +23,8 @@
 
 import argparse
 
+# purpose: create CLASS4GL input data from era-interim
+# example: python $CLASS4GL/setup/setup_era.py --split_by 50 --path_forcing /data/gent/vo/000/gvo00090/D2D/data/SOUNDINGS/ERA_JOAO/ --path_experiments /user/data/gent/gvo000/gvo00090/$USER/data/MY_FIRST_CLASS4GL_EXPERIMENTS/ERA_TEST_JOAO/ --c4gl_path_lib $CLASS4GL  --station_chunk_number 0 --station_id 83779 --force_pars "b=7.12,CGsat=3.670e-06,p=6,a=0.135,C2ref=0.8,C1sat=0.213" --first_YYYYMMDD "20140101"
 #if __name__ == '__main__':
 parser = argparse.ArgumentParser()
 #parser.add_argument('--timestamp')
@@ -54,6 +56,7 @@
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
 parser.add_argument('--global_chunk_number') # this is the batch number according to split-by in case of considering all stations
 parser.add_argument('--station_chunk_number') # this is the batch number according to split-by in case of considering all stations
+parser.add_argument('--force_pars',default='') # run a specific station id
 args = parser.parse_args()
 
 sys.path.insert(0, args.c4gl_path_lib)
@@ -141,8 +144,8 @@
 print("station numbers included in the whole batch "+\
       "(all chunks):",list(all_stations_select.index))
 
-dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d",)
-dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d",)
+dtfirst = dt.datetime.strptime(args.first_YYYYMMDD,"%Y%m%d").astimezone(dt.timezone.utc)
+dtlast = dt.datetime.strptime(args.last_YYYYMMDD,"%Y%m%d").astimezone(dt.timezone.utc)
 # ===============================
 print("Creating daily timeseries from", dtfirst," to ", dtlast)
 # ===============================
@@ -165,14 +168,14 @@
         if args.split_by != -1:
             raise ValueError("Chunks are defined by --split-by, but I don't know which chunk to run. Please provide --global_chunk_number or --station_chunk_number, or leave out --split-by.")
         run_station_chunk = 0
-        print("stations that are processed.",list(run_stations.index))
+        #print("stations that are processed.",list(run_stations.index))
 
 DTS_chunk = DTS[(int(run_station_chunk)*int(args.split_by)):\
                  (int(run_station_chunk)+1)*int(args.split_by)]
 
 # for the current implementation we only consider one station. Let's upgrade it
 # later for more stations.
-run_station_chunk = int(args.global_chunk_number)
+#run_station_chunk = int(args.global_chunk_number)
 
 # ===============================
 print('start looping over chunk')
@@ -215,6 +218,7 @@
                            datetime_daylight = datetime, \
                            doy = datetime.timetuple().tm_yday,\
                            runtime = runtime,\
+                           tstart = ldatetime.hour + ldatetime.minute/60. + ldatetime.second/3600.,\
                           ))
 
     c4gli.get_global_input(globaldata)
@@ -250,6 +254,8 @@
                          source=air_ap_input_source,
                          mode=air_ap_mode)
 
+    for parkey, parvalue in [par.split('=') for par in args.force_pars.split(',')]:
+        c4gli.update(source='user_specified', pars={parkey: float(parvalue)})
     if not c4gli.check_source_globaldata():
         print('Warning: some input sources appear invalid')
 
diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index 9414220..d53934f 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -49,6 +49,8 @@
 parser.add_argument('--last_station_row')
 parser.add_argument('--c4gl_path_lib')#,default='/user/data/gent/gvo000/gvo00090/D2D/software/CLASS/class4gl/lib')
 parser.add_argument('--station_id') # run a specific station id
+parser.add_argument('--check_inputdata',default='True') # run a specific station id
+parser.add_argument('--force_pars',default='') # run a specific station id
 # parser.add_argument('--error_handling',default='dump_on_success')
 # parser.add_argument('--subset_output',default='morning') # this tells which yaml subset
 
@@ -61,6 +63,13 @@
 
 fn_stations = args.path_input+'/igra-stations.txt'
 
+def isfloat(value):
+  try:
+    float(value)
+    return True
+  except ValueError:
+    return False
+
 
 #calculate the root mean square error
 
@@ -162,6 +171,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
     #                   for EXP in experiments.keys()])
         
     dict_diag_station = {}
+    df_logic_morning = pd.DataFrame()
+    df_logic_afternoon = pd.DataFrame()
     with open(fnout,'w') as fileout, \
          open(fnout_afternoon,'w') as fileout_afternoon:
         wy_strm = wyoming(PATH=args.path_input, STNM=STN.name)
@@ -216,7 +227,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                           )
     
 
-            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.)
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 350.)
             
             print('logic:', logic)
             # the result
@@ -236,7 +247,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
             # If the morning is ok, then we try to find a decent afternoon
             # sounding
             logic_afternoon_def =dict()
-
+            df_logic_morning = df_logic_morning.append(logic,ignore_index=True)
             if morning_ok == 1.:
                 print('MORNING OK!')
                 # we get the current date
@@ -305,6 +316,9 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                             afternoon_first = False
 
                         print('logic_afternoon: ',logic_afternoon)
+                        df_logic_afternoon = df_logic_afternoon.append(logic_afternoon, ignore_index=True)
+                        if len(df_logic_afternoon)> 30:
+                            stop
                         print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
                         if afternoon_ok == 1.:
                             # # doesn't work :(
@@ -354,10 +368,14 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                                 logic_afternoon_def['global_parameters_ok'] = False
         
                                 c4gli.get_global_input(globaldata)
+                                #saopaulo:sandyclayloam -> --force_pars "LAI=3.5,b=7.12,CGsat=3.670,p=6,a=0.135,C2ref=0.8,C1sat=0.213"
+                                #curitiba:clay -> --force_pars "LAI=3.5,b=11.40,CGsat=3.6,p=12,a=0.083,C2ref=0.3,C1sat=0.342"
+                                for parkey,parvalue in [par.split('=') for par in args.force_pars.split(',')]:
+                                    c4gli.update(source='user_specified', pars={parkey:float(parvalue)})
                                 print('VERY CLOSE...')
-                                if c4gli.check_source_globaldata() and \
+                                if (args.check_inputdata == 'False') or (c4gli.check_source_globaldata()  and \
                                     (c4gli.check_source(source='wyoming',\
-                                                       check_only_sections='pars')):
+                                                       check_only_sections='pars'))):
 
                                     logic_afternoon_def['global_parameters_ok'] = True
                                     print('starting dumps')
diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 93fbb20..035f8e0 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -8,6 +8,7 @@
 import sys
 import pytz
 import math
+import logging
 
 
 arguments = []
@@ -27,7 +28,7 @@
                     default='dump_on_success',\
                     help="type of error handling: either\n - 'dump_on_success' (default)\n - 'dump_always'"))
 arguments.append(dict(arg='--diag_tropo',\
-                    default=['advt','advq','advu','advv'],\
+                    default='',#\'advt,advq,advu,advv',\
                     help="field to diagnose the mean in the troposphere (<= 3000m)"))
 arguments.append(dict(arg='--subset_forcing',
                     default='ini', 
@@ -48,7 +49,15 @@
 arguments.append(dict(arg='--global_chunk_number',help="this is the batch number of the expected series of experiments according to split_by"))
 arguments.append(dict(arg='--station_chunk_number',help="this is the batch number according to split_by in case of considering one station"))
 arguments.append(dict(arg='--experiments_names', help="Alternative output names that are given to the experiments. By default, these are the same as --experiments") )
-
+arguments.append(dict(arg='--debug_level', type=str,default=None,help="Debug level according to the standard python logging module. ") )
+# {'CRITICAL': 50,
+#   'FATAL': 50,
+#   'ERROR': 40,
+#   'WARN': 30,
+#   'WARNING': 30,
+#   'INFO': 20,
+#   'DEBUG': 10,
+#   'NOTSET': 0}
 
 
 if __name__ == '__main__':
@@ -326,11 +335,11 @@ def execute(**kwargs):
                                                         record_morning.index_start, 
                                                         record_morning.index_end,
                                                         mode='model_input')
-                        if args.diag_tropo is not None:
+                        if args.diag_tropo is not '':
                             print('add tropospheric parameters on advection and subsidence (for diagnosis)')
                             seltropo = (c4gli_morning.air_ac.p > c4gli_morning.air_ac.p.iloc[-1]+ 3000.*(- 1.2 * 9.81 ))
                             profile_tropo = c4gli_morning.air_ac[seltropo]
-                            for var in args.diag_tropo:#['t','q','u','v',]:
+                            for var in args.diag_tropo.split(','):#['t','q','u','v',]:
                                 if var[:3] == 'adv':
                                     mean_adv_tropo = np.mean(profile_tropo[var+'_x']+profile_tropo[var+'_y'] )
                                     c4gli_morning.update(source='era-interim',pars={var+'_tropo':mean_adv_tropo})
@@ -356,7 +365,7 @@ def execute(**kwargs):
                                             runtime})
                         c4gli_morning.update(source=expname, pars=exp)
     
-                        c4gl = class4gl(c4gli_morning)
+                        c4gl = class4gl(c4gli_morning,debug_level=args.debug_level)
     
                         if args.error_handling == 'dump_always':
                             try:

From c50c60a8ecd071944f3c0990ec45715c1632ca9b Mon Sep 17 00:00:00 2001
From: woutersh 
Date: Mon, 22 Feb 2021 14:42:49 +0100
Subject: [PATCH 128/129] features (manual input parameters) bugfixes in the
 era-based data generation allow user to specify logging level --debug_level
 revert test setting (h_e=150 instead of 350) to previous default for
 igra-sounding

---
 class4gl/setup/setup_igra.py | 6 +++---
 setup.py                     | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/class4gl/setup/setup_igra.py b/class4gl/setup/setup_igra.py
index d53934f..f8e72f4 100644
--- a/class4gl/setup/setup_igra.py
+++ b/class4gl/setup/setup_igra.py
@@ -227,7 +227,7 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
                           )
     
 
-            logic['mlherrlow'] = (c4gli.pars.h_e <= 350.)
+            logic['mlherrlow'] = (c4gli.pars.h_e <= 150.) ###350
             
             print('logic:', logic)
             # the result
@@ -317,8 +317,8 @@ def rmse(y_actual,y_predicted,z_actual = None, z_predicted = None,filternan_actu
 
                         print('logic_afternoon: ',logic_afternoon)
                         df_logic_afternoon = df_logic_afternoon.append(logic_afternoon, ignore_index=True)
-                        if len(df_logic_afternoon)> 30:
-                            stop
+                        # if len(df_logic_afternoon)> 30:
+                        #     stop
                         print(afternoon_ok,c4gli_afternoon.pars.ldatetime)
                         if afternoon_ok == 1.:
                             # # doesn't work :(
diff --git a/setup.py b/setup.py
index 1506d2d..8bfffd9 100644
--- a/setup.py
+++ b/setup.py
@@ -6,7 +6,7 @@
 # repository: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
 setup(
         name='class4gl',
-        version='1.0.1',
+        version='1.0.2',
         license='gpl-3.0',        # https://help.github.com/articles/licensing-a-repository
         description = 'a framework to investigate the dynamics of the atmospheric boundary layer weather balloons worldwide', # Give a short description
         author = 'Hendrik Wouters',                        # Type in your name

From 324f76718d868ee8b8c8b7b24bd6f70b605bcd21 Mon Sep 17 00:00:00 2001
From: hendrikwout 
Date: Sat, 26 Jun 2021 22:48:52 +0200
Subject: [PATCH 129/129] write out error messages on job failure in
 sumulations.py

---
 class4gl/simulations/simulations.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/class4gl/simulations/simulations.py b/class4gl/simulations/simulations.py
index 035f8e0..08ec611 100644
--- a/class4gl/simulations/simulations.py
+++ b/class4gl/simulations/simulations.py
@@ -374,8 +374,9 @@ def execute(**kwargs):
                                     print('Warning: some input sources appear invalid')
                                 c4gl.run()
                                 print('run succesful')
-                            except:
-                                print('run not succesful')
+                            except Exception as inst:
+                                print('run not succesful. Error message is:')
+                                print(inst.args) 
                             onerun = True
     
                             print("dumping to "+str(file_ini)+ ' ('+fn_ini+')') 
@@ -405,7 +406,9 @@ def execute(**kwargs):
                                           #timeseries_only=timeseries_only,\
                                          )
                                 onerun = True
-                           except:
+                           except Exception as inst:
+                               print('run not succesful. Error message is:')
+                               print(inst.args) 
                                print('run not succesful')
                         isim += 1
     

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